Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on ...Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on different areas,and the rainfall varies with seasons,the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation.This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model(ST-QPE),which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations.We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory.Experimental results are verified and analyzed by using statistical and meteorological methods,and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment,which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently.展开更多
The video-oriented facial expression recognition has always been an important issue in emotion perception.At present,the key challenge in most existing methods is how to effectively extract robust features to characte...The video-oriented facial expression recognition has always been an important issue in emotion perception.At present,the key challenge in most existing methods is how to effectively extract robust features to characterize facial appearance and geometry changes caused by facial motions.On this basis,the video in this paper is divided into multiple segments,each of which is simultaneously described by optical flow and facial landmark trajectory.To deeply delve the emotional information of these two representations,we propose a Deep Spatiotemporal Network with Dual-flow Fusion(defined as DSN-DF),which highlights the region and strength of expressions by spatiotemporal appearance features and the speed of change by spatiotemporal geometry features.Finally,experiments are implemented on CKþand MMI datasets to demonstrate the superiority of the proposed method.展开更多
As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk dete...As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore,more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method,this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks,first,the video stream is framed adaptively according to the pixel changes in the video stream.Then,the optimized MobileNet is used to extract the feature map of the video stream,which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally,training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study,and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation,and has good real-time performance.The average accuracy of the algorithm can reach 87.8%,and the frame rate is 61 frames/s,which is of great significance for improving the reliability and accuracy of security control methods.展开更多
Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-iti...Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.展开更多
This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,i...This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.展开更多
Accurate prediction of solar photovoltaic power is crucial for renewable integration.However,existing methods struggles to simultaneously capture its complex spatial correlation and nonlinear temporal dependencies.Her...Accurate prediction of solar photovoltaic power is crucial for renewable integration.However,existing methods struggles to simultaneously capture its complex spatial correlation and nonlinear temporal dependencies.Here,we propose a novel spatiotemporal relationship fusion network(STRFN)for short-term prediction of photovoltaic power generation.STRFN uses convolutional neural networks to extract spatial features,long short-term memory networks to capture time dependence,and an attention mechanism to enhance its expressiveness.Additionally,the optimal network hyperparameters of STRFN are identified through Bayesian optimization.Moreover,it employs advanced data preprocessing techniques to improve input data quality.These techniques include feature recognition,principal component analysis,location coding,and sliding window segmentation.Our STRFN is applied to two typical PV systems for demonstration and compared with traditional deep learning models.The results show that our model’s accuracy and stability significantly outperform traditional deep learning models,with RMSE of 2.46 and 0.036,and MAPE of 1.51%and 1.94%.Furthermore,in predictions for typical days across four seasons,our STRFN still maintained consistent superior performance-evidenced by its normalized RMSE(NRMSE)of 0.90%and 0.61%for the two PV systems.Finally,we integrate data processing,model training,and results visualization together into a one-stop platform and make it user-friendly and easily improved for solar power prediction.Our proposed method along with its forecasting platform can offer valuable insights and guidelines for researchers and PV operators.展开更多
The rapid growth and increasing complexity of Internet of Things(IoT)devices have made network intrusion detection a critical challenge,especially in edge computing environments where data privacy is a primary concern...The rapid growth and increasing complexity of Internet of Things(IoT)devices have made network intrusion detection a critical challenge,especially in edge computing environments where data privacy is a primary concern.Machine learning-based intrusion detection techniques enhance IoT network security but often require centralized network data,posing significant risks to data privacy and security.Although federated learning(FL)-based network intrusion detection methods have emerged in recent years to address privacy concerns,they have not fully leveraged the advantages of graph neural networks(GNNs)for intrusion detection.To address this issue,we propose a federated spatiotemporal graph convolutional network(FedSTGCN)model,which integrates the capabilities of spatiotemporal GNNs(STGNNs)and federated learning.This framework enables collaborative model training across distributed IoT devices without requiring the sharing of raw data,thereby improving network intrusion detection accuracy while preserving data privacy.Extensive experiments are conducted on two widely used IoT intrusion detection datasets to evaluate the effectiveness of the proposed approach.The results demonstrate that FedSTGCN outperforms other methods in both binary and multiclass classification tasks,achieving over 97%accuracy in binary classification tasks and over 92%weighted F1-score in multiclass classification tasks.展开更多
