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Causal Representation Enhances Cross-Domain Named Entity Recognition in Large Language Models
《Computers, Materials & Continua》2025年第5期2809-2828,共20页Jiahao Wu Jinzhong Xu Xiaoming Liu Guan Yang Jie Liu 
supported by National Natural Science Foundation of China Joint Fund for Enterprise Innovation Development(U23B2029);National Natural Science Foundation of China(62076167,61772020);Key Scientific Research Project of Higher Education Institutions in Henan Province(24A520058,24A520060,23A520022);Postgraduate Education Reform and Quality Improvement Project of Henan Province(YJS2024AL053).
Large language models cross-domain named entity recognition task in the face of the scarcity of large language labeled data in a specific domain,due to the entity bias arising from the variation of entity information ...
关键词：Large language model entity bias causal graph structure 
Learning Hierarchical Adaptive Code Clouds for Neural 3D Shape Representation
《Machine Intelligence Research》2025年第2期304-323,共20页Yuanxun Lu Xinya Ji Hao Zhu Xun Cao 
supported by the National Natural Science Foundation of China(Nos.62001213 and 62025108).
Neural implicit representation(NIR)has attracted significant attention in 3D shape representation for its efficiency,generalizability,and flexibility compared with traditional explicit representations.Previous works u...
关键词：Representation learning shape analysis deep implicit function 3D reconstruction 3D modeling 
Segmentation of CAD models using hybrid representation
《虚拟现实与智能硬件(中英文)》2025年第2期188-202,共15页Claude UWIMANA Shengdi ZHOU Limei YANG Zhuqing LI Norbelt MUTAGISHA Edouard NIYONGABO Bin ZHOU 
Supported by the National Key Research and Development Program of China(2024YFB3311703);National Natural Science Foundation of China(61932003);Beijing Science and Technology Plan Project(Z221100006322003).
In this paper,we introduce an innovative method for computer-aided design(CAD)segmentation by concatenating meshes and CAD models.Many previous CAD segmentation methods have achieved impressive performance using singl...
关键词：B-RepNet hybrid segmentation CAD models classification MeshCNN MeshCAD-Net 
GTE:learning code AST representation efficiently and effectively
《Science China(Information Sciences)》2025年第3期389-390,共2页Yihao QIN Shangwen WANG Bo LIN Kang YANG Xiaoguang MAO 
With the development of deep learning in recent years,code representation learning techniques have become the foundation of many software engineering tasks such as program classification[1]and defect detection.Earlier...
关键词：SUCH LEARNING REPRESENTATION 
Efficient Parameterization for Knowledge Graph Embedding Using Hierarchical Attention Network
《Computers, Materials & Continua》2025年第3期4287-4300,共14页Zhen-Yu Chen Feng-Chi Liu Xin Wang Cheng-Hsiung Lee Ching-Sheng Lin 
supported by the National Science and Technology Council(NSTC),Taiwan,under Grants Numbers 112-2622-E-029-009 and 112-2221-E-029-019.
In the domain of knowledge graph embedding,conventional approaches typically transform entities and relations into continuous vector spaces.However,parameter efficiency becomes increasingly crucial when dealing with l...
关键词：Knowledge graph embedding parameter efficiency representation learning reserved entity and relation sets hierarchical attention network 
Multi-Scale Feature Fusion and Advanced Representation Learning for Multi Label Image Classification
《Computers, Materials & Continua》2025年第3期5285-5306,共22页Naikang Zhong Xiao Lin Wen Du Jin Shi 
supported by the National Natural Science Foundation of China(62302167,62477013);Natural Science Foundation of Shanghai(No.24ZR1456100);Science and Technology Commission of Shanghai Municipality(No.24DZ2305900);the Shanghai Municipal Special Fund for Promoting High-Quality Development of Industries(2211106).
Multi-label image classification is a challenging task due to the diverse sizes and complex backgrounds of objects in images.Obtaining class-specific precise representations at different scales is a key aspect of feat...
关键词：Image classification MULTI-LABEL multi scale attention mechanisms feature fusion 
Ontology Matching Method Based on Gated Graph Attention Model
《Computers, Materials & Continua》2025年第3期5307-5324,共18页Mei Chen Yunsheng Xu Nan Wu Ying Pan 
supported by the National Natural Science Foundation of China(grant numbers 62267005 and 42365008);the Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing.
With the development of the Semantic Web,the number of ontologies grows exponentially and the semantic relationships between ontologies become more and more complex,understanding the true semantics of specific terms o...
关键词：Ontology matching representation learning OWL2Vec*method graph attention model 
Hybrid mesh-neural representation for 3D transparent object reconstruction
《Computational Visual Media》2025年第1期123-140,共18页Jiamin Xu Zihan Zhu Hujun Bao Weiwei Xu 
supported by“Pioneer”and“Leading Goose”R&D Program of Zhejiang(No.2023C01181);supported by National Natural Science Foundation of China(No.62302134);Zhejiang Provincial Natural Science Foundation(No.LQ24F020031);supported by Information Technology Center and State Key Lab of CAD&CG,Zhejiang University.
In this study,we propose a novel method to reconstruct the 3D shapes of transparent objects using images captured by handheld cameras under natural lighting conditions.It combines the advantages of an explicit mesh an...
关键词：transparent object 3D reconstruction environment matting neural rendering 
Synergistic Multi-Drug Combination Prediction Based on Heterogeneous Network Representation Learning with Contrastive Learning
《Tsinghua Science and Technology》2025年第1期215-233,共19页Xin Xi Jinhui Yuan Shan Lu Jieyue He 
supported by National Key R&D Program of China(No.2019YFC1711000);Collaborative Innovation Center of Novel Software Technology and Industrialization.
The combination of multiple drugs is a significant therapeutic strategy that can enhance treatment effectiveness and reduce medication side effects.However,identifying effective synergistic drug combinations in a vast...
关键词：western medicine synergistic drug combination heterogeneous network contrastive learning 
FDCPNet:feature discrimination and context propagation network for 3D shape representation
《虚拟现实与智能硬件(中英文)》2025年第1期83-94,共12页Weimin SHI Yuan XIONG Qianwen WANG Han JIANG Zhong ZHOU 
Supported by the National Key R&D Program of China(2022YFC3803600).
Background Three-dimensional(3D)shape representation using mesh data is essential in various applications,such as virtual reality and simulation technologies.Current methods for extracting features from mesh edges or ...
关键词：3D shape representation Mesh model MeshNet Feature discrimination Context propagation 
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