Traffic flow prediction is a crucial element of intelligent transportation systems.However,accu-rate traffic flow prediction is quite challenging because of its highly nonlinear,complex,and dynam-ic characteristics.To...Traffic flow prediction is a crucial element of intelligent transportation systems.However,accu-rate traffic flow prediction is quite challenging because of its highly nonlinear,complex,and dynam-ic characteristics.To address the difficulties in simultaneously capturing local and global dynamic spatiotemporal correlations in traffic flow,as well as the high time complexity of existing models,a multi-head flow attention-based local-global dynamic hypergraph convolution(MFA-LGDHC)pre-diction model is proposed.which consists of multi-head flow attention(MHFA)mechanism,graph convolution network(GCN),and local-global dynamic hypergraph convolution(LGHC).MHFA is utilized to extract the time dependency of traffic flow and reduce the time complexity of the model.GCN is employed to catch the spatial dependency of traffic flow.LGHC utilizes down-sampling con-volution and isometric convolution to capture the local and global spatial dependencies of traffic flow.And dynamic hypergraph convolution is used to model the dynamic higher-order relationships of the traffic road network.Experimental results indicate that the MFA-LGDHC model outperforms current popular baseline models and exhibits good prediction performance.展开更多
View-based 3-D object retrieval has become an emerging topic in recent years,especially with the fast development of visual content acquisition devices,such as mobile phones with cameras.Extensive research efforts hav...View-based 3-D object retrieval has become an emerging topic in recent years,especially with the fast development of visual content acquisition devices,such as mobile phones with cameras.Extensive research efforts have been dedicated to this task,while it is still difficult to measure the relevance between two objects with multiple views.In recent years,learning-based methods have been investigated in view-based 3-D object retrieval,such as graph-based learning.It is noted that the graph-based methods suffer from the high computational cost from the graph construction and the corresponding learning process.In this paper,we introduce a general framework to accelerate the learning-based view-based 3-D object matching in large scale data.Given a query object Q and one object O from a 3-D dataset D,the first step is to extract a small set of candidate relevant 3-D objects for object O.Then multiple hypergraphs can be constructed based on this small set of 3-D objects and the learning on the fused hypergraph is conducted to generate the relevance between Q and O,which can be further used in the retrieval procedure.Experiments demonstrate the effectiveness of the proposed framework.展开更多
Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(H...Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data reconstruction.However,most existing AE-based unmixing algorithms usually ignore the spatial information of HSIs.To solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for unmixing.Firstly,the traditional AE architecture is specifically improved as an unsupervised unmixing framework.Secondly,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their abundances.Moreover,L_(1/2)norm is further used to enhance abundances sparsity.Finally,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.展开更多
In the field of cancer treatment,drug combination therapy appears to be a promising treatment strategy compared to monotherapy.Recently,plenty of computational models are gradually applied to prioritize synergistic dr...In the field of cancer treatment,drug combination therapy appears to be a promising treatment strategy compared to monotherapy.Recently,plenty of computational models are gradually applied to prioritize synergistic drug combinations.However,the existing prediction models have not fully exploited the multi-way relations between drug combinations and cell lines.Besides,the number of identified drug-drug-cell line triplets is insufficient owning to the high cost of in vitro screening,which affects the ability of models to capture and utilize multi-way relations.To address this challenge,we design the multi-view hypergraph contrastive learning model,termed MHCLSyn,for synergistic drug combination prediction.First,the synergistic drug-drug-cell line triplets are formulated as a drug synergy hypergraph,and three task-specific hypergraphs are designed based on the drug synergy hypergraph.Then,we design a multi-view hypergraph contrastive learning with enhancement schemes,which allows for more expressive and discriminative node representation learning on drug synergy hypergraph.After that,the representations of nodes indicating drug-drug-cell line triplets are inputted to fully connected network for making predictions.Extensive experiments show MHCLSyn achieves better performance than state-of-the-art prediction models on benchmark datasets and is applicable to unseen drug combinations or cell lines.Case study indicates that MHCLSyn is capable of detecting potential synergistic drug combinations.展开更多
Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a nov...Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a novel hypergraph-based framework for moving object classification.Specifically,we capture moving objects with an event camera,to perceive and collect asynchronous event streams in a high temporal resolution.Unlike stacked event frames,we encode asynchronous event data into a hypergraph,fully mining the high-order correlation of event data,and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition.The experimental results show that our method has a good performance in moving object classification(e.g.,gait identification).展开更多
This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph...This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph neural networks to model the intricate spatio-temporal correlations among traffic data.Although these methods have achieved performance improvements,they often suffer from the following limitations:These methods face challenges in modeling high-order correlations between nodes.These methods overlook the interactions between nodes at different scales.To tackle these issues,in this paper,we propose a novel model named multi-scale dynamic hypergraph convolutional network(MSDHGCN)for traffic flow forecasting.Our MSDHGCN can effectively model the dynamic higher-order relationships between nodes at multiple time scales,thereby enhancing the capability for traffic forecasting.Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.展开更多
基金Supported by the Key R&D Program of Gansu Province(No.23YFGA0063)the Key Talent Project of Gansu Province(No.2024RCXM57,2024RCXM22)the Major Science and Technology Special Program of Gansu Province(No.25ZYJA037).
