scholarly journals The collective motion of thermophoretic-type active particle suspension under spatial–temporal modulation

AIP Advances ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 125028
Author(s):  
Chong-Wai Io ◽  
Yu-Xuan Shane ◽  
Yu-Sheng Tang
Keyword(s):  
Collective Motion ◽  
Active Particle ◽  
Particle Suspension ◽  
Temporal Modulation

The mesoscopic collective motion of self-propelling active particle suspension confined in two-dimensional micro-channel

2019 ◽  
Vol 32 (9) ◽  
pp. 095101
Author(s):  
Sin-Cen Cai ◽  
Yu-Xuan Shen ◽  
Chong-Wai Io
Keyword(s):  
Collective Motion ◽  
Active Particle ◽  
Particle Suspension ◽  
Micro Channel ◽  
Two Dimensional

Local interaction rules and collective motion in black neon tetra (Hyphessobrycon herbertaxelrodi) and zebrafish (Danio rerio).

2019 ◽  
Vol 133 (2) ◽  
pp. 143-155 ◽  
Author(s):  
Vicenç Quera ◽  
Elisabet Gimeno ◽  
Francesc S. Beltran ◽  
Ruth Dolado
Keyword(s):  
Danio Rerio ◽  
Collective Motion ◽  
Local Interaction

RELAXATION AND COLLECTIVE MOTION IN SUPERCONDUCTORS. A TWO-FLUID DESCRIPTION

1978 ◽  
Vol 39 (C6) ◽  
pp. C6-488-C6-489 ◽  
Author(s):  
C. J. Pethick ◽  
H. Smith
Keyword(s):  
Collective Motion ◽  
Two Fluid

DAMPING OF COLLECTIVE MOTION, MEMORY EFFECTS AND ENTROPY

1987 ◽  
Vol 48 (C2) ◽  
pp. C2-55-C2-57
Author(s):  
K. GÜTTER
Keyword(s):  
Collective Motion ◽  
Memory Effects

Analysis of the Bath Motion in the MM-SQC Dynamics Using Unsupervised Machine Learning Dimensionality Reduction Approaches: Principal Component Analysis

2020 ◽  
Author(s):  
Jiawei Peng ◽  
Yu Xie ◽  
Deping Hu ◽  
Zhenggang Lan
Keyword(s):  
Machine Learning ◽  
Principal Component Analysis ◽  
Collective Motion ◽  
Principal Component ◽  
Component Analysis ◽  
Nonadiabatic Dynamics ◽  
Trajectory Data ◽  
Unsupervised Machine Learning ◽  
Physical Knowledge ◽  
Vibronic Couplings

The system-plus-bath model is an important tool to understand nonadiabatic dynamics for large molecular systems. The understanding of the collective motion of a huge number of bath modes is essential to reveal their key roles in the overall dynamics. We apply the principal component analysis (PCA) to investigate the bath motion based on the massive data generated from the MM-SQC (symmetrical quasi-classical dynamics method based on the Meyer-Miller mapping Hamiltonian) nonadiabatic dynamics of the excited-state energy transfer dynamics of Frenkel-exciton model. The PCA method clearly clarifies that two types of bath modes, which either display the strong vibronic couplings or have the frequencies close to electronic transition, are very important to the nonadiabatic dynamics. These observations are fully consistent with the physical insights. This conclusion is obtained purely based on the PCA understanding of the trajectory data, without the large involvement of pre-defined physical knowledge. The results show that the PCA approach, one of the simplest unsupervised machine learning methods, is very powerful to analyze the complicated nonadiabatic dynamics in condensed phase involving many degrees of freedom.

Auditory Filterbank Learning for Temporal Modulation Features in Replay Spoof Speech Detection

2018 ◽  
Author(s):  
Hardik Sailor ◽  
Madhu Kamble ◽  
Hemant Patil
Keyword(s):  
Temporal Modulation ◽  
Speech Detection

REGULAR STRUCTURE OF LOW-LYING STATES FOR ATOMIC NUCLEI UNDER RANDOM INTERACTIONS

2008 ◽  
Vol 17 (supp01) ◽  
pp. 304-317
Author(s):  
Y. M. ZHAO
Keyword(s):  
Ground State ◽  
Collective Motion ◽  
Regular Structure ◽  
Positive Parity ◽  
Many Body ◽  
Random Interactions ◽  
Atomic Nuclei ◽  
Many Body Systems

In this paper we review regularities of low-lying states for many-body systems, in particular, atomic nuclei, under random interactions. We shall discuss the famous problem of spin zero ground state dominance, positive parity dominance, collective motion, odd-even staggering, average energies, etc., in the presence of random interactions.

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