At present,most k-dominant Skyline query algorithms are oriented to static datasets,this paper proposes a k-dominant Skyline query algorithm for dynamic datasets.The algorithm is recursive circularly.First,we compute ...At present,most k-dominant Skyline query algorithms are oriented to static datasets,this paper proposes a k-dominant Skyline query algorithm for dynamic datasets.The algorithm is recursive circularly.First,we compute the dominant ability of each object and sort objects in descending order by dominant ability.Then,we maintain an inverted index of the dominant index by k-dominant Skyline point calculation algorithm.When the data changes,it is judged whether the update point will afect the k dominant Skyline point set.So the k-dominant Skyline point of the new data set is obtained by inserting and deleting algorithm.The proposed algorithm resolves maintenance isue of a frequently updated database by dynamically updating the data sets.The experimental results show that the query algorithm can efectively improve query eficiency.展开更多
The deformability and high degree of freedom of mollusks bring challenges in mathematical modeling and synthesis of motions.Traditional analytical and statistical models are limited by either rigid skeleton assumption...The deformability and high degree of freedom of mollusks bring challenges in mathematical modeling and synthesis of motions.Traditional analytical and statistical models are limited by either rigid skeleton assumptions or model capacity,and have difficulty in generating realistic and multi-pattern mollusk motions.In this work,we present a large-scale dynamic pose dataset of Drosophila larvae and propose a motion synthesis model named Path2Pose to generate a pose sequence given the initial poses and the subsequent guiding path.The Path2Pose model is further used to synthesize long pose sequences of various motion patterns through a recursive generation method.Evaluation analysis results demonstrate that our novel model synthesizes highly realistic mollusk motions and achieves state-of-the-art performance.Our work proves high performance of deep neural networks for mollusk motion synthesis and the feasibility of long pose sequence synthesis based on the customized body shape and guiding path.展开更多
基金The authors are grateful to the editors and reviewers for their helpful comments and suggestions.This research was partially supported by National Key R&D Program of China(2018********01)National Social Science Foundation project(17BXW065)+1 种基金Science and Technology Research project of Henan province(172102310628,162102310616)Science and Technology Research project of Zhengzhou(141PPTGG368).
文摘At present,most k-dominant Skyline query algorithms are oriented to static datasets,this paper proposes a k-dominant Skyline query algorithm for dynamic datasets.The algorithm is recursive circularly.First,we compute the dominant ability of each object and sort objects in descending order by dominant ability.Then,we maintain an inverted index of the dominant index by k-dominant Skyline point calculation algorithm.When the data changes,it is judged whether the update point will afect the k dominant Skyline point set.So the k-dominant Skyline point of the new data set is obtained by inserting and deleting algorithm.The proposed algorithm resolves maintenance isue of a frequently updated database by dynamically updating the data sets.The experimental results show that the query algorithm can efectively improve query eficiency.
基金supported by the Zhejiang Lab,China(No.2020KB0AC02)the Zhejiang Provincial Key R&D Program,China(Nos.2022C01022,2022C01119,and 2021C03003)+2 种基金the National Natural Science Foundation of China(Nos.T2293723 and 61972347)the Zhejiang Provincial Natural Science Foundation,China(No.LR19F020005)the Fundamental Research Funds for the Central Universities,China(No.226-2022-00051)。
文摘The deformability and high degree of freedom of mollusks bring challenges in mathematical modeling and synthesis of motions.Traditional analytical and statistical models are limited by either rigid skeleton assumptions or model capacity,and have difficulty in generating realistic and multi-pattern mollusk motions.In this work,we present a large-scale dynamic pose dataset of Drosophila larvae and propose a motion synthesis model named Path2Pose to generate a pose sequence given the initial poses and the subsequent guiding path.The Path2Pose model is further used to synthesize long pose sequences of various motion patterns through a recursive generation method.Evaluation analysis results demonstrate that our novel model synthesizes highly realistic mollusk motions and achieves state-of-the-art performance.Our work proves high performance of deep neural networks for mollusk motion synthesis and the feasibility of long pose sequence synthesis based on the customized body shape and guiding path.
