We consider the block elimination problem Q A 1 A 2 = ?C 0 , where, given a matrix A 2 R mk , A 11 2 R kk , we try to nd a matrix C with C T C = A T A and an orthogonal matrix Q that eliminates A 2. Sun and Bischof recently showed that any orthogonal matrix can be represented in the so-called basis-kernel representation Q = Q(Y; S) = I ? Y ST T. Applying this framework to the block elimination ...

n≥0 where T denotes the shift on Ω. Conditioned on the particle having asymptotic mean velocity equal to any given ξ, we show that the empirical process of the environment Markov chain converges to a stationary process μ ξ under the averaged measure. When d ≥ 3 and ξ is sufficiently close to the typical velocity, we prove that averaged and quenched large deviations are equivalent and when condi...

In this paper we study two forms of blurring effects that may appear in the reconstruction of 3D Electron Microscopy (EM), specifically in single particle reconstruction from random orientations of large multi-unit biomolecular complexes. We model the blurring effects as being due to independent contributions from: (1) variations in the conformation of the biomolecular complex; and (2) errors a...

Abstract. In this article we consider the stochastic heat equation ut −∆u = Ḃ in (0, T )×Rd, with vanishing initial conditions, driven by a Gaussian noise Ḃ which is fractional in time, with Hurst index H ∈ (1/2, 1), and colored in space, with spatial covariance given by a function f . Our main result gives the necessary and sufficient condition on H for the existence of a solution. When f is t...

Viability theory is the study of dynamical systems that asks “what is the set of initial conditions that generate evolutions, which obey the laws of motion of a system plus other constraints, for the length of the evolution”. We apply viability theory to Judd’s (JPE, 1987) dynamic tax model to identify which economic states are viable, in that the application of annually constrained tax-rate ad...

We provide a new, analytic kernel for scale space filtering of dMRI data. The kernel is an approximation for the Green’s function of a hypo-elliptic diffusion on the 3D rigid body motion group SE(3), for fiber enhancement in dMRI. The enhancements are described by linear scale space PDEs in the coupled space of positions and orientations embedded in SE(3). As initial condition for the evolution...

We consider the joint distributions of particle positions for the continuous time totally asymmetric simple exclusion process (TASEP). They are expressed as Fredholm determinants with a kernel defining a signed determinantal point process. We then consider certain periodic initial conditions and determine the kernel in the scaling limit. This result has been announced first in a letter by one o...

We present an operating system-level security model for controlling ne-grained programs, such as downloaded executable content, and compare this security model's implementation to that of language-based security models. Language-based security has wellknown limitations, such as the lack of complete mediation (e.g., for compiled programs or race condition attacks) and faulty self-protection (e e...

In this paper a novel method is introduced based on the use of an unsupervised version of kernel least mean square (KLMS) algorithm for solving ordinary differential equations (ODEs). The algorithm is unsupervised because here no desired signal needs to be determined by user and the output of the model is generated by iterating the algorithm progressively. However, there are several new impleme...