Recently,the importance of data analysis has increased significantly due to the rapid data increase.In particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS...Recently,the importance of data analysis has increased significantly due to the rapid data increase.In particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing values.High missing values in data lead to the decreased predictive performance of models.Existing missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image data.Additionally,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication data.This study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these issues.The proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial convolution.Experimental results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive accuracy.Through this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.展开更多
Recent advancements in spatial transcriptomics(ST)technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues.Despite these capabilities of the ...Recent advancements in spatial transcriptomics(ST)technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues.Despite these capabilities of the ST data,accurately dissecting spatiotemporal structures(e.g.,spatial domains,temporal trajectories,and functional interactions)remains challenging.Here,we introduce a computational framework,PearlST(partial differential equation[PDE]-enhanced adversarial graph autoencoder of ST),for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder.PearlST employs contrastive learning to extract histological image features,integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries,and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders.Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering,trajectory inference,and pseudotime analysis.Furthermore,PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings,as illustrated in a human breast cancer dataset.Overall,PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.展开更多
Prevalent use of motion capture(MoCap)produces large volumes of data and MoCap data retrieval becomes crucial for efficient data reuse.MoCap clips may not be neatly segmented and labeled,increasing the difficulty of r...Prevalent use of motion capture(MoCap)produces large volumes of data and MoCap data retrieval becomes crucial for efficient data reuse.MoCap clips may not be neatly segmented and labeled,increasing the difficulty of retrieval.In order to effectively retrieve such data,we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment(PESTA)in this work.It retrieves similarities at the sub-sequence level,achieves robustness against singular frames and enables control of tradeoff between precision and efficiency.It firstly learns a dictionary of encoded postures utilizing unsupervised adversarial autoencoder techniques and,based on which,compactly symbolizes any MoCap sequence.Secondly,it conducts strided temporal alignment to align a query sequence to repository sequences to retrieve the best-matching sub-sequences from the repository.Further,it extends to find matches for multiple sub-queries in a long query at sharply promoted efficiency and minutely sacrificed precision.Outstanding performance of the proposed scheme is well demonstrated by experiments on two public MoCap datasets and one MoCap dataset captured by ourselves.展开更多
基金supported by the MSIT (Ministry of Science and ICT),Korea,under the ITRC (Information Technology Research Center)support program (IITP-2018-0-01405)supervised by the IITP (Institute for Information&Communications Technology Planning&Evaluation).
文摘Recently,the importance of data analysis has increased significantly due to the rapid data increase.In particular,vehicle communication data,considered a significant challenge in Intelligent Transportation Systems(ITS),has spatiotemporal characteristics and many missing values.High missing values in data lead to the decreased predictive performance of models.Existing missing value imputation models ignore the topology of transportation net-works due to the structural connection of road networks,although physical distances are close in spatiotemporal image data.Additionally,the learning process of missing value imputation models requires complete data,but there are limitations in securing complete vehicle communication data.This study proposes a missing value imputation model based on adversarial autoencoder using spatiotemporal feature extraction to address these issues.The proposed method replaces missing values by reflecting spatiotemporal characteristics of transportation data using temporal convolution and spatial convolution.Experimental results show that the proposed model has the lowest error rate of 5.92%,demonstrating excellent predictive accuracy.Through this,it is possible to solve the data sparsity problem and improve traffic safety by showing superior predictive performance.
基金supported by grants from the National Key R&D Program of China(2021YFF1200903)the National Natural Science Foundation of China(62273364,11931019,11871070,and 62362062)+2 种基金the Guangdong Basic and Applied Basic Research Foundation(2020B1515020047)Fundamental Research Funds for the Central Universities,Sun Yat-sen University(231lgbj025)the open fund of Information Materials and Intelligent Sensing Laboratory of Anhui Province(grant no.IMIS202105).
文摘Recent advancements in spatial transcriptomics(ST)technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues.Despite these capabilities of the ST data,accurately dissecting spatiotemporal structures(e.g.,spatial domains,temporal trajectories,and functional interactions)remains challenging.Here,we introduce a computational framework,PearlST(partial differential equation[PDE]-enhanced adversarial graph autoencoder of ST),for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder.PearlST employs contrastive learning to extract histological image features,integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries,and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders.Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering,trajectory inference,and pseudotime analysis.Furthermore,PearlST elucidates functional regulations of the latent features by linking intercellular ligand-receptor interactions to most contributing genes of the low-dimensional embeddings,as illustrated in a human breast cancer dataset.Overall,PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.
基金supported by Shandong Provincial Natural Science Foundation of China under Grant No.ZR2022MF294.
文摘Prevalent use of motion capture(MoCap)produces large volumes of data and MoCap data retrieval becomes crucial for efficient data reuse.MoCap clips may not be neatly segmented and labeled,increasing the difficulty of retrieval.In order to effectively retrieve such data,we propose an elastic content-based retrieval scheme via unsupervised posture encoding and strided temporal alignment(PESTA)in this work.It retrieves similarities at the sub-sequence level,achieves robustness against singular frames and enables control of tradeoff between precision and efficiency.It firstly learns a dictionary of encoded postures utilizing unsupervised adversarial autoencoder techniques and,based on which,compactly symbolizes any MoCap sequence.Secondly,it conducts strided temporal alignment to align a query sequence to repository sequences to retrieve the best-matching sub-sequences from the repository.Further,it extends to find matches for multiple sub-queries in a long query at sharply promoted efficiency and minutely sacrificed precision.Outstanding performance of the proposed scheme is well demonstrated by experiments on two public MoCap datasets and one MoCap dataset captured by ourselves.
