The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to u...The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.展开更多
为解决篇章级多事件抽取中事件及论元角色间全局语义关联缺失、文档信息利用不足的问题,提出了基于论元关联和图神经网络的篇章级多事件抽取(document-level multi-event extraction based on argument correlation and graph neural ne...为解决篇章级多事件抽取中事件及论元角色间全局语义关联缺失、文档信息利用不足的问题,提出了基于论元关联和图神经网络的篇章级多事件抽取(document-level multi-event extraction based on argument correlation and graph neural network,DEEACG)模型。首先,使用基于变换器的双向编码器表示(bidirectional encoder representations from Transformers,BERT)模块获取实体,并引入实体共事件性预测任务,增强实体间的语义关联。接着,引入可学习的事件代理节点,构建包含实体、上下文和代理节点的异构图,通过特征线性调制图神经网络(graph neural network with feature-wise linear modulation,GNN-FiLM)与多头自注意力机制,实现多事件间的全局交互与语义融合。然后,通过多层感知机进行事件类型检测。最后,构建双投影空间建模论元关联,采用Bron-Kerbosch算法提取图中极大团作为候选论元组合,并结合多头注意力实现论元角色分类。结果表明,DEEACG模型在中文金融公告(Chinese financial announcements,ChFinAnn)数据集的多事件抽取任务中性能明显提升,与关系增强文档级事件抽取(relation-enabled document-level event extraction,ReDEE)模型相比,F1均值提升了2.1个百分点。该研究证实DEEACG模型能有效捕捉多事件间语义关联,适用于篇章级多事件抽取任务。展开更多
The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural netwo...The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural network,which can be applied only to regular grids,is adopted to capture the spatial dependence for flow prediction.However,the obtained regions are always irregular considering the road network and administrative boundaries;thus,dividing the city into grids is inaccurate for prediction.In this paper,we propose a new model based on multi-graph convolutional network and gated recurrent unit(MGCN-GRU)to predict traffic flows for irregular regions.Specifically,we first construct heterogeneous inter-region graphs for a city to reflect the rela-tionships among regions.In each graph,nodes represent the irregular regions and edges represent the relationship types between regions.Then,we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes.The GRU is further used to capture the temporal dependence and to predict future traffic flows.Experimental results based on three real-world large-scale datasets(public bicycle system dataset,taxi dataset,and dockless bike-sharing dataset)show that our MGCN-GRU model outperforms a variety of existing methods.展开更多
现有基于预训练语言模型(PLM)的作文自动评分(AES)方法偏向于直接使用从PLM提取的全局语义特征表示作文的质量,却忽略了作文质量与更细粒度特征关联关系的问题。聚焦于中文AES研究,从多种文本角度分析和评估作文质量,提出利用图神经网络...现有基于预训练语言模型(PLM)的作文自动评分(AES)方法偏向于直接使用从PLM提取的全局语义特征表示作文的质量,却忽略了作文质量与更细粒度特征关联关系的问题。聚焦于中文AES研究,从多种文本角度分析和评估作文质量,提出利用图神经网络(GNN)对作文的多尺度特征进行联合学习的中文AES方法。首先,利用GNN分别获取作文在句子级别和段落级别的篇章特征;然后,将这些篇章特征与作文的全局语义特征进行联合特征学习,实现对作文更精准的评分;最后,构建一个中文AES数据集,为中文AES研究提供数据基础。在所构建的数据集上的实验结果表明,所提方法在6个作文主题上的平均二次加权Kappa(QWK)系数相较于R2-BERT(Bidirectional Encoder Representations from Transformers model with Regression and Ranking)提升了1.1个百分点,验证了在AES任务中进行多尺度特征联合学习的有效性。同时,消融实验结果进一步表明了不同尺度的作文特征对评分效果的贡献。为了证明小模型在特定任务场景下的优越性,与当前流行的通用大语言模型GPT-3.5-turbo和DeepSeek-V3进行了对比。结果表明,使用所提方法的BERT(Bidirectional Encoder Representations from Transformers)模型在6个作文主题上的平均QWK比GPT-3.5-turbo和DeepSeek-V3分别高出了65.8和45.3个百分点,验证了大语言模型(LLMs)在面向领域的篇章级作文评分任务中,因缺乏大规模有监督微调数据而表现不佳的观点。展开更多
文摘The ability to accurately predict urban traffic flows is crucial for optimising city operations.Consequently,various methods for forecasting urban traffic have been developed,focusing on analysing historical data to understand complex mobility patterns.Deep learning techniques,such as graph neural networks(GNNs),are popular for their ability to capture spatio-temporal dependencies.However,these models often become overly complex due to the large number of hyper-parameters involved.In this study,we introduce Dynamic Multi-Graph Spatial-Temporal Graph Neural Ordinary Differential Equation Networks(DMST-GNODE),a framework based on ordinary differential equations(ODEs)that autonomously discovers effective spatial-temporal graph neural network(STGNN)architectures for traffic prediction tasks.The comparative analysis of DMST-GNODE and baseline models indicates that DMST-GNODE model demonstrates superior performance across multiple datasets,consistently achieving the lowest Root Mean Square Error(RMSE)and Mean Absolute Error(MAE)values,alongside the highest accuracy.On the BKK(Bangkok)dataset,it outperformed other models with an RMSE of 3.3165 and an accuracy of 0.9367 for a 20-min interval,maintaining this trend across 40 and 60 min.Similarly,on the PeMS08 dataset,DMST-GNODE achieved the best performance with an RMSE of 19.4863 and an accuracy of 0.9377 at 20 min,demonstrating its effectiveness over longer periods.The Los_Loop dataset results further emphasise this model’s advantage,with an RMSE of 3.3422 and an accuracy of 0.7643 at 20 min,consistently maintaining superiority across all time intervals.These numerical highlights indicate that DMST-GNODE not only outperforms baseline models but also achieves higher accuracy and lower errors across different time intervals and datasets.
