Collective Motions and Formations of Multi-robots Based on Simple Dynamics

2007 ◽  
Author(s):  
Ken Sugawara
Keyword(s):  
Collective Motions

Understanding of social behaviour in human collective motions with non-trivial rule of control

Impact ◽  
2019 ◽  
Vol 2019 (10) ◽  
pp. 84-86
Author(s):  
Keisuke Fujii
Keyword(s):  
Machine Learning ◽  
Team Sports ◽  
Machine Learning Techniques ◽  
Theoretical Modelling ◽  
Video Footage ◽  
Learning Techniques ◽  
Collective Motions ◽  
Tracking Movements ◽  
Or Team ◽  
Computational Ability

The coordination and movement of people in large crowds, during sports games or when socialising, seems readily explicable. Sometimes this occurs according to specific rules or instructions such as in a sport or game, at other times the motivations for movement may be more focused around an individual's needs or fears. Over the last decade, the computational ability to identify and track a given individual in video footage has increased. The conventional methods of how data is gathered and interpreted in biology rely on fitting statistical results to particular models or hypotheses. However, data from tracking movements in social groups or team sports are so complex as they cannot easily analyse the vast amounts of information and highly varied patterns. The author is an expert in human behaviour and machine learning who is based at the Graduate School of Informatics at Nagoya University. His challenge is to bridge the gap between rule-based theoretical modelling and data-driven modelling. He is employing machine learning techniques to attempt to solve this problem, as a visiting scientist in RIKEN Center for Advanced Intelligence Project.

Normal Mode Analysis of Trp RNA Binding Attenuation Protein: Structure and Collective Motions

2011 ◽  
Vol 51 (9) ◽  
pp. 2361-2371 ◽  
Author(s):  
Guang Hu ◽  
Servaas Michielssens ◽  
Samuel L. C. Moors ◽  
Arnout Ceulemans
Keyword(s):  
Protein Structure ◽  
Normal Mode ◽  
Rna Binding ◽  
Normal Mode Analysis ◽  
Mode Analysis ◽  
Collective Motions

scholarly journals Transcriptional Protein-Protein Cooperativity in POU/HMG/DNA Complexes Revealed by Normal Mode Analysis

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Debby D. Wang ◽  
Hong Yan
Keyword(s):  
Normal Mode ◽  
Normal Mode Analysis ◽  
Embryonic Stem ◽  
Interaction Mechanism ◽  
Mode Analysis ◽  
Biological Processes ◽  
Dna Complexes ◽  
Median Correlation ◽  
Collective Motions ◽  
Statistical Studies

Biomolecular cooperativity is of great scientific interest due to its role in biological processes. Two transcription factors (TFs), Oct-4 and Sox-2, are crucial in transcriptional regulation of embryonic stem cells. In this paper, we analyze how Oct-1 (a similar POU factor) and Sox-2, interact cooperatively at their enhancer binding sites in collective motions. Normal mode analysis (NMA) is implemented to study the collective motions of two complexes with each involving these TFs and an enhancer. The special structure of Oct proteins is analyzed comprehensively, after which each Oct/Sox group is reassembled into two protein pairs. We subsequently propose a segmentation idea to extract the most correlated segments in each pair, using correlations of motion magnitude curves. The median analysis on these correlation values shows the intimacy of subunit POUS (Oct-1) and Sox-2. Using those larger-than-median correlation values, we conduct statistical studies and propose several protein-protein cooperative modes (SandD) coupled with their subtypes. Additional filters are applied and similar results are obtained. A supplementary study on the rotation angle curves reaches an agreement with these modes. Overall, these proposed cooperative modes provide useful information for us to understand the complicated interaction mechanism in the POU/HMG/DNA complexes.

scholarly journals Multi-agent Attentional Activity Recognition

2019 ◽  
Author(s):  
Kaixuan Chen ◽  
Lina Yao ◽  
Dalin Zhang ◽  
Bin Guo ◽  
Zhiwen Yu
Keyword(s):  
Activity Recognition ◽  
State Of The Art ◽  
Body Part ◽  
Body Parts ◽  
Temporal Attention ◽  
Attention Model ◽  
Proposed Model ◽  
Collective Motions ◽  
Multi Agent ◽  
Real World Datasets

Multi-modality is an important feature of sensor based activity recognition. In this work, we consider two inherent characteristics of human activities, the spatially-temporally varying salience of features and the relations between activities and corresponding body part motions. Based on these, we propose a multi-agent spatial-temporal attention model. The spatial-temporal attention mechanism helps intelligently select informative modalities and their active periods. And the multiple agents in the proposed model represent activities with collective motions across body parts by independently selecting modalities associated with single motions. With a joint recognition goal, the agents share gained information and coordinate their selection policies to learn the optimal recognition model. The experimental results on four real-world datasets demonstrate that the proposed model outperforms the state-of-the-art methods.

Sign in / Sign up

Export Citation Format

Share Document