Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surround...Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.展开更多
Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address ...Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.展开更多
Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of t...Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of the catalyst. Although several effective models have been proposed in previous research to address anomaly detection in chemical processes, most fail to adequately capture the spatial-temporal dependencies of multi-source, mixed-frequency information. In this study, an innovative multi-source mixed-frequency information fusion framework based on a spatial-temporal graph attention network (MIF-STGAT) is proposed to investigate the causes of FCC regenerator catalyst loss anomalies for guide onsite operational management, enhancing the long-term stability of FCC unit operations. First, a reconstruction-based dual-encoder-decoder framework is developed to facilitate the acquisition of mixed-frequency features and information fusion during the FCC regenerator catalyst loss process. Subsequently, a graph attention network and a multilayer long short-term memory network with a differential structure are integrated into the reconstruction-based dual-encoder-shared-decoder framework to capture the dynamic fluctuations and critical features associated with anomalies. Experimental results from the Chinese FCC industrial process demonstrate that MIF-STGAT achieves excellent accuracy and interpretability for anomaly detection.展开更多
Attentional orienting and response inhibition have largely been studied separately. Each has yielded important findings, but controversy remains concerning whether they share any neurocognitive processes. These confli...Attentional orienting and response inhibition have largely been studied separately. Each has yielded important findings, but controversy remains concerning whether they share any neurocognitive processes. These conflicting findings may originate from two issues： （1） at the cognitive level, attentional orienting and response inhibition are typically studied in isolation; and （2） at the technological level, a single neuroimaging method is typically used to study these processes. This article reviews recent achievements in both spatial and temporal neuroimaging, emphasizing the relationship between attentional orienting and response inhibition. We suggest that coordinated engagement, both top-down and bottom-up, serves as a common neural mechanism underlying these two cognitive processes. In addition, the right ventrolateral prefrontal cortex may play a major role in their harmonious operation.展开更多
Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic...Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.展开更多
Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtempora...Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset.展开更多
The temporal evolution and spatial distribution characteristics of network attention of Tengwang Pavilion, a 5 A tourist attraction in Nanchang City from 2011 to 2019 were analyzed by using Baidu index search platform...The temporal evolution and spatial distribution characteristics of network attention of Tengwang Pavilion, a 5 A tourist attraction in Nanchang City from 2011 to 2019 were analyzed by using Baidu index search platform. The results showed that network attention of Tengwang Pavilion in China was increasing year by year, but the annual growth rate was different. There were two peak periods of network attention in a year. They were in April and October respectively. From a weekly point of view, the network attention of Tengwang Pavilion was the lowest on Friday, the highest on Saturdays, and higher on Saturdays and Sundays than on weekdays. From the point of view of geographical distribution, the province that paid the most attention to Tengwang Pavilion on network was Jiangxi Province and the largest city was Nanchang. Tengwang Pavilion scenic spot should pay more attention to the network attention and distribution characteristics of tourists, grasp the potential needs to better guide the development and marketing of tourism products, ensure the safety of tourist attractions, and promote the sustainable development of scenic spots.展开更多
Objective video quality assessment plays a very important role in multimedia signal processing. Several extensions of the structural similarity （SSIM） index could not predict the quality of the video sequence effect...Objective video quality assessment plays a very important role in multimedia signal processing. Several extensions of the structural similarity （SSIM） index could not predict the quality of the video sequence effectively. In this paper we propose a structural similarity quality metric for videos based on a spatial-temporal visual attention model. This model acquires the motion attended region and the distortion attended region by computing the motion features and the distortion contrast. It mimics the visual attention shifting between the two attended regions and takes the burst of error into account by introducing the non-linear weighting fimctions to give a much higher weighting factor to the extremely damaged frames. The proposed metric based on the model renders the final object quality rating of the whole video sequence and is validated using the 50 Hz video sequences of Video Quality Experts Group Phase I test database.展开更多
Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction acc...Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.展开更多
篇章要素识别(discourse element identification)的主要任务是识别篇章要素单元并进行分类.针对篇章要素识别对上下文依赖性理解不足的问题,提出一种基于BiLSTM-Attention的识别篇章要素模型,提高议论文篇章要素识别的准确率.该模型利...篇章要素识别(discourse element identification)的主要任务是识别篇章要素单元并进行分类.针对篇章要素识别对上下文依赖性理解不足的问题,提出一种基于BiLSTM-Attention的识别篇章要素模型,提高议论文篇章要素识别的准确率.该模型利用句子结构和位置编码来识别句子的成分关系,通过双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)进一步获得深层次上下文相关联的信息;引入注意力机制(attention mechanism)优化模型特征向量,提高文本分类的准确度;最终用句间多头自注意力(multi-head self-attention)获取句子在内容和结构上的关系,弥补距离较远的句子依赖问题.相比于HBiLSTM、BERT等基线模型,在相同参数、相同实验条件下,中文数据集和英文数据集上准确率分别提升1.3%、3.6%,验证了该模型在篇章要素识别任务中的有效性.展开更多
基金supported by the National Key Research and Development Program of China(2018AAA0101005,2018AAA0102404)the Program of the Huawei Technologies Co.Ltd.(FA2018111061SOW12)+1 种基金the National Natural Science Foundation of China(61773054)the Youth Research Fund of the State Key Laboratory of Complex Systems Management and Control(20190213)。
文摘Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.