文摘为解决篇章级多事件抽取中事件及论元角色间全局语义关联缺失、文档信息利用不足的问题,提出了基于论元关联和图神经网络的篇章级多事件抽取(document-level multi-event extraction based on argument correlation and graph neural network,DEEACG)模型。首先,使用基于变换器的双向编码器表示(bidirectional encoder representations from Transformers,BERT)模块获取实体,并引入实体共事件性预测任务,增强实体间的语义关联。接着,引入可学习的事件代理节点,构建包含实体、上下文和代理节点的异构图,通过特征线性调制图神经网络(graph neural network with feature-wise linear modulation,GNN-FiLM)与多头自注意力机制,实现多事件间的全局交互与语义融合。然后,通过多层感知机进行事件类型检测。最后,构建双投影空间建模论元关联,采用Bron-Kerbosch算法提取图中极大团作为候选论元组合,并结合多头注意力实现论元角色分类。结果表明,DEEACG模型在中文金融公告(Chinese financial announcements,ChFinAnn)数据集的多事件抽取任务中性能明显提升,与关系增强文档级事件抽取(relation-enabled document-level event extraction,ReDEE)模型相比,F1均值提升了2.1个百分点。该研究证实DEEACG模型能有效捕捉多事件间语义关联,适用于篇章级多事件抽取任务。
基金the National Natural Science Foundation of China(No.61903109)the Zhejiang Provincial Natural Science Foundation of China(No.LY19F030021)。
文摘The prediction of regional traffic flows is important for traffic control and management in an intelligent traffic system.With the help of deep neural networks,the convolutional neural network or residual neural network,which can be applied only to regular grids,is adopted to capture the spatial dependence for flow prediction.However,the obtained regions are always irregular considering the road network and administrative boundaries;thus,dividing the city into grids is inaccurate for prediction.In this paper,we propose a new model based on multi-graph convolutional network and gated recurrent unit(MGCN-GRU)to predict traffic flows for irregular regions.Specifically,we first construct heterogeneous inter-region graphs for a city to reflect the rela-tionships among regions.In each graph,nodes represent the irregular regions and edges represent the relationship types between regions.Then,we propose a multi-graph convolutional network to fuse different inter-region graphs and additional attributes.The GRU is further used to capture the temporal dependence and to predict future traffic flows.Experimental results based on three real-world large-scale datasets(public bicycle system dataset,taxi dataset,and dockless bike-sharing dataset)show that our MGCN-GRU model outperforms a variety of existing methods.
文摘现有基于预训练语言模型(PLM)的作文自动评分(AES)方法偏向于直接使用从PLM提取的全局语义特征表示作文的质量,却忽略了作文质量与更细粒度特征关联关系的问题。聚焦于中文AES研究,从多种文本角度分析和评估作文质量,提出利用图神经网络(GNN)对作文的多尺度特征进行联合学习的中文AES方法。首先,利用GNN分别获取作文在句子级别和段落级别的篇章特征;然后,将这些篇章特征与作文的全局语义特征进行联合特征学习,实现对作文更精准的评分;最后,构建一个中文AES数据集,为中文AES研究提供数据基础。在所构建的数据集上的实验结果表明,所提方法在6个作文主题上的平均二次加权Kappa(QWK)系数相较于R2-BERT(Bidirectional Encoder Representations from Transformers model with Regression and Ranking)提升了1.1个百分点,验证了在AES任务中进行多尺度特征联合学习的有效性。同时,消融实验结果进一步表明了不同尺度的作文特征对评分效果的贡献。为了证明小模型在特定任务场景下的优越性,与当前流行的通用大语言模型GPT-3.5-turbo和DeepSeek-V3进行了对比。结果表明,使用所提方法的BERT(Bidirectional Encoder Representations from Transformers)模型在6个作文主题上的平均QWK比GPT-3.5-turbo和DeepSeek-V3分别高出了65.8和45.3个百分点,验证了大语言模型(LLMs)在面向领域的篇章级作文评分任务中,因缺乏大规模有监督微调数据而表现不佳的观点。