Polymers often exhibit multi-state conformational transitions with multiple pathways as temperature varies.However,characterizing the inherent features of these pathways is hindered by the lack of physical characteriz...Polymers often exhibit multi-state conformational transitions with multiple pathways as temperature varies.However,characterizing the inherent features of these pathways is hindered by the lack of physical characterizations that can distinguish various transition pathways between complex and disordered states.In this work,we introduced a machine-learning framework based on spatiotemporal point-cloud neural networks to identify and analyze conformational transition pathways in polymer chains.As a case study,we applied this framework to the temperature-induced unfolding of a single semi-flexible polymer chain,simulated via coarse-grained molecular dynamics.We first combined spatiotemporal point cloud neural networks and contrastive learning to extract features of conformational evolution,and then we employed unsupervised learning methods to cluster distinct transition pathways and unfolding trajectories.Our results reveal that,with increasing temperature,semi-flexible polymer chains exhibit five distinct unfolding pathways:rigid rod→random coil;small toroid→large toroid→hairpin→random coil;rod bundle→hairpin→random coil;hairpin→random coil;and tailed structure→random coil.We further calculated the structural order parameters of those typical conformations with distinct transition pathways,we distincted five transition mechanisms,including the straightening of rigid rods,tightening of small rings,expansion of hairpin ends,symmetrization of rod bundles,and retraction of tailed structures.These findings demonstrate that our framework presents a promising data-driven approach for analyzing complex conformational transitions in disordered polymers,which might be potentially extendable to other heterogeneous systems like intrinsically disordered proteins.展开更多
Automatically detecting learners’engagement levels helps to develop more effective online teaching and assessment programs,allowing teachers to provide timely feedback and make personalized adjustments based on stude...Automatically detecting learners’engagement levels helps to develop more effective online teaching and assessment programs,allowing teachers to provide timely feedback and make personalized adjustments based on students’needs to enhance teaching effectiveness.Traditional approaches mainly rely on single-frame multimodal facial spatial information,neglecting temporal emotional and behavioural features,with accuracy affected by significant pose variations.Additionally,convolutional padding can erode feature maps,affecting feature extraction’s representational capacity.To address these issues,we propose a hybrid neural network architecture,the redistributing facial features and temporal convolutional network(RefEIP).This network consists of three key components:first,utilizing the spatial attention mechanism large kernel attention(LKA)to automatically capture local patches and mitigate the effects of pose variations;second,employing the feature organization and weight distribution(FOWD)module to redistribute feature weights and eliminate the impact of white features and enhancing representation in facial feature maps.Finally,we analyse the temporal changes in video frames through the modern temporal convolutional network(ModernTCN)module to detect engagement levels.We constructed a near-infrared engagement video dataset(NEVD)to better validate the efficiency of the RefEIP network.Through extensive experiments and in-depth studies,we evaluated these methods on the NEVD and the Database for Affect in Situations of Elicitation(DAiSEE),achieving an accuracy of 90.8%on NEVD and 61.2%on DAiSEE in the fourclass classification task,indicating significant advantages in addressing engagement video analysis problems.展开更多
Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal...Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.展开更多
Spiral waves have been observed in the biological experiments on rat cortex perfused with drugs which can block inhibitory synapse and switch neuron excitability from type II to type I. To simulate the spiral waves ob...Spiral waves have been observed in the biological experiments on rat cortex perfused with drugs which can block inhibitory synapse and switch neuron excitability from type II to type I. To simulate the spiral waves observed in the experiment, the spatiotemporal patterns are investigated in a network composed of neurons with type I and II excitabilities and excitatory coupling. Spiral waves emerge when the percentage(p) of neurons with type I excitability in the network is at middle levels, which is dependent on the coupling strength. Compared with other spatial patterns which appear at different p values, spiral waves exhibit optimal spatial correlation at a certain spatial frequency, implying the occurrence of spatial coherence resonance-like phenomenon. Some dynamical characteristics of the network such as mean firing frequency and synchronous degree can be well interpreted with distinct properties between type I excitability and type II excitability. The results not only identify dynamics of spiral waves in neuronal networks composed of neurons with different excitabilities, but also are helpful to understanding the emergence of spiral waves observed in the biological experiment.展开更多
Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk factors.In this study,we conduc...Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk factors.In this study,we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective,where nodes capture the local transmission intensities resulting from dominant vector species,the population density,and land cover,and edges describe the cross-region human mobility patterns.The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations.Our study focuses on malaria-severe districts in Cambodia.The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics:the risks increase in the rainy season and decrease in the dry season;remote and sparsely populated areas generally show higher transmission intensities than other areas.Our findings suggest that:the human mobility(e.g.,in planting/harvest seasons),environment(e.g.,temperature),and contact risk(coexistences of human and vector occurrence)contribute to malaria transmission in spatiotemporally varying degrees;quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.展开更多
基金This work is supported by the Key Research and Development Program of Hunan Province(No.2019SK2161)the Key Research and Development Program of Hunan Province(No.2016SK2017).
