create a practical set domain propagator that keeps all information (possibly exponential in size) about the set of possible set values for a set variable. In this paper we first show that we can use the same ROBDD approach to build an efficient bounds propagator. The main advantage of this approach

-secting, hypersurface flowing along its gradient field with con-stant speed or a speed that depends on the curvature. It is moved by solving a “Hamilton-Jacob? ’ type equation written for a func-tion in which the interface is a particular level set. A speed term synthesizpd from the image is used to stop the interface

to represent the generic knowledge of a domain expert, and it turns into a computational architecture if the links are used not merely for storing factual knowledge but also for directing and activating the data flow in the computations which manipulate this knowledge. The first part of the paper deals

use. In this paper we present new algorithmic techniques that substantially improve the running time of the belief propagation approach. One of our techniques reduces the complexity of the inference algorithm to be linear rather than quadratic in the number of possible labels for each pixel, which

Least fixpoints as meanings of recursive definitions.

We devise new numerical algorithms, called PSC algorithms, for following fronts propagating with curvature-dependent speed. The speed may be an arbitrary function of curvature, and the front can also be passively advected by an underlying flow. These algorithms approximate the equations of motion

applied to the interpretation of static images, in which the probability distribution of possible interpretations is represented by a randomly generated set. Condensation uses learned dynamical models, together with visual observations, to propagate the random set over time. The result is highly robust

Density Propagation over time. It uses `factored sampling', a method previously applied to interpretation of static images, in which the distribution of possible interpretations is represented by a randomly generated set of representatives. The Condensation algorithm combines factored sampling

inference scheme in a more general setting? We compare the marginals computed using loopy propagation to the exact ones in four Bayesian network architectures, including two real-world networks: ALARM and QMR. We find that the loopy beliefs often converge and when they do, they give a good

and the Internet class RR data formats (e.g., host addresses). Since the previous RFC set, several definitions have changed, so some previous definitions are obsolete. Experimental or obsolete features are clearly marked in these RFCs, and such information should be used with caution. The reader is especially