No matter classical narratology or postclassical narratology,both put more emphasis on studies from a temporal dimension while neglecting the spatial one.Yet in fact,as all the other integrate studies,there exists in ...No matter classical narratology or postclassical narratology,both put more emphasis on studies from a temporal dimension while neglecting the spatial one.Yet in fact,as all the other integrate studies,there exists in narratological studies a temporal dimension and a spatial one as well.With the emergence of all kinds of problems and the maturity of all sorts of research conditions,now it’s time to attach importance to studies from spatial dimension.Therefore,all-round and indepth studies should be carried out around narrative and spatial issues so as to establish spatial narratology.And as a new realm of narratological studies,spatial narratology involves a question domain of a wide field.展开更多
Visual object recognition in humans and nonhuman primates is achieved by the ventral visual pathway(ventral occipital-temporal cortex,VOTC),which shows a well-documented object domain structure.An on-going question is...Visual object recognition in humans and nonhuman primates is achieved by the ventral visual pathway(ventral occipital-temporal cortex,VOTC),which shows a well-documented object domain structure.An on-going question is what type of information is processed in the higher-order VOTC that underlies such observations,with recent evidence suggesting effects of certain visual features.Combining computational vision models,fMRI experiment using a parametric-modulation approach,and natural image statistics of common objects,we depicted the neural distribution of a comprehensive set of visual features in the VOTC,identifying voxel sensitivities with specific feature sets across geometry/shape,Fourier power,and color.The visual feature combination pattern in the VOTC is significantly explained by their relationships to different types of response-action computation(fight-or-flight,navigation,and manipulation),as derived from behavioral ratings and natural image statistics.These results offer a comprehensive visual feature map in the VOTC and a plausible theoretical explanation as a mapping onto different types of downstream response-action systems.展开更多
Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).H...Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep learning.Therefore,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep learning.The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving.Our approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD efficiently.To further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is discussed.Experimental results show that our approach facilitates the training process of AI components with the STTD.展开更多
文摘No matter classical narratology or postclassical narratology,both put more emphasis on studies from a temporal dimension while neglecting the spatial one.Yet in fact,as all the other integrate studies,there exists in narratological studies a temporal dimension and a spatial one as well.With the emergence of all kinds of problems and the maturity of all sorts of research conditions,now it’s time to attach importance to studies from spatial dimension.Therefore,all-round and indepth studies should be carried out around narrative and spatial issues so as to establish spatial narratology.And as a new realm of narratological studies,spatial narratology involves a question domain of a wide field.
基金by the National Natural Science Foundation of China(31671128,31925020,31700999,31700943,and 31500882)the Changjiang Scholar Professorship Award(T2016031)Fundamental Research Funds for the Central Universities(2017EYT35).
文摘Visual object recognition in humans and nonhuman primates is achieved by the ventral visual pathway(ventral occipital-temporal cortex,VOTC),which shows a well-documented object domain structure.An on-going question is what type of information is processed in the higher-order VOTC that underlies such observations,with recent evidence suggesting effects of certain visual features.Combining computational vision models,fMRI experiment using a parametric-modulation approach,and natural image statistics of common objects,we depicted the neural distribution of a comprehensive set of visual features in the VOTC,identifying voxel sensitivities with specific feature sets across geometry/shape,Fourier power,and color.The visual feature combination pattern in the VOTC is significantly explained by their relationships to different types of response-action computation(fight-or-flight,navigation,and manipulation),as derived from behavioral ratings and natural image statistics.These results offer a comprehensive visual feature map in the VOTC and a plausible theoretical explanation as a mapping onto different types of downstream response-action systems.
基金supports for this work,provided by the National Natural Science Foundation of China(Grant No.61972153)the National Key Research and Development Program(No.2018YFE0101000)+1 种基金the Key projects of the Ministry of Science and Technology(No.2020AAA0107800)are gratefully acknowledged.
文摘Nowadays,autonomous driving has been attracted widespread attention from academia and industry.As we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep learning.Therefore,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep learning.The novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous driving.Our approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD efficiently.To further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is discussed.Experimental results show that our approach facilitates the training process of AI components with the STTD.
