A broader view on jamming: from spring networks to circle packings

Soft Matter ◽  
2019 ◽  
Vol 15 (15) ◽  
pp. 3076-3084 ◽  
Author(s):  
Varda F. Hagh ◽  
Eric I. Corwin ◽  
Kenneth Stephenson ◽  
M. F. Thorpe
Keyword(s):  
Elastic Networks ◽  
Circle Packings

Jammed packings can be generated by pruning elastic networks and mapping them into circle packings.

Fracture Growth in 2 d Elastic Networks with Born Model

1989 ◽  
Vol 10 (1) ◽  
pp. 7-13 ◽  
Author(s):  
H Yan ◽  
G Li ◽  
L. M Sander
Keyword(s):  
Elastic Networks ◽  
Born Model ◽  
Fracture Growth

Interactions in Sphere Packings

2001 ◽  
Vol 215 (5) ◽  
Author(s):  
J. Hauck ◽  
K. Mika
Keyword(s):  
Sphere Packings ◽  
Homogeneous Sphere ◽  
Circle Packings

The circle packings including non-periodic packings and 41 homogeneous sphere packings with 4≤ T

scholarly journals Addendum to our paper “The Schwarz-Pick lemma for circle packings”

1992 ◽  
Vol 36 (2) ◽  
pp. 177-177
Author(s):  
Alan F. Beardon ◽  
Kenneth Stephenson
Keyword(s):  
Circle Packings

Complexity and elastic networks

2010 ◽  
Vol 25 (3) ◽  
pp. e2
Author(s):  
Béla Suki
Keyword(s):  
Elastic Networks

Curvilinear polygons, finite circle packings, and normal grain growth

2004 ◽  
Vol 385 (1-2) ◽  
pp. 229-234 ◽  
Author(s):  
M NORDBAKKE ◽  
N RYUM ◽  
O HUNDERI
Keyword(s):  
Grain Growth ◽  
Circle Packings

Intention and Body-Mood Engineering via Proactive Robot Moves in HRI

2015 ◽  
pp. 255-282 ◽  
Author(s):  
O. Can Görür ◽  
Aydan M. Erkmen
Keyword(s):  
Path Planning ◽  
Mobile Robots ◽  
Human Body ◽  
Mood Induction ◽  
Positive Mood ◽  
Focused Attention ◽  
Elastic Networks ◽  
Human Intention ◽  
Novel Concept ◽  
Intention Estimation

This chapter focuses on emotion and intention engineering by socially interacting robots that induce desired emotions/intentions in humans. The authors provide all phases that pave this road, supported by overviews of leading works in the literature. The chapter is partitioned into intention estimation, human body-mood detection through external-focused attention, path planning through mood induction and reshaping intention. Moreover, the authors present their novel concept, with implementation, of reshaping current human intention into a desired one, using contextual motions of mobile robots. Current human intention has to be deviated towards the new desired one by destabilizing the obstinance of human intention, inducing positive mood and making the “robot gain curiosity of human”. Deviations are generated as sequences of transient intentions tracing intention trajectories. The authors use elastic networks to generate, in two modes of body mood: “confident” and “suspicious”, transient intentions directed towards the desired one, choosing among intentional robot moves previously learned by HMM.

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