Exact solution to the mean exit time problem for free inertial processes driven by Gaussian white noise

We obtain the exact analytical expression, up to a quadrature, for the mean exit time, T(x,v), of a free inertial process driven by Gaussian white noise from a region (0,L) in space. We obtain a completely explicit expression for T(x,0) and discuss the dependence of T(x,v) as a function of the size L of the region. We develop a new method that may be used to solve other exit time problems.

Bistability driven by white shot noise

We consider mean-first-passage times and transition rates in bistable systems driven by white shot noise. We obtain closed analytical expressions, asymptotic approximations, and numerical simulations in two cases of interest: (i) jumps sizes exponentially distributed and (ii) jumps of the same size.

The White Cane Magazine, Winter 2011, Vol. 2, no. 1

Quarterly newsletter produced by the Iowa Department of Blind, about the information and activities that are on going in the department.

The White Cane Magazine, Summer 2011, Vol. 2, no. 3

Quarterly newsletter produced by the Iowa Department of Blind, about the information and activities that are on going in the department.

The White Cane Magazine, Fall 2011, Vol. 2, no. 4

Quarterly newsletter produced by the Iowa Department of Blind, about the information and activities that are on going in the department.

The White Cane Magazine, Winter 2012, Vol. 3, no. 1

Quarterly newsletter produced by the Iowa Department of Blind, about the information and activities that are on going in the department.

The White Cane Magazine, Spring 2012, Vol. 3, no. 2

Quarterly newsletter produced by the Iowa Department of Blind, about the information and activities that are on going in the department.

Exact solution to the exit-time problem for an undamped free particle driven by Gaussian white noise

In a recent paper [Phys. Rev. Lett. 75, 189 (1995)] we have presented the exact analytical expression for the mean exit time, T(x,v), of a free inertial process driven by Gaussian white noise out of a region (0,L) in space. In this paper we give a detailed account of the method employed and present results on asymptotic properties and averages of T(x,v).

Class of correlated random networks with hidden variables

We study a class of models of correlated random networks in which vertices are characterized by hidden variables controlling the establishment of edges between pairs of vertices. We find analytical expressions for the main topological properties of these models as a function of the distribution of hidden variables and the probability of connecting vertices. The expressions obtained are checked by means of numerical simulations in a particular example. The general model is extended to describe a practical algorithm to generate random networks with an a priori specified correlation structure. We also present an extension of the class, to map nonequilibrium growing networks to networks with hidden variables that represent the time at which each vertex was introduced in the system.

White-Tailed Deer(Odocoileus virginianus), 2011

Data sheet produced by the Iowa Department of Natural Resources is about different times of animals, insects, snakes, birds, fish, butterflies, etc. that can be found in Iowa.

White Tailed Jack Rabbit, 2011

Data sheet produced by the Iowa Department of Natural Resources is about different times of animals, insects, snakes, birds, fish, butterflies, etc. that can be found in Iowa.

On a class of exact solutions to the Fokker-Planck equations

In this paper we study under which circumstances there exists a general change of gross variables that transforms any FokkerPlanck equation into another of the OrnsteinUhlenbeck class that, therefore, has an exact solution. We find that any FokkerPlanck equation will be exactly solvable by means of a change of gross variables if and only if the curvature tensor and the torsion tensor associated with the diffusion is zero and the transformed drift is linear. We apply our criteria to the Kubo and Gompertz models.

Rgtsp: a generalized top scoring pairs package for class prediction.

SUMMARY: A top scoring pair (TSP) classifier consists of a pair of variables whose relative ordering can be used for accurately predicting the class label of a sample. This classification rule has the advantage of being easily interpretable and more robust against technical variations in data, as those due to different microarray platforms. Here we describe a parallel implementation of this classifier which significantly reduces the training time, and a number of extensions, including a multi-class approach, which has the potential of improving the classification performance. AVAILABILITY AND IMPLEMENTATION: Full C++ source code and R package Rgtsp are freely available from http://lausanne.isb-sib.ch/~vpopovic/research/. The implementation relies on existing OpenMP libraries.

Your Iowa ... and nation : class room guide post,1963.

Contains information to be used by teachers in classrooms including history of the the United States flag, the government of Iowa, the Constitution of Iowa, the birth of Americanism, the Declaration of Independence, the Constitution of the United States, Lincoln's Gettysburg Address.