A hierarchical 3D-motion learning framework for animal spontaneous behavior mapping
Animal behavior usually has a hierarchical structure and dynamics. Here, the authors propose a parallel and multi-layered framework to learn the hierarchical dynamics and generate an objective metric to map the behaviour into the feature space.
Guardado en:
Autores principales: | Kang Huang, Yaning Han, Ke Chen, Hongli Pan, Gaoyang Zhao, Wenling Yi, Xiaoxi Li, Siyuan Liu, Pengfei Wei, Liping Wang |
---|---|
Formato: | article |
Lenguaje: | EN |
Publicado: |
Nature Portfolio
2021
|
Materias: | |
Acceso en línea: | https://doaj.org/article/22d70b3eba9c46f58c59165575b32afc |
Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
Ejemplares similares
-
An Intelligent Hierarchical Security Framework for VANETs
por: Fábio Gonçalves, et al.
Publicado: (2021) -
Rats spontaneously perceive global motion direction of drifting plaids.
por: Giulio Matteucci, et al.
Publicado: (2021) -
Liquid metal amoeba with spontaneous pseudopodia formation and motion capability
por: Liang Hu, et al.
Publicado: (2017) -
Time-ordering in Heisenberg’s equation of motion as related to spontaneous radiation
por: Benjamin D. Strycker
Publicado: (2021) -
Macroscopic helical chirality and self-motion of hierarchical self-assemblies induced by enantiomeric small molecules
por: Yang Yang, et al.
Publicado: (2018)