文摘Radar quantitative precipitation estimation(QPE)is a key and challenging task for many designs and applications with meteorological purposes.Since the Z-R relation between radar and rain has a number of parameters on different areas,and the rainfall varies with seasons,the traditional methods are incapable of achieving high spatial and temporal resolution and thus difficult to obtain a refined rainfall estimation.This paper proposes a radar quantitative precipitation estimation algorithm based on the spatiotemporal network model(ST-QPE),which designs a convolutional time-series network QPE-Net8 and a multi-scale feature fusion time-series network QPE-Net22 to address these limitations.We report on our investigation into contrast reversal experiments with radar echo and rainfall data collected by the Hunan Meteorological Observatory.Experimental results are verified and analyzed by using statistical and meteorological methods,and show that the ST-QPE model can inverse the rainfall information corresponding to the radar echo at a given moment,which provides practical guidance for accurate short-range precipitation nowcasting to prevent and mitigate disasters efficiently.
基金This work is supported by Natural Science Foundation of China(Grant No.61903056)Major Project of Science and Technology Research Program of Chongqing Education Commission of China(Grant No.KJZDM201900601)+3 种基金Chongqing Research Program of Basic Research and Frontier Technology(Grant Nos.cstc2019jcyj-msxmX0681,cstc2021jcyj-msxmX0530,and cstc2021jcyjmsxmX0761)Project Supported by Chongqing Municipal Key Laboratory of Institutions of Higher Education(Grant No.cqupt-mct-201901)Project Supported by Chongqing Key Laboratory of Mobile Communications Technology(Grant No.cqupt-mct-202002)Project Supported by Engineering Research Center of Mobile Communications,Ministry of Education(Grant No.cqupt-mct202006)。
文摘The video-oriented facial expression recognition has always been an important issue in emotion perception.At present,the key challenge in most existing methods is how to effectively extract robust features to characterize facial appearance and geometry changes caused by facial motions.On this basis,the video in this paper is divided into multiple segments,each of which is simultaneously described by optical flow and facial landmark trajectory.To deeply delve the emotional information of these two representations,we propose a Deep Spatiotemporal Network with Dual-flow Fusion(defined as DSN-DF),which highlights the region and strength of expressions by spatiotemporal appearance features and the speed of change by spatiotemporal geometry features.Finally,experiments are implemented on CKþand MMI datasets to demonstrate the superiority of the proposed method.
基金This paper is supported by the Science and technology projects of Yunnan Province(Grant No.202202AD080004).
文摘As the scale of the power system continues to expand,the environment for power operations becomes more and more complex.Existing risk management and control methods for power operations can only set the same risk detection standard and conduct the risk detection for any scenario indiscriminately.Therefore,more reliable and accurate security control methods are urgently needed.In order to improve the accuracy and reliability of the operation risk management and control method,this paper proposes a method for identifying the key links in the whole process of electric power operation based on the spatiotemporal hybrid convolutional neural network.To provide early warning and control of targeted risks,first,the video stream is framed adaptively according to the pixel changes in the video stream.Then,the optimized MobileNet is used to extract the feature map of the video stream,which contains both time-series and static spatial scene information.The feature maps are combined and non-linearly mapped to realize the identification of dynamic operating scenes.Finally,training samples and test samples are produced by using the whole process image of a power company in Xinjiang as a case study,and the proposed algorithm is compared with the unimproved MobileNet.The experimental results demonstrated that the method proposed in this paper can accurately identify the type and start and end time of each operation link in the whole process of electric power operation,and has good real-time performance.The average accuracy of the algorithm can reach 87.8%,and the frame rate is 61 frames/s,which is of great significance for improving the reliability and accuracy of security control methods.
