scholarly journals UNCOVERING LOW-DIMENSIONAL TOPOLOGICAL STRUCTURE IN THE QCD VACUUM

Author(s):  
I. HORVÁTH ◽  
S.J. DONG ◽  
T. DRAPER ◽  
K.F. LIU ◽  
N. MATHUR ◽  
...  
2004 ◽  
Vol 129-130 ◽  
pp. 677-679 ◽  
Author(s):  
I. Horváth ◽  
S.J. Dong ◽  
T. Draper ◽  
F.X. Lee ◽  
K.F. Liu ◽  
...  

2018 ◽  
Vol 37 (10) ◽  
pp. 1233-1252 ◽  
Author(s):  
Jonathan Hoff ◽  
Alireza Ramezani ◽  
Soon-Jo Chung ◽  
Seth Hutchinson

In this article, we present methods to optimize the design and flight characteristics of a biologically inspired bat-like robot. In previous, work we have designed the topological structure for the wing kinematics of this robot; here we present methods to optimize the geometry of this structure, and to compute actuator trajectories such that its wingbeat pattern closely matches biological counterparts. Our approach is motivated by recent studies on biological bat flight that have shown that the salient aspects of wing motion can be accurately represented in a low-dimensional space. Although bats have over 40 degrees of freedom (DoFs), our robot possesses several biologically meaningful morphing specializations. We use principal component analysis (PCA) to characterize the two most dominant modes of biological bat flight kinematics, and we optimize our robot’s parametric kinematics to mimic these. The method yields a robot that is reduced from five degrees of actuation (DoAs) to just three, and that actively folds its wings within a wingbeat period. As a result of mimicking synergies, the robot produces an average net lift improvesment of 89% over the same robot when its wings cannot fold.


Author(s):  
Ernst-Michael Ilgenfritz ◽  
Karl Koller ◽  
Yoshiaki Koma ◽  
Gerrit Schierholz ◽  
Volker Weinberg

2006 ◽  
Vol 153 (1) ◽  
pp. 328-335 ◽  
Author(s):  
E.-M. Ilgenfritz ◽  
K. Koller ◽  
Y. Koma ◽  
G. Schierholz ◽  
T. Streuer ◽  
...  

2016 ◽  
Vol 31 (28n29) ◽  
pp. 1645023 ◽  
Author(s):  
Dmitri E. Kharzeev

QCD possesses a compact gauge group, and this implies a non-trivial topological structure of the vacuum. In this contribution to the Gribov-85 Memorial volume, we first discuss the origin of Gribov copies and their interpretation in terms of fluctuating topology in the QCD vacuum. We then describe the recent work with E. Levin that links the confinement of gluons and color screening to the fluctuating topology, and discuss implications for spin physics, high energy scattering, and the physics of quark-gluon plasma.


2003 ◽  
Vol 68 (11) ◽  
Author(s):  
I. Horváth ◽  
S. J. Dong ◽  
T. Draper ◽  
F. X. Lee ◽  
K. F. Liu ◽  
...  

2001 ◽  
Vol 16 (supp01c) ◽  
pp. 1210-1212
Author(s):  
H. B. THACKER

Several lattice calculations which probe the chiral and topological structure of QCD are discussed. The results focus attention on the low-lying eigenmodes of the Dirac operator in typical gauge field configurations.


2021 ◽  
Vol 15 ◽  
Author(s):  
Louis Kang ◽  
Boyan Xu ◽  
Dmitriy Morozov

Persistent cohomology is a powerful technique for discovering topological structure in data. Strategies for its use in neuroscience are still undergoing development. We comprehensively and rigorously assess its performance in simulated neural recordings of the brain's spatial representation system. Grid, head direction, and conjunctive cell populations each span low-dimensional topological structures embedded in high-dimensional neural activity space. We evaluate the ability for persistent cohomology to discover these structures for different dataset dimensions, variations in spatial tuning, and forms of noise. We quantify its ability to decode simulated animal trajectories contained within these topological structures. We also identify regimes under which mixtures of populations form product topologies that can be detected. Our results reveal how dataset parameters affect the success of topological discovery and suggest principles for applying persistent cohomology, as well as persistent homology, to experimental neural recordings.


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