Fronto-parietal mirror neuron system modeling: visuospatial transformations support imitation learning independently of imitator perspective

H Oh, AR Braun, JA Reggia, RJ Gentili - Human Movement Science, 2019 - Elsevier
Although the human mirror neuron system (MNS) is critical for action observation and
imitation, most MNS investigations overlook the visuospatial transformation processes that …

A Cognitive Robotic Imitation Learning System Based On Cause-Effect Reasoning

GE Katz - 2017 - search.proquest.com
As autonomous systems become more intelligent and ubiquitous, it is increasingly important
that their behavior can be easily controlled and understood by human end users. Robotic …

Trajectory Tracking of COVID-19 Epidemic Risk Using Self-organizing Feature Map

N Chen, A Chen, X Yao - Bulletin of the Chinese Academy of …, 2022 - bcas.edpsciences.org
The ongoing COVID-19 has become a worldwide pandemic with increasing confirmed
cases and deaths across the globe. By July 2022, the number of cumulative confirmed cases …

Assessment and clustering of temporal disaster risk: Two case studies of China

N Chen, Z Zhang, Y Ma, A Chen… - Intelligent Decision …, 2022 - content.iospress.com
Disaster risk assessment is the foundation to carry out a comprehensive disaster reduction.
Despite a growing body of literature on this subject, dynamic risk assessment concerning the …

Enhanced learning strategies for tactile shape estimation and grasp planning of unknown objects

S Yang - 2019 - uwspace.uwaterloo.ca
Grasping is one of the key capabilities for a robot operating and interacting with humans in a
real environment. The conventional approaches require accurate information on both object …

GASOM: Genetic Algorithm Assisted Architecture Learning in Self Organizing Maps

A Saboo, A Sharma, T Dash - … 2017, Guangzhou, China, November 14-18 …, 2017 - Springer
Abstract Self Organizing Map (SOM) is a special kind of neuron architecture that partially
simulates the visual cortex of the animal brain and has been proven to be exceptionally …