文摘Predicting the behavior of renewable energy systems requires models capable of generating accurate forecasts from limited historical data,a challenge that becomes especially pronounced when commissioning new facil-ities where operational records are scarce.This review aims to synthesize recent progress in data-efficient deep learning approaches for addressing such“cold-start”forecasting problems.It primarily covers three interrelated domains—solar photovoltaic(PV),wind power,and electrical load forecasting—where data scarcity and operational variability are most critical,while also including representative studies on hydropower and carbon emission prediction to provide a broader systems perspective.To this end,we examined trends from over 150 predominantly peer-reviewed studies published between 2019 and mid-2025,highlighting advances in zero-shot and few-shot meta-learning frameworks that enable rapid model adaptation with minimal labeled data.Moreover,transfer learning approaches combined with spatiotemporal graph neural networks have been employed to transfer knowledge from existing energy assets to new,data-sparse environments,effectively capturing hidden dependencies among geographic features,meteorological dynamics,and grid structures.Synthetic data generation has further proven valuable for expanding training samples and mitigating overfitting in cold-start scenarios.In addition,large language models and explainable artificial intelligence(XAI)—notably conversational XAI systems—have been used to interpret and communicate complex model behaviors in accessible terms,fostering operator trust from the earliest deployment stages.By consolidating methodological advances,unresolved challenges,and open-source resources,this review provides a coherent overview of deep learning strategies that can shorten the data-sparse ramp-up period of new energy infrastructures and accelerate the transition toward resilient,low-carbon electricity grids.
基金supported by the National Natural Science Foundation of China(62373317)the Tianshan Talent Training Program(2022TSYCCX0013)+3 种基金the Key Project of Natural Science Foundation of Xinjiang(2021D01D10)the Basic Research Foundation for Universities of Xinjiang(XJEDU2023P023)the Xinjiang Key Laboratory of Applied Mathematics(XJDX1401)the Intelligent Control and Optimization Research Platform in Xinjiang University.
文摘This paper is dedicated to fixed-time passivity and synchronization for multi-weighted spatiotemporal directed networks.First,to achieve fixed-time passivity,a type of decentralized power-law controller is developed,in which only one parameter needs to be adjusted in the power-law terms;this greatly decreases the inconvenience of parameter adjustment.Second,several fixed-time passivity criteria with LMI forms are derived by using a Gauss divergence theorem to deal with the spatial diffusion of nodes and by applying the Hölder’s inequality to dispose rigorously the power-law term greater than one in the designed control scheme;this improves the previous theoretical analysis.Additionally,the fixed-time synchronization of spatiotemporal directed networks with multi-weights is addressed as a direct result of fixed-time strict passivity.Finally,a numerical example is presented in order to show the validity of the theoretical analysis.
基金National Key Research and Development Program of China(2024YFB4006400).
文摘Accurate prediction of solar photovoltaic power is crucial for renewable integration.However,existing methods struggles to simultaneously capture its complex spatial correlation and nonlinear temporal dependencies.Here,we propose a novel spatiotemporal relationship fusion network(STRFN)for short-term prediction of photovoltaic power generation.STRFN uses convolutional neural networks to extract spatial features,long short-term memory networks to capture time dependence,and an attention mechanism to enhance its expressiveness.Additionally,the optimal network hyperparameters of STRFN are identified through Bayesian optimization.Moreover,it employs advanced data preprocessing techniques to improve input data quality.These techniques include feature recognition,principal component analysis,location coding,and sliding window segmentation.Our STRFN is applied to two typical PV systems for demonstration and compared with traditional deep learning models.The results show that our model’s accuracy and stability significantly outperform traditional deep learning models,with RMSE of 2.46 and 0.036,and MAPE of 1.51%and 1.94%.Furthermore,in predictions for typical days across four seasons,our STRFN still maintained consistent superior performance-evidenced by its normalized RMSE(NRMSE)of 0.90%and 0.61%for the two PV systems.Finally,we integrate data processing,model training,and results visualization together into a one-stop platform and make it user-friendly and easily improved for solar power prediction.Our proposed method along with its forecasting platform can offer valuable insights and guidelines for researchers and PV operators.