文摘Traffic flow prediction is a crucial element of intelligent transportation systems.However,accu-rate traffic flow prediction is quite challenging because of its highly nonlinear,complex,and dynam-ic characteristics.To address the difficulties in simultaneously capturing local and global dynamic spatiotemporal correlations in traffic flow,as well as the high time complexity of existing models,a multi-head flow attention-based local-global dynamic hypergraph convolution(MFA-LGDHC)pre-diction model is proposed.which consists of multi-head flow attention(MHFA)mechanism,graph convolution network(GCN),and local-global dynamic hypergraph convolution(LGHC).MHFA is utilized to extract the time dependency of traffic flow and reduce the time complexity of the model.GCN is employed to catch the spatial dependency of traffic flow.LGHC utilizes down-sampling con-volution and isometric convolution to capture the local and global spatial dependencies of traffic flow.And dynamic hypergraph convolution is used to model the dynamic higher-order relationships of the traffic road network.Experimental results indicate that the MFA-LGDHC model outperforms current popular baseline models and exhibits good prediction performance.
文摘View-based 3-D object retrieval has become an emerging topic in recent years,especially with the fast development of visual content acquisition devices,such as mobile phones with cameras.Extensive research efforts have been dedicated to this task,while it is still difficult to measure the relevance between two objects with multiple views.In recent years,learning-based methods have been investigated in view-based 3-D object retrieval,such as graph-based learning.It is noted that the graph-based methods suffer from the high computational cost from the graph construction and the corresponding learning process.In this paper,we introduce a general framework to accelerate the learning-based view-based 3-D object matching in large scale data.Given a query object Q and one object O from a 3-D dataset D,the first step is to extract a small set of candidate relevant 3-D objects for object O.Then multiple hypergraphs can be constructed based on this small set of 3-D objects and the learning on the fused hypergraph is conducted to generate the relevance between Q and O,which can be further used in the retrieval procedure.Experiments demonstrate the effectiveness of the proposed framework.
基金National Natural Science Foundation of China(No.62001098)Fundamental Research Funds for the Central Universities of Ministry of Education of China(No.2232020D-33)。
文摘Deep learning(DL)has shown its superior performance in dealing with various computer vision tasks in recent years.As a simple and effective DL model,autoencoder(AE)is popularly used to decompose hyperspectral images(HSIs)due to its powerful ability of feature extraction and data reconstruction.However,most existing AE-based unmixing algorithms usually ignore the spatial information of HSIs.To solve this problem,a hypergraph regularized deep autoencoder(HGAE)is proposed for unmixing.Firstly,the traditional AE architecture is specifically improved as an unsupervised unmixing framework.Secondly,hypergraph learning is employed to reformulate the loss function,which facilitates the expression of high-order similarity among locally neighboring pixels and promotes the consistency of their abundances.Moreover,L_(1/2)norm is further used to enhance abundances sparsity.Finally,the experiments on simulated data,real hyperspectral remote sensing images,and textile cloth images are used to verify that the proposed method can perform better than several state-of-the-art unmixing algorithms.
基金supported by the National Key Research and Development Program of China(No.2021YFE0102100)the National Natural Science Foundation of China(Nos.62322301,62172002,and 82060373)+2 种基金the Outstanding Youth Research Project of Universities in Anhui Province(No.2022AH020010)the University Synergy Innovation Program of Anhui Province(Nos.GXXT-2022-035 and GXXT-2021-039)the Xinjiang Tianshan Project(No.2022TSYCLJ0032).
文摘In the field of cancer treatment,drug combination therapy appears to be a promising treatment strategy compared to monotherapy.Recently,plenty of computational models are gradually applied to prioritize synergistic drug combinations.However,the existing prediction models have not fully exploited the multi-way relations between drug combinations and cell lines.Besides,the number of identified drug-drug-cell line triplets is insufficient owning to the high cost of in vitro screening,which affects the ability of models to capture and utilize multi-way relations.To address this challenge,we design the multi-view hypergraph contrastive learning model,termed MHCLSyn,for synergistic drug combination prediction.First,the synergistic drug-drug-cell line triplets are formulated as a drug synergy hypergraph,and three task-specific hypergraphs are designed based on the drug synergy hypergraph.Then,we design a multi-view hypergraph contrastive learning with enhancement schemes,which allows for more expressive and discriminative node representation learning on drug synergy hypergraph.After that,the representations of nodes indicating drug-drug-cell line triplets are inputted to fully connected network for making predictions.Extensive experiments show MHCLSyn achieves better performance than state-of-the-art prediction models on benchmark datasets and is applicable to unseen drug combinations or cell lines.Case study indicates that MHCLSyn is capable of detecting potential synergistic drug combinations.
基金the National Key Research and Development Program of China(No.2021ZD0112400)。
文摘Unlike traditional video cameras,event cameras capture asynchronous event streams in which each event encodes pixel location,triggers’timestamps,and the polarity of brightness changes.In this paper,we introduce a novel hypergraph-based framework for moving object classification.Specifically,we capture moving objects with an event camera,to perceive and collect asynchronous event streams in a high temporal resolution.Unlike stacked event frames,we encode asynchronous event data into a hypergraph,fully mining the high-order correlation of event data,and designing a mixed convolutional hypergraph neural network for training to achieve a more efficient and accurate motion target recognition.The experimental results show that our method has a good performance in moving object classification(e.g.,gait identification).
基金the National Key Research and Development Program of China(No.2021ZD0112400)。
文摘This paper focuses on the problem of traffic flow forecasting,with the aim of forecasting future traffic conditions based on historical traffic data.This problem is typically tackled by utilizing spatio-temporal graph neural networks to model the intricate spatio-temporal correlations among traffic data.Although these methods have achieved performance improvements,they often suffer from the following limitations:These methods face challenges in modeling high-order correlations between nodes.These methods overlook the interactions between nodes at different scales.To tackle these issues,in this paper,we propose a novel model named multi-scale dynamic hypergraph convolutional network(MSDHGCN)for traffic flow forecasting.Our MSDHGCN can effectively model the dynamic higher-order relationships between nodes at multiple time scales,thereby enhancing the capability for traffic forecasting.Experiments on two real-world datasets demonstrate the effectiveness of the proposed method.
