The metaverse is a visual world that blends the physical world and digital world.At present,the development of the metaverse is still in the early stage,and there lacks a framework for the visual construction and expl...The metaverse is a visual world that blends the physical world and digital world.At present,the development of the metaverse is still in the early stage,and there lacks a framework for the visual construction and exploration of the metaverse.In this paper,we propose a framework that summarizes how graphics,interaction,and visualization techniques support the visual construction of the metaverse and user-centric exploration.We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline.We propose a taxonomy of interaction technologies based on interaction tasks,user actions,feedback and various sensory channels,and a taxonomy of visualization techniques that assist user awareness.Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse.We hope this paper can provide a stepping stone for further research in the area of graphics,interaction and visualization in the metaverse.展开更多
According to smoothness assumption,local topological structure can be shared between feature and label manifolds.This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA)to implement the label e...According to smoothness assumption,local topological structure can be shared between feature and label manifolds.This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA)to implement the label enhancement process.In general,we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds.Then,we establish an unconstrained optimization model based on the optimal theory presented in this paper.The model is suitable for solving problems with a large number of sample points.Finally,the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy.展开更多
The digital twin brain(DTB)computing model from brain-inspired computing research is an emerging artificial intelligence technique,which is realized by a computational modeling approach of hardware and software.It can...The digital twin brain(DTB)computing model from brain-inspired computing research is an emerging artificial intelligence technique,which is realized by a computational modeling approach of hardware and software.It can achieve various cognitive abilities and their synergistic mechanisms in a manner similar to the human brain.Given that the task of the DTB is to simulate the functions of the human brain,comparing the similarities and differences between the two is crucial.However,the visualization study of the DTB is still under-researched.Moreover,the complexity of the datasets(multilevel spatiotemporal granularity and different types of comparison tasks)presents new challenges to the analysis and exploration of visualization.Therefore,in this study,we proposed DTBVis,a visual analytics system that supports comparison tasks for the DTB.DTBVis supports iterative explorations from different levels and at different granularities.Combined with automatic similarity recommendation,and high-dimensional exploration,DTBVis can assist experts in understanding the similarities and differences between the DTB and the human brain,thus helping them adjust their model and enhance its functionality.The highest level of DTBVis shows an overview of the datasets from the brain,which is used for comparison and exploration of the function and structure of the DTB and the human brain.The medium level is used for the comparison and exploration of a designated brain region.The low level can analyze a designated brain voxel.We worked closely with experts of brain science and held regular seminars with them.Feedback from the experts indicates that our approach helps them conduct comparative studies of the DTB and human brain and make modeling adjustments of the DTB through intuitive visual comparisons and interactive explorations.展开更多
The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities.They often output a great deal of spatio-temporal simulation data,which are traditionally charac...The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities.They often output a great deal of spatio-temporal simulation data,which are traditionally characterized as single-run,multi-run,multi-variate,multi-modal and multi-dimensional.From the perspective of data exploration and analysis,we noticed that many works focusing on spatiotemporal simulation data often share similar exploration techniques,for example,the exploration schemes designed in simulation space,parameter space,feature space and combinations of them.However,it lacks a survey to have a systematic overview of the essential commonalities shared by those works.In this survey,we take a novel multi-space perspective to categorize the state-ofthe-art works into three major categories.Specifically,the works are characterized as using similar techniques such as visual designs in simulation space(e.g,visual mapping,boxplot-based visual summarization,etc.),parameter space analysis(e.g,visual steering,parameter space projection,etc.)and data processing in feature space(e.g,feature definition and extraction,sampling,reduction and clustering of simulation data,etc.).展开更多
基金Shanghai Municipal Science and Technology Major Project(No.2018SHZDZX01,2021SHZDZX0103)and ZJLab.This work is also supported by Shanghai Sailing Program No.21YF1402900,Science and Technology Commission of Shanghai Municipality(Grant No.21ZR1403300)+1 种基金by Open Research Fund of Beijing Key Laboratory of Big Data Technology for Food Safety(Project No.BTBD-2021KF03)Beijing Technology and Business University and NSFC No.61972010.