文摘The rapid growth and increasing complexity of Internet of Things(IoT)devices have made network intrusion detection a critical challenge,especially in edge computing environments where data privacy is a primary concern.Machine learning-based intrusion detection techniques enhance IoT network security but often require centralized network data,posing significant risks to data privacy and security.Although federated learning(FL)-based network intrusion detection methods have emerged in recent years to address privacy concerns,they have not fully leveraged the advantages of graph neural networks(GNNs)for intrusion detection.To address this issue,we propose a federated spatiotemporal graph convolutional network(FedSTGCN)model,which integrates the capabilities of spatiotemporal GNNs(STGNNs)and federated learning.This framework enables collaborative model training across distributed IoT devices without requiring the sharing of raw data,thereby improving network intrusion detection accuracy while preserving data privacy.Extensive experiments are conducted on two widely used IoT intrusion detection datasets to evaluate the effectiveness of the proposed approach.The results demonstrate that FedSTGCN outperforms other methods in both binary and multiclass classification tasks,achieving over 97%accuracy in binary classification tasks and over 92%weighted F1-score in multiclass classification tasks.
基金financially supported by the National Key R&D Program of China(No.2022YFB3707303)the National Natural Science Foundation of China(No.52293471)。
文摘Polymers often exhibit multi-state conformational transitions with multiple pathways as temperature varies.However,characterizing the inherent features of these pathways is hindered by the lack of physical characterizations that can distinguish various transition pathways between complex and disordered states.In this work,we introduced a machine-learning framework based on spatiotemporal point-cloud neural networks to identify and analyze conformational transition pathways in polymer chains.As a case study,we applied this framework to the temperature-induced unfolding of a single semi-flexible polymer chain,simulated via coarse-grained molecular dynamics.We first combined spatiotemporal point cloud neural networks and contrastive learning to extract features of conformational evolution,and then we employed unsupervised learning methods to cluster distinct transition pathways and unfolding trajectories.Our results reveal that,with increasing temperature,semi-flexible polymer chains exhibit five distinct unfolding pathways:rigid rod→random coil;small toroid→large toroid→hairpin→random coil;rod bundle→hairpin→random coil;hairpin→random coil;and tailed structure→random coil.We further calculated the structural order parameters of those typical conformations with distinct transition pathways,we distincted five transition mechanisms,including the straightening of rigid rods,tightening of small rings,expansion of hairpin ends,symmetrization of rod bundles,and retraction of tailed structures.These findings demonstrate that our framework presents a promising data-driven approach for analyzing complex conformational transitions in disordered polymers,which might be potentially extendable to other heterogeneous systems like intrinsically disordered proteins.
基金supported by the National Natural Science Foundation of China(No.62367006)the Graduate Innovative Fund of Wuhan Institute of Technology(Grant No.CX2023551).
文摘Automatically detecting learners’engagement levels helps to develop more effective online teaching and assessment programs,allowing teachers to provide timely feedback and make personalized adjustments based on students’needs to enhance teaching effectiveness.Traditional approaches mainly rely on single-frame multimodal facial spatial information,neglecting temporal emotional and behavioural features,with accuracy affected by significant pose variations.Additionally,convolutional padding can erode feature maps,affecting feature extraction’s representational capacity.To address these issues,we propose a hybrid neural network architecture,the redistributing facial features and temporal convolutional network(RefEIP).This network consists of three key components:first,utilizing the spatial attention mechanism large kernel attention(LKA)to automatically capture local patches and mitigate the effects of pose variations;second,employing the feature organization and weight distribution(FOWD)module to redistribute feature weights and eliminate the impact of white features and enhancing representation in facial feature maps.Finally,we analyse the temporal changes in video frames through the modern temporal convolutional network(ModernTCN)module to detect engagement levels.We constructed a near-infrared engagement video dataset(NEVD)to better validate the efficiency of the RefEIP network.Through extensive experiments and in-depth studies,we evaluated these methods on the NEVD and the Database for Affect in Situations of Elicitation(DAiSEE),achieving an accuracy of 90.8%on NEVD and 61.2%on DAiSEE in the fourclass classification task,indicating significant advantages in addressing engagement video analysis problems.