基金supported by the National Natural Science Foundation of China(Grant Nos.62472149,62376089,62202147)Hubei Provincial Science and Technology Plan Project(2023BCB04100).
文摘Accurate traffic flow prediction has a profound impact on modern traffic management. Traffic flow has complex spatial-temporal correlations and periodicity, which poses difficulties for precise prediction. To address this problem, a Multi-head Self-attention and Spatial-Temporal Graph Convolutional Network (MSSTGCN) for multiscale traffic flow prediction is proposed. Firstly, to capture the hidden traffic periodicity of traffic flow, traffic flow is divided into three kinds of periods, including hourly, daily, and weekly data. Secondly, a graph attention residual layer is constructed to learn the global spatial features across regions. Local spatial-temporal dependence is captured by using a T-GCN module. Thirdly, a transformer layer is introduced to learn the long-term dependence in time. A position embedding mechanism is introduced to label position information for all traffic sequences. Thus, this multi-head self-attention mechanism can recognize the sequence order and allocate weights for different time nodes. Experimental results on four real-world datasets show that the MSSTGCN performs better than the baseline methods and can be successfully adapted to traffic prediction tasks.
基金supported by the Innovative Research Group Project of the National Natural Science Foundation of China(22021004)Sinopec Major Science and Technology Projects(321123-1).
文摘Anomaly fluctuations in operating conditions, catalyst wear, crushing, and the deterioration of feedstock properties in fluid catalytic cracking (FCC) units can disrupt the normal circulating fluidization process of the catalyst. Although several effective models have been proposed in previous research to address anomaly detection in chemical processes, most fail to adequately capture the spatial-temporal dependencies of multi-source, mixed-frequency information. In this study, an innovative multi-source mixed-frequency information fusion framework based on a spatial-temporal graph attention network (MIF-STGAT) is proposed to investigate the causes of FCC regenerator catalyst loss anomalies for guide onsite operational management, enhancing the long-term stability of FCC unit operations. First, a reconstruction-based dual-encoder-decoder framework is developed to facilitate the acquisition of mixed-frequency features and information fusion during the FCC regenerator catalyst loss process. Subsequently, a graph attention network and a multilayer long short-term memory network with a differential structure are integrated into the reconstruction-based dual-encoder-shared-decoder framework to capture the dynamic fluctuations and critical features associated with anomalies. Experimental results from the Chinese FCC industrial process demonstrate that MIF-STGAT achieves excellent accuracy and interpretability for anomaly detection.
基金supported by the National Natural Science Foundation of China (31100745 91232725 and 61175117)+5 种基金the National Basic Research Development Program (973 Program) of China (2011CB707803)the ‘111’ Project of China the Doctoral Training Fund of China (2010018511016)the Scientific Project of Bureau of Education Chongqing Municipality China (KJ110502)
文摘Attentional orienting and response inhibition have largely been studied separately. Each has yielded important findings, but controversy remains concerning whether they share any neurocognitive processes. These conflicting findings may originate from two issues： （1） at the cognitive level, attentional orienting and response inhibition are typically studied in isolation; and （2） at the technological level, a single neuroimaging method is typically used to study these processes. This article reviews recent achievements in both spatial and temporal neuroimaging, emphasizing the relationship between attentional orienting and response inhibition. We suggest that coordinated engagement, both top-down and bottom-up, serves as a common neural mechanism underlying these two cognitive processes. In addition, the right ventrolateral prefrontal cortex may play a major role in their harmonious operation.
基金the National Natural Science Foundation of China(No.61461027,61762059)the Provincial Science and Technology Program supported the Key Project of Natural Science Foundation of Gansu Province(No.22JR5RA226)。
文摘Considering the nonlinear structure and spatial-temporal correlation of traffic network,and the influence of potential correlation between nodes of traffic network on the spatial features,this paper proposes a traffic speed prediction model based on the combination of graph attention network with self-adaptive adjacency matrix(SAdpGAT)and bidirectional gated recurrent unit(BiGRU).First-ly,the model introduces graph attention network(GAT)to extract the spatial features of real road network and potential road network respectively in spatial dimension.Secondly,the spatial features are input into BiGRU to extract the time series features.Finally,the prediction results of the real road network and the potential road network are connected to generate the final prediction results of the model.The experimental results show that the prediction accuracy of the proposed model is im-proved obviously on METR-LA and PEMS-BAY datasets,which proves the advantages of the pro-posed spatial-temporal model in traffic speed prediction.
