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Element relational graph-augmented multi-granularity contextualized encoding for document-level event role filler extraction
《Frontiers of Computer Science》2025年第2期137-138,共2页Enchang ZHU Zhengtao YU Yuxin HUANG Shengxiang GAO Yantuan XIAN 
supported by the National Natural Science Foundation of China(Grant Nos.U21B2027,U23A20388,62266028);the Yunnan Provincial Major Science and Technology Special Plan Projects(202302AD080003,202202AD080003,202303AP140008);the Yunnan Fundamental Research Projects(202301AS070047);the Kunming University of Science and Technology’s”Double First-rate”Construction Joint Project(202201BE070001-021).
1 Introduction Document-level Role Filler Extraction aims to identify those spans of text that denote the role fillers for each event described in the document[1].Despite achieving certain accomplishments,existing met...
关键词：CONTEXTUAL SPITE semantic 
GraphFM:Graph Factorization Machines for Feature Interaction Modelling
《Machine Intelligence Research》2025年第2期239-253,共15页Shu Wu Zekun Li Yunyue Su Zeyu Cui Xiaoyu Zhang Liang Wang 
supported by the National Science Foundation of China(No.62141608).
Factorization machine(FM)is a prevalent approach to modelling pairwise(second-order)feature interactions when dealing with high-dimensional sparse data.However,on the one hand,FMs fail to capture higher-order feature ...
关键词：Feature interaction factorization machines graph neural network recommender system deep learning 
LEGF-DST:LLMs-Enhanced Graph-Fusion Dual-Stream Transformer for Fine-Grained Chinese Malicious SMS Detection
《Computers, Materials & Continua》2025年第2期1901-1924,共24页Xin Tong Jingya Wang Ying Yang Tian Peng Hanming Zhai Guangming Ling 
supported by the Fundamental Research Funds for the Central Universities(2024JKF13);the Beijing Municipal Education Commission General Program of Science and Technology(No.KM202414019003).
With the widespread use of SMS(Short Message Service),the proliferation of malicious SMS has emerged as a pressing societal issue.While deep learning-based text classifiers offer promise,they often exhibit suboptimal ...
关键词：Transformers malicious SMS multi-task learning large language models 
Counterfactual Learning on Graphs:A Survey
《Machine Intelligence Research》2025年第1期17-59,共43页Zhimeng Guo Zongyu Wu Teng Xiao Charu Aggarwal Hui Liu Suhang Wang 
supported by,or in part by the National Science Foundation(NSF),USA(No.IIS-1909702);Army Research Office(ARO),USA(No.W911NF-21-10198),and Cisco Faculty Research Award.
Graph-structured data are pervasive in the real-world such as social networks,molecular graphs and transaction networks.Graph neural networks(GNNs)have achieved great success in representation learning on graphs,facil...
关键词：Counterfactual learning graph-structured data graph neural networks FAIRNESS explainability 
PM_(2.5) probabilistic forecasting system based on graph generative network with graph U-nets architecture
《Journal of Central South University》2025年第1期304-318,共15页LI Yan-fei YANG Rui DUAN Zhu LIU Hui 
Project(2020YFC2008605)supported by the National Key Research and Development Project of China;Project(52072412)supported by the National Natural Science Foundation of China;Project(2021JJ30359)supported by the Natural Science Foundation of Hunan Province,China。
Urban air pollution has brought great troubles to physical and mental health,economic development,environmental protection,and other aspects.Predicting the changes and trends of air pollution can provide a scientific ...
关键词：PM_(2.5)interval forecasting graph generative network graph U-Nets sparse Bayesian regression kernel density estimation spatial-temporal characteristics 
自调节图卷积UNet的三维人体姿态估计方法
《北京航空航天大学学报》2025年第1期63-74,共12页马金林 崔琦磊 马自萍 武江涛 曹浩杰 
国家自然科学基金(62462001);宁夏自然科学基金(2024AAC03147,2023AAC03264);中央高校基本科研业务费专项资金(2023ZRLG02)。
基于图卷积网络的三维人体姿态估计方法无法提取关节点的多尺度特征和未充分利用相邻节点的拓扑关系问题,提出自调节图卷积UNet的三维人体姿态估计方法M-Joint-UNet。M-Joint-UNet方法由Joint-UNet、自调节图卷积和融合损失3部分组成:Jo...
关键词：三维人体姿态估计 图卷积 Graph-UNet 关节点池化 权重矩阵 
Harnessing multimodal large language models for traffic knowledge graph generation and decision-making
《Communications in Transportation Research》2024年第1期378-381,共4页Senyun Kuang Yang Liu Xin Wang Xinhua Wu Yintao Wei 
National Natural Science Foundation of China(Grant Nos.51761135124,11672148,52003142,and 51775293).
1.Introduction Autonomous driving advancements have increased the importance of understanding traffic scenes for intelligent transportation systems.Substantial progress has been made in traditional tasks such as road ...
关键词：MODAL TRAFFIC driving 
Graph-Induced by Modules via Tensor Product
《Applied Mathematics》2024年第12期840-847,共8页Mohammad Jarrar 
This paper investigates the connections between ring theory, module theory, and graph theory through the graph G(R)of a ring R. We establish that vertices of G(R)correspond to modules, with edges defined by the vanish...
关键词：Graph Theory Commutative Ring Tensor Product CONNECTED DIAMETER Semisimple Ring 
SGG-DGCN:Wind Turbine Anomaly Identification by Using Deep Graph Convolutional Networks with Similarity Graph Generation Strategy
《Journal of Dynamics, Monitoring and Diagnostics》2024年第4期258-267,共10页Xiaomin Wang Di Zhou Xiao Zhuang Jian Ge and Jiawei Xiang 
supported by National Natural Science Foundation of China(Nos.U52305124,U62201399);the Zhejiang Natural Science Foundation of China(Nos.LQ23E050002);the Basic Scientific Research Project of Wenzhou City(Nos.G2022008,G2023028);the General Scientific Research Project of Educational Department of Zhejiang Province(Nos.Y202249008,Y202249041);China Postdoctoral Science Foundation(Nos.2023M740988);Zhejiang Provincial Postdoctoral Science Foundation(Nos.ZJ2023122);the Master’s Innovation Foundation of Wenzhou University(Nos.3162024004106).
In order to minimize wind turbine failures,fault diagnosis of wind turbines is becoming increasinglyimportant,deep learning methods excel at multivariate monitoring and data modeling,but they are often limited toEucli...
关键词：anomaly identification deep graph convolutional networks similarity graph generation wind turbine 
Graph-geometric message passing via a graph convolution transformer for FKP regression
《Science China(Information Sciences)》2024年第12期172-186,共15页Huizhi ZHU Wenxia XU Jian HUANG Baocheng YU 
supported by National Natural Science Foundation Youth Fund of China (Grant No.61803286);Innovation Fund Project of Hubei Key Laboratory of Intelligent Robot (Grant No.HBIRL202210)。
In this paper, the forward kinematics problem(FKP) of the Gough-Stewart platform(GSP) with six degrees of freedom(6 DoFs) is estimated via deep learning. We propose a graph convolution transformer model by systematica...
关键词：deep learning graph-structured learning graph convolution transformer forward kinematics problem Gough-Stewart platform 
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