基金supported by National Social Science Fund of China[grant number 21JCA004]Soft Science Research Project of Ministry of Housing and Urban-Rural Development of China[grant number R20200287]Open Research Fund of Key Laboratory of Digital Cartography and Land Information Application,Ministry of Natural Resources[grant number ZRZYBWD202102].
文摘Being a kind of non-Euclidean data,spatiotemporal graph data exists everywhere from trafficflow,air quality index to crime case,etc.Unlike the raster data,the irregular and disordered characteristics of spatiotemporal graph data have attracted the research interest of scholars,with the prediction of spatiotemporal graph data being one of the research hot spots.The emergence of spatiotemporal graph neural networks(ST-GNNs)provides a new insight for solving the problem of obtaining spatial correlation for spatiotemporal graph data prediction while achieving state-of-the-art performance.In this paper,comprehensive survey of research on ST-GNNs prediction domain isa presented,where the background of ST-GNNs is introduced before the computational paradigm of ST-GNN is thoroughly reviewed.From the perspective of model construction,59 well-known models in recent years are classified and discussed.Some of these models are further analyzed in terms of performance and efficiency.Subsequently,the categories and applicationfields of spatiotemporal graph data are summarized,providing a clear idea of technology selection for different applications.Finally,the evolution history and future direction of ST-GNNs are also summarized,to facilitate future researchers to timely understand the current state of prediction research by ST-GNNs.
基金supported by the National Natural Science Foundation of China(Grant Nos.11372224&11572225)
文摘Spiral waves have been observed in the biological experiments on rat cortex perfused with drugs which can block inhibitory synapse and switch neuron excitability from type II to type I. To simulate the spiral waves observed in the experiment, the spatiotemporal patterns are investigated in a network composed of neurons with type I and II excitabilities and excitatory coupling. Spiral waves emerge when the percentage(p) of neurons with type I excitability in the network is at middle levels, which is dependent on the coupling strength. Compared with other spatial patterns which appear at different p values, spiral waves exhibit optimal spatial correlation at a certain spatial frequency, implying the occurrence of spatial coherence resonance-like phenomenon. Some dynamical characteristics of the network such as mean firing frequency and synchronous degree can be well interpreted with distinct properties between type I excitability and type II excitability. The results not only identify dynamics of spiral waves in neuronal networks composed of neurons with different excitabilities, but also are helpful to understanding the emergence of spiral waves observed in the biological experiment.
基金funded by the Ministry of Science and Technology of China(2021ZD0112501/2021ZD0112502)the HKSAR Research Grants Council(12201318/12201619/12202220)the HKBU/CSD Departmental Start-up Fund for New Assistant Professors.
文摘Malaria control can significantly benefit from a holistic and precise way of quantitatively measuring the transmission intensity,which needs to incorporate spatiotemporally varying risk factors.In this study,we conduct a systematic investigation to characterize malaria transmission intensity by taking a spatiotemporal network perspective,where nodes capture the local transmission intensities resulting from dominant vector species,the population density,and land cover,and edges describe the cross-region human mobility patterns.The inferred network enables us to accurately assess the transmission intensity over time and space from available empirical observations.Our study focuses on malaria-severe districts in Cambodia.The malaria transmission intensities determined using our transmission network reveal both qualitatively and quantitatively their seasonal and geographical characteristics:the risks increase in the rainy season and decrease in the dry season;remote and sparsely populated areas generally show higher transmission intensities than other areas.Our findings suggest that:the human mobility(e.g.,in planting/harvest seasons),environment(e.g.,temperature),and contact risk(coexistences of human and vector occurrence)contribute to malaria transmission in spatiotemporally varying degrees;quantitative relationships between these influential factors and the resulting malaria transmission risk can inform evidence-based tailor-made responses at the right locations and times.
