deterministic interpretation of the Kalman #ltering formulas is given, using theprinc#RB of least squares estimation. The observed signal and the to-be-estimated signal are modeled as being generated as outputs of a #nite-dimensional linear system driven by an input disturbanc, Postulating

We look at a 1-membrane catalytic P system with evolution rules of the form Ca → Cv or a → v, where C is a catalyst, a is a noncatalyst symbol, and v is a (possibly null) string representing a multiset of noncatalyst symbols. (Note that we are only interested in the multiplicities of the symbols

Bugs due to data races in multithreaded programs often exhibit non-deterministic symptoms and are notoriously difficult to find. This paper describes RaceTrack, a dynamic race detection tool that tracks the actions of a program and reports a warning whenever a suspicious pattern of activity has

Abstract — This paper deals with value function and Q-function approximation in deterministic Markovian decision processes. A general statistical framework based on the Kalman filtering paradigm is introduced. Its principle is to adopt a parametric representation of the value function, to model

Consider an n-vertex graph G = (V,E) of maximum degree ∆, and suppose that each vertex v ∈ V hosts a processor. The processors are allowed to communicate only with their neighbors in G. The communication is synchronous, i.e., it proceeds in discrete rounds. In the distributed vertex coloring

We present a deterministic algorithm that given a tree T with n vertices, a starting vertex v and a slackness parameter ǫ> 0, estimates within an additive error of ǫ the cover and return time, namely, the expected time it takes a simple random walk that starts at v to visit all vertices

for chaos, the role of molecular diffusion being taken by additive noise; we extensively use this analogy below. The evolution of a passive scalar density φ(x, y, t) in a two-dimensional incompressible flow V (x, y, t) is described by the equation ∂φ + V (x,t) ·∇φ = D∆φ, (1)

Summary. Based on concepts introduced in Preliminaries For simplicity, we adopt the following convention: x, y, X denote sets, E denotes a non empty set, e denotes an element of E, u, u 1 , v, v 1 , v 2 , w denote elements of E ω , F denotes a subset of E ω , i, k, l denote natural numbers

to approximate the optimal value function V :\Omega !<F12.3

We prove old and new results on the complexity of computing the dimension of algebraic varieties. In particular, we show that this problem is NP-complete in the Blum-Shub-Smale model of computation over C , that it admits a s O(1) D O(n) deterministic algorithm, and that for systems