Developing a comprehensive understanding of inter-city interactions is crucial for regional planning.We therefore examined spatiotemporal patterns of population migration across the Qinghai-Tibet Plateau(QTP)using mig...Developing a comprehensive understanding of inter-city interactions is crucial for regional planning.We therefore examined spatiotemporal patterns of population migration across the Qinghai-Tibet Plateau(QTP)using migration big data from Tencent for the period between 2015 and 2019.We initially used decomposition and breakpoint detection methods to examine time-series migration data and to identify the two seasons with the strongest and weakest population migration levels,between June 18th and August 18th and between October 8th and February 15th,respectively.Population migration within the former period was 2.03 times that seen in the latter.We then used a variety of network analysis methods to examine population flow directions as well as the importance of each individual city in migration.The two capital cities on the QTP,Lhasa and Xining,form centers for population migration and are also transfer hubs through which migrants from other cities off the plateau enter and leave this region.Data show that these two cities contribute more than 35%of total population migration.The majority of migrants tend to move within the province,particularly during the weakest migration season.We also utilized interactive relationship force and radiation models to examine the interaction strength and the radiating energy of each individual city.Results show that Lhasa and Xining exhibit the strongest interactions with other cities and have the largest radiating energies.Indeed,the radiating energy of the QTP cities correlates with their gross domestic product(GDP)(Pearson correlation coefficient:0.754 in the weakest migration season,WMS versus 0.737 in the strongest migration season,SMS),while changes in radiating energy correlate with the tourism-related revenue(Pearson correlation coefficient:0.685).These outcomes suggest that level of economic development and level of tourism are the two most important factors driving the QTP population migration.The results of this analysis provide critical clarification guidance regarding huge QTP development differences.展开更多
In the field of deep learning,current human action recognition algorithms often treat temporal information,spatial information,and background information equally,which leads to limited recognition accuracy.To address ...In the field of deep learning,current human action recognition algorithms often treat temporal information,spatial information,and background information equally,which leads to limited recognition accuracy.To address this issue,this pap er proposes a human action recognition algorithm based on spatiotemporal information interaction.First,a dual-pathway network is proposed to learn spatial and temporal information at different refresh rates.The network includes a sparse pathway operatin g at a low frame rate to capture spatial semantic information,and a parallel dense pathway operating at a high frame rate to capture temporal motion information.Second,to extract more discriminative features from videos,a cross-dual attention interaction model is introduced to focus on key regions of video segments and explicitly exchange spatiotemporal information between the two pathways.Experimental results show that the proposed algorithm achieves recognition accuracies of 97.6%on the UCF101 datase t and 78.4%on the HMDB51 dataset,outperforming the novel SlowFast algorithm by 1.8%and 1.4%,respectively.Combined with a nighttime image enhancement algorithm based on MDIFE-Net curve estimation,the method achieved an accuracy of 83.2%on the ARID nighttime data set-an improvement of 22.9%over the performance before image enhancement.This demonstrates the method’s strong potential for real-world nighttime action recognition applications.展开更多
基金National Natural Science Foundation of China(41590845)Strategic Priority Research Program of the Chinese Academy of Sciences(XDA19040501)+2 种基金Strategic Priority Research Program of the Chinese Academy of Sciences(XDA20040401)National Key Research and Development Program of China(2017YFB0503605)National Key Research and Development Program of China(2017YFC1503003)。
文摘Developing a comprehensive understanding of inter-city interactions is crucial for regional planning.We therefore examined spatiotemporal patterns of population migration across the Qinghai-Tibet Plateau(QTP)using migration big data from Tencent for the period between 2015 and 2019.We initially used decomposition and breakpoint detection methods to examine time-series migration data and to identify the two seasons with the strongest and weakest population migration levels,between June 18th and August 18th and between October 8th and February 15th,respectively.Population migration within the former period was 2.03 times that seen in the latter.We then used a variety of network analysis methods to examine population flow directions as well as the importance of each individual city in migration.The two capital cities on the QTP,Lhasa and Xining,form centers for population migration and are also transfer hubs through which migrants from other cities off the plateau enter and leave this region.Data show that these two cities contribute more than 35%of total population migration.The majority of migrants tend to move within the province,particularly during the weakest migration season.We also utilized interactive relationship force and radiation models to examine the interaction strength and the radiating energy of each individual city.Results show that Lhasa and Xining exhibit the strongest interactions with other cities and have the largest radiating energies.Indeed,the radiating energy of the QTP cities correlates with their gross domestic product(GDP)(Pearson correlation coefficient:0.754 in the weakest migration season,WMS versus 0.737 in the strongest migration season,SMS),while changes in radiating energy correlate with the tourism-related revenue(Pearson correlation coefficient:0.685).These outcomes suggest that level of economic development and level of tourism are the two most important factors driving the QTP population migration.The results of this analysis provide critical clarification guidance regarding huge QTP development differences.
文摘In the field of deep learning,current human action recognition algorithms often treat temporal information,spatial information,and background information equally,which leads to limited recognition accuracy.To address this issue,this pap er proposes a human action recognition algorithm based on spatiotemporal information interaction.First,a dual-pathway network is proposed to learn spatial and temporal information at different refresh rates.The network includes a sparse pathway operatin g at a low frame rate to capture spatial semantic information,and a parallel dense pathway operating at a high frame rate to capture temporal motion information.Second,to extract more discriminative features from videos,a cross-dual attention interaction model is introduced to focus on key regions of video segments and explicitly exchange spatiotemporal information between the two pathways.Experimental results show that the proposed algorithm achieves recognition accuracies of 97.6%on the UCF101 datase t and 78.4%on the HMDB51 dataset,outperforming the novel SlowFast algorithm by 1.8%and 1.4%,respectively.Combined with a nighttime image enhancement algorithm based on MDIFE-Net curve estimation,the method achieved an accuracy of 83.2%on the ARID nighttime data set-an improvement of 22.9%over the performance before image enhancement.This demonstrates the method’s strong potential for real-world nighttime action recognition applications.