基金supported by the Key Research&Development Plan Project of Shandong Province,China(No.2017GGX10127).
文摘Continuous sign language recognition(CSLR)is challenging due to the complexity of video background,hand gesture variability,and temporal modeling difficulties.This work proposes a CSLR method based on a spatialtemporal graph attention network to focus on essential features of video series.The method considers local details of sign language movements by taking the information on joints and bones as inputs and constructing a spatialtemporal graph to reflect inter-frame relevance and physical connections between nodes.The graph-based multihead attention mechanism is utilized with adjacent matrix calculation for better local-feature exploration,and short-term motion correlation modeling is completed via a temporal convolutional network.We adopted BLSTM to learn the long-termdependence and connectionist temporal classification to align the word-level sequences.The proposed method achieves competitive results regarding word error rates(1.59%)on the Chinese Sign Language dataset and the mean Jaccard Index(65.78%)on the ChaLearn LAP Continuous Gesture Dataset.
基金Sponsored by Humanities and Social Sciences Research Projects in Colleges and Universities in Jiangxi Province(JC1542)
文摘The temporal evolution and spatial distribution characteristics of network attention of Tengwang Pavilion, a 5 A tourist attraction in Nanchang City from 2011 to 2019 were analyzed by using Baidu index search platform. The results showed that network attention of Tengwang Pavilion in China was increasing year by year, but the annual growth rate was different. There were two peak periods of network attention in a year. They were in April and October respectively. From a weekly point of view, the network attention of Tengwang Pavilion was the lowest on Friday, the highest on Saturdays, and higher on Saturdays and Sundays than on weekdays. From the point of view of geographical distribution, the province that paid the most attention to Tengwang Pavilion on network was Jiangxi Province and the largest city was Nanchang. Tengwang Pavilion scenic spot should pay more attention to the network attention and distribution characteristics of tourists, grasp the potential needs to better guide the development and marketing of tourism products, ensure the safety of tourist attractions, and promote the sustainable development of scenic spots.
文摘Objective video quality assessment plays a very important role in multimedia signal processing. Several extensions of the structural similarity （SSIM） index could not predict the quality of the video sequence effectively. In this paper we propose a structural similarity quality metric for videos based on a spatial-temporal visual attention model. This model acquires the motion attended region and the distortion attended region by computing the motion features and the distortion contrast. It mimics the visual attention shifting between the two attended regions and takes the burst of error into account by introducing the non-linear weighting fimctions to give a much higher weighting factor to the extremely damaged frames. The proposed metric based on the model renders the final object quality rating of the whole video sequence and is validated using the 50 Hz video sequences of Video Quality Experts Group Phase I test database.
文摘Accurate traffic prediction is crucial for an intelligent traffic system (ITS). However, the excessive non-linearity and complexity of the spatial-temporal correlation in traffic flow severely limit the prediction accuracy of most existing models, which simply stack temporal and spatial modules and fail to capture spatial-temporal features effectively. To improve the prediction accuracy, a multi-head attention spatial-temporal graph neural network (MSTNet) is proposed in this paper. First, the traffic data is decomposed into unique time spans that conform to positive rules, and valuable traffic node attributes are mined through an adaptive graph structure. Second, time and spatial features are captured using a multi-head attention spatial-temporal module. Finally, a multi-step prediction module is used to achieve future traffic condition prediction. Numerical experiments were conducted on an open-source dataset, and the results demonstrate that MSTNet performs well in spatial-temporal feature extraction and achieves more positive forecasting results than the baseline methods.
文摘篇章要素识别(discourse element identification)的主要任务是识别篇章要素单元并进行分类.针对篇章要素识别对上下文依赖性理解不足的问题,提出一种基于BiLSTM-Attention的识别篇章要素模型,提高议论文篇章要素识别的准确率.该模型利用句子结构和位置编码来识别句子的成分关系,通过双向长短期记忆网络(bidirectional long short-term memory,BiLSTM)进一步获得深层次上下文相关联的信息;引入注意力机制(attention mechanism)优化模型特征向量,提高文本分类的准确度;最终用句间多头自注意力(multi-head self-attention)获取句子在内容和结构上的关系,弥补距离较远的句子依赖问题.相比于HBiLSTM、BERT等基线模型,在相同参数、相同实验条件下,中文数据集和英文数据集上准确率分别提升1.3%、3.6%,验证了该模型在篇章要素识别任务中的有效性.