文摘The metaverse is a visual world that blends the physical world and digital world.At present,the development of the metaverse is still in the early stage,and there lacks a framework for the visual construction and exploration of the metaverse.In this paper,we propose a framework that summarizes how graphics,interaction,and visualization techniques support the visual construction of the metaverse and user-centric exploration.We introduce three kinds of visual elements that compose the metaverse and the two graphical construction methods in a pipeline.We propose a taxonomy of interaction technologies based on interaction tasks,user actions,feedback and various sensory channels,and a taxonomy of visualization techniques that assist user awareness.Current potential applications and future opportunities are discussed in the context of visual construction and exploration of the metaverse.We hope this paper can provide a stepping stone for further research in the area of graphics,interaction and visualization in the metaverse.
基金supported by the National Natural Science Foundation of China(Nos.61702270,41971343,and 61702271)China Postdoctoral Science Foundation(No.2017M621592)China Scholarship Council(No.CSC201906865006)。
文摘According to smoothness assumption,local topological structure can be shared between feature and label manifolds.This study proposes a new algorithm based on Local Tangent Space Alignment(LTSA)to implement the label enhancement process.In general,we first establish a learning model for feature extraction in label space and use a feature extraction method of LTSA to guide the reconstruction of label manifolds.Then,we establish an unconstrained optimization model based on the optimal theory presented in this paper.The model is suitable for solving problems with a large number of sample points.Finally,the experiment results show that the algorithm can effectively improve the training speed and multilabel dataset prediction accuracy.
基金This work is supported by National Natural Science Foundation of China(NSFC No.62202105)Shanghai Municipal Science and Technology Major Project,China(No.2018SHZDZX01,2021SHZDZX0103)General Program,China(No.21ZR1403300),Sailing Program,China(No.21YF1402900)and ZJLab,China.
文摘The digital twin brain(DTB)computing model from brain-inspired computing research is an emerging artificial intelligence technique,which is realized by a computational modeling approach of hardware and software.It can achieve various cognitive abilities and their synergistic mechanisms in a manner similar to the human brain.Given that the task of the DTB is to simulate the functions of the human brain,comparing the similarities and differences between the two is crucial.However,the visualization study of the DTB is still under-researched.Moreover,the complexity of the datasets(multilevel spatiotemporal granularity and different types of comparison tasks)presents new challenges to the analysis and exploration of visualization.Therefore,in this study,we proposed DTBVis,a visual analytics system that supports comparison tasks for the DTB.DTBVis supports iterative explorations from different levels and at different granularities.Combined with automatic similarity recommendation,and high-dimensional exploration,DTBVis can assist experts in understanding the similarities and differences between the DTB and the human brain,thus helping them adjust their model and enhance its functionality.The highest level of DTBVis shows an overview of the datasets from the brain,which is used for comparison and exploration of the function and structure of the DTB and the human brain.The medium level is used for the comparison and exploration of a designated brain region.The low level can analyze a designated brain voxel.We worked closely with experts of brain science and held regular seminars with them.Feedback from the experts indicates that our approach helps them conduct comparative studies of the DTB and human brain and make modeling adjustments of the DTB through intuitive visual comparisons and interactive explorations.
基金supported by the National Natural Science Foundation of China(NSFC)Grant Nos.61702271,61702270.
文摘The widespread use of numerical simulations in different scientific domains provides a variety of research opportunities.They often output a great deal of spatio-temporal simulation data,which are traditionally characterized as single-run,multi-run,multi-variate,multi-modal and multi-dimensional.From the perspective of data exploration and analysis,we noticed that many works focusing on spatiotemporal simulation data often share similar exploration techniques,for example,the exploration schemes designed in simulation space,parameter space,feature space and combinations of them.However,it lacks a survey to have a systematic overview of the essential commonalities shared by those works.In this survey,we take a novel multi-space perspective to categorize the state-ofthe-art works into three major categories.Specifically,the works are characterized as using similar techniques such as visual designs in simulation space(e.g,visual mapping,boxplot-based visual summarization,etc.),parameter space analysis(e.g,visual steering,parameter space projection,etc.)and data processing in feature space(e.g,feature definition and extraction,sampling,reduction and clustering of simulation data,etc.).
