An Investigation of Surface Reconstruction from Binocular Disparity Based on Standard Regularization Theory: Comparison between “Membrane” and “Thin-Plate” Potential Energy Models

2011 ◽  
Vol 113 (1) ◽  
pp. 113-126
Author(s):  
Aya Shiraiwa ◽  
Takefumi Hayashi
Keyword(s):  
Potential Energy ◽  
Surface Reconstruction ◽  
Thin Plate ◽  
Binocular Disparity ◽  
Regularization Theory ◽  
Energy Models ◽  
Theory Comparison ◽  
Standard Regularization

scholarly journals DP-GEN: A concurrent learning platform for the generation of reliable deep learning based potential energy models

2020 ◽  
Vol 253 ◽  
pp. 107206 ◽  
Author(s):  
Yuzhi Zhang ◽  
Haidi Wang ◽  
Weijie Chen ◽  
Jinzhe Zeng ◽  
Linfeng Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Potential Energy ◽  
Concurrent Learning ◽  
Energy Models ◽  
Learning Platform

Equivalence of the three empirical potential energy models for diatomic molecules

2012 ◽  
Vol 274 ◽  
pp. 5-8 ◽  
Author(s):  
Ping-Quan Wang ◽  
Lie-Hui Zhang ◽  
Chun-Sheng Jia ◽  
Jian-Yi Liu
Keyword(s):  
Potential Energy ◽  
Diatomic Molecules ◽  
Empirical Potential ◽  
Energy Models

scholarly journals Neural encoding of binocular disparity: Energy models, position shifts and phase shifts

1996 ◽  
Vol 36 (12) ◽  
pp. 1839-1857 ◽  
Author(s):  
David J. Fleet ◽  
Hermann Wagner ◽  
David J. Heeger
Keyword(s):  
Binocular Disparity ◽  
Phase Shifts ◽  
Neural Encoding ◽  
Energy Models

Improved expressions for the Schiöberg potential energy models for diatomic molecules

2012 ◽  
Vol 278 ◽  
pp. 23-26 ◽  
Author(s):  
Ping-Quan Wang ◽  
Jian-Yi Liu ◽  
Lie-Hui Zhang ◽  
Si-Yi Cao ◽  
Chun-Sheng Jia
Keyword(s):  
Potential Energy ◽  
Diatomic Molecules ◽  
Energy Models

Additions and corrections: Potential energy models for nonbonding interactions in solid chlorine

1980 ◽  
Vol 19 (7) ◽  
pp. 2200-2200 ◽  
Author(s):  
Leh-Yeh Hsu ◽  
Donald E. Williams
Keyword(s):  
Potential Energy ◽  
Energy Models

On Potential Energy Models for EA-based Ab Initio Protein Structure Prediction

2010 ◽  
Vol 18 (2) ◽  
pp. 255-275 ◽  
Author(s):  
Milan Mijajlovic ◽  
Mark J. Biggs ◽  
Dusan P. Djurdjevic
Keyword(s):  
Protein Structure ◽  
Potential Energy ◽  
Ab Initio ◽  
Protein Structure Prediction ◽  
Structure Prediction ◽  
Three Dimensional ◽  
Free Energy Surface ◽  
Energy Models ◽  
Parameter Values

Ab initio protein structure prediction involves determination of the three-dimensional (3D) conformation of proteins on the basis of their amino acid sequence, a potential energy (PE) model that captures the physics of the interatomic interactions, and a method to search for and identify the global minimum in the PE (or free energy) surface such as an evolutionary algorithm (EA). Many PE models have been proposed over the past three decades and more. There is currently no understanding of how the behavior of an EA is affected by the PE model used. The study reported here shows that the EA behavior can be profoundly affected: the EA performance obtained when using the ECEPP PE model is significantly worse than that obtained when using the Amber, OPLS, and CVFF PE models, and the optimal EA control parameter values for the ECEPP model also differ significantly from those associated with the other models.

Potential energy models of excited compound nucleus

2021 ◽  
Vol 258 ◽  
pp. 107605
Author(s):  
P.N. Nadtochy ◽  
E.G. Ryabov ◽  
A.V. Karpov ◽  
D.V. Vanin ◽  
G.D. Adeev
Keyword(s):  
Potential Energy ◽  
Compound Nucleus ◽  
Energy Models

scholarly journals Properties of Surface Reconstruction Based on Binocular Disparity and Motion Parallax

i-Perception ◽  
10.1068/ic392 ◽  
2011 ◽  
Vol 2 (4) ◽  
pp. 392-392
Author(s):  
Aya Shiraiwa ◽  
Takefumi Hayashi
Keyword(s):  
Surface Reconstruction ◽  
Binocular Disparity ◽  
Motion Parallax
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