Recently, we developed a method (Amadei et al., J. Biomol. Str. Dyn. 13: 615-626; de Groot et al., J. Biomol. Str. Dyn. 13: 741-751, 1996) to obtain an extended sampling of the configurational space of proteins, using an adapted form of molecular dynamics (MD) simulations, based on the essential dynamics (ED) (Amadei et al., Proteins 17:412-425, 1993) method. In the present study, this ED sampl...

Studies of populations such as drug users encounter difficulties because the members of the populations are rare, hidden, or hard to reach. Conventionally designed large-scale surveys detect relatively few members of the populations so that estimates of population characteristics have high uncertainty. Ethnographic studies, on the other hand, reach suitable numbers of individuals only through t...

We examine cluster-series methods for the behavior of simple fluids under confinement, applying the Mayer-sampling Monte Carlo method to evaluate the necessary cluster integrals. We examine two series formulations, one based on the density of a bulk phase in equilibrium with the confined phase (effectively specifying the chemical potential), and another based on the density of the confined phas...

The most important factor of classification for improving classification accuracy is the training data. However, the data in real-world applications often are imbalanced class distribution, that is, most of the data are in majority class and little data are in minority class. In this case, if all the data are used to be the training data, the classifier tends to predict that most of the incomin...

Molecular dynamics (MD) simulations offer a way to explore the conformational state space of large, biologically relevant molecules. Our sampling method, called “concurrent adaptive sampling” (CAS), utilizes MD simulations by letting a number of “walkers” adaptively explore the state space in consecutive steps. Walkers start in one conformational state, execute a short MD simulation and thus en...

This paper presents a strategy for applying sampling techniques to relational databases, in the context of data quality auditing or decision support processes. Fuzzy cluster sampling is used to survey sets of records for correctness of business rules. Relational algebra estimators are presented as a data quality-auditing tool.

Virtual move Monte Carlo is a cluster algorithm which was originally developed for strongly attractive colloidal, molecular, or atomistic systems in order to both approximate the collective dynamics and avoid sampling of unphysical kinetic traps. In this paper, we present the algorithm in the form, which selects the moving cluster through a wider class of virtual states and which is applicable ...

Matousek [Discrete Comput. Geom. 24 (1) (2000) 61–84] designed an O(nlogn) deterministic algorithm for the approximate 2-means clustering problem for points in fixed dimensional Euclidean space which had left open the possibility of a linear time algorithm. In this paper, we present a simple randomized algorithm to determine an approximate 2-means clustering of a given set of points in fixed di...

A two-term Edgeworth expansion for the standardized version of the sample total in a two-stage sampling design is derived. In particular, for the commonly used stratified and cluster sampling schemes, formal two-term asymptotic expansions are obtained for the Studentized versions of the sample total. These results are applied in conjunction with the bootstrap to construct more accurate confiden...

We present a novel condition, which we term the network nullspace property, which ensures accurate recovery of graph signals representing massive network-structured datasets from few signal values. The network nullspace property couples the cluster structure of the underlying network-structure with the geometry of the sampling set. Our results can be used to design efficient sampling strategies...