Recent algorithmic advances and continual increase in computational power have made it possible to simulate protein folding and dynamics on the level of ensembles. Furthermore, analyzing protein structure by using ensemble representation is intrinsic to certain experimental techniques, such as nuclear magnetic resonance. This creates a problem of how to compare an ensemble of molecules with a g...

Time-averaging restraints in molecular dynamics simulations were introduced to account for the averaging implicit in spectroscopic data. Space- or molecule-averaging restraints have been used to overcome the fact that not all molecular conformations can be visited during the finite time of a simulation of a single molecule. In this work we address the issue of using the correct Boltzmann weight...

Cellular heterogeneity and stochastic fluctuation play key roles in biological processes. Single molecule approaches have the key advantage of avoiding ensemble averaging, allowing the observation of transient intermediates and heterogeneity (both static and dynamic). Thus they have revolutionised the way many biological questions are addressed. The challenge ahead is to develop integrated appr...

Tree-based methods have become popular for analyzing complex data structures where the primary goal is risk stratification of patients. Ensemble techniques improve the accuracy in prediction and address the instability in a single tree by growing an ensemble of trees and aggregating. However, in the process, individual trees get lost. In this paper, we propose a methodology for identifying the ...

Boltzmann sampling based on Metropolis algorithm has been extensively used for simulating a canonical ensemble and for calculating macroscopic properties of a closed system at desired temperatures. An estimate of a mechanical property, like energy, of an equilibrium system, is made by averaging over a large number microstates generated by Boltzmann Monte Carlo methods. This is possible because ...

The visual system extracts average features from groups of objects (Ariely, 2001; Dakin & Watt, 1997; Watamaniuk & Sekuler, 1992), including high-level stimuli such as faces (Haberman & Whitney, 2007, 2009). This phenomenon, known as ensemble perception, implies a covert process, which would not require fixation of individual stimulus elements. However, some evidence suggests that ensemble perc...

in this paper the substantiation of the method of full averaging for fuzzy differential inclusions is considered. these results generalize the results of [17, 20] for differential inclusions with hukuhara derivative and of [18] for fuzzy differential equations.

Ensemble research has shown that the aggregated output of an ensemble of predictors can be more accurate than a single predictor. This is true also for lazy learning systems like Case-Based Reasoning (CBR) and k-NearestNeighbour. Aggregation is normally achieved by voting in classification tasks and by averaging in regression tasks. For CBR, this increased accuracy comes at the cost of interpre...