scholarly journals Fast Solver for Large Scale Multistate Bennett Acceptance Ratio Equations

2019 ◽  
Vol 15 (2) ◽  
pp. 799-802 ◽  
Author(s):  
Xinqiang Ding ◽  
Jonah Z. Vilseck ◽  
Charles L. Brooks
Keyword(s):  
Large Scale ◽  
Acceptance Ratio ◽  
Fast Solver ◽  
Bennett Acceptance Ratio

scholarly journals Multiensemble Markov models of molecular thermodynamics and kinetics

2016 ◽  
Vol 113 (23) ◽  
pp. E3221-E3230 ◽  
Author(s):  
Hao Wu ◽  
Fabian Paul ◽  
Christoph Wehmeyer ◽  
Frank Noé
Keyword(s):  
Detailed Balance ◽  
Rare Event ◽  
Umbrella Sampling ◽  
High Dimensional ◽  
Acceptance Ratio ◽  
Markov State ◽  
Markov State Models ◽  
Bennett Acceptance Ratio ◽  
Special Cases ◽  
State Models

We introduce the general transition-based reweighting analysis method (TRAM), a statistically optimal approach to integrate both unbiased and biased molecular dynamics simulations, such as umbrella sampling or replica exchange. TRAM estimates a multiensemble Markov model (MEMM) with full thermodynamic and kinetic information at all ensembles. The approach combines the benefits of Markov state models—clustering of high-dimensional spaces and modeling of complex many-state systems—with those of the multistate Bennett acceptance ratio of exploiting biased or high-temperature ensembles to accelerate rare-event sampling. TRAM does not depend on any rate model in addition to the widely used Markov state model approximation, but uses only fundamental relations such as detailed balance and binless reweighting of configurations between ensembles. Previous methods, including the multistate Bennett acceptance ratio, discrete TRAM, and Markov state models are special cases and can be derived from the TRAM equations. TRAM is demonstrated by efficiently computing MEMMs in cases where other estimators break down, including the full thermodynamics and rare-event kinetics from high-dimensional simulation data of an all-atom protein–ligand binding model.

scholarly journals Multimodal access control system combining RFID, fingerprint and facial recognition

2020 ◽  
Vol 20 (1) ◽  
pp. 405
Author(s):  
Mohamed El Beqqal ◽  
Mostafa Azizi ◽  
Jean Louis Lanet
Keyword(s):  
Large Scale ◽  
Facial Recognition ◽  
Recognition Rate ◽  
Error Rates ◽  
Identification System ◽  
Effective Measure ◽  
Acceptance Ratio ◽  
Performance Requirements ◽  
Biometric Systems ◽  
Access Control System

<span>Monomodal biometry does not constitute an effective measure to meet the desired performance requirements for large-scale applications, due to limita-tions such as noisy data, restricted degree of freedom and unacceptable error rates. Some of these problems can be solved through multimodal biometric systems that involve using a combination of two or more biometric modali-ties in a single identification system. Identification based on multiple biomet-rics represents an emerging trend. The reason for combining different modal-ities is to improve the recognition rate. In practice, multi-biometric aims to reduce the False Acceptance Ratio (FAR) and False Rejection Ratio (FRR) which are two standard metrics widely used in the accuracy of biometric sys-tems. In this paper, we will examine the different possible scenario in multi-modal biometric systems using RFID, fingerprint and facial recognition, that can be adopted to merge information and improve the overall accuracy of the system.</span>

scholarly journals Building a Macro-mixing Dual-basin Go Model using the Multistate Bennett Acceptance Ratio

2020 ◽  
Vol 118 (3) ◽  
pp. 179a
Author(s):  
Ai Shinobu ◽  
Chigusa Kobayashi ◽  
Yasuhiro Matsunaga ◽  
Yuji Sugita
Keyword(s):  
Acceptance Ratio ◽  
Bennett Acceptance Ratio ◽  
Go Model

BAR-based optimum adaptive sampling regime for variance minimization in alchemical transformation: the nonequilibrium stratification

2018 ◽  
Vol 20 (3) ◽  
pp. 2009-2021 ◽  
Author(s):  
Xiaohui Wang ◽  
Xingzhao Tu ◽  
John Z. H. Zhang ◽  
Zhaoxi Sun
Keyword(s):  
Adaptive Sampling ◽  
Current Work ◽  
Variance Minimization ◽  
Acceptance Ratio ◽  
Bennett Acceptance Ratio ◽  
Sampling Regime

Following the previously proposed equilibrate-state sampling based adaptive sampling regime Optimum Bennett Acceptance Ratio (OBAR), we introduce its nonequilibrium extension, Optimum Crooks’ Equation (OCE) in the current work.

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