Several numerical schemes for the solution of hyperbolic conservation laws are based on exploiting the information obtained by considering a sequence of Riemann problems. It is argued that in existing schemes much of this information is degraded, and that only certain features of the exact solution

Several methods have been previously used to approximate free boundaries in finite difference numerical simulations. A simple, but powerful, method is described that is based on the concept of a fractional volume of fluid (VOF). This method is shown to be more flexible and efficient than other

The interests and incentives of managers and shareholders conflict over such issues as the optimal size of the firm and the payment of cash to shareholders. These conflicts are especially severe in firms with large free cash flows—more cash than profitable investment opportunities. The theory

. The EM algorithm performs maximum likelihood estimation for data in which some variables are unobserved. We present a function that resembles negative free energy and show that the M step maximizes this function with respect to the model parameters and the E step maximizes it with respect

algorithms. It is shown that the essential condition for distribution-free learnability is finiteness of the Vapnik-Chervonenkis dimension, a simple combinatorial parameter of the class of concepts to be learned. Using this parameter, the complexity and closure properties of learnable classes are analyzed

fixed effects and trend terms, and we consider both pooled within dimension tests and group mean between dimension tests+ We derive limiting distributions for these and show that they are normal and free of nuisance parameters+ We also provide Monte Carlo evidence to demonstrate their small sample size

derivatives with regard to the parameters can be approximated accurately and efficiently. Examples are presented of contrastive divergence learning using several types of expert on several types of data.

, for assessing convergence of several parameters simultaneously.

Abstract Several common systems satisfy some but not all of the VR This paper describes the CAVE (CAVE Automatic Virtual Environment) virtual reality/scientific visualization system in detail and demonstrates that projection technology applied to virtual-reality goals achieves a system that matches

We present conditional random fields, a framework for building probabilistic models to segment and label sequence data. Conditional random fields offer several advantages over hidden Markov models and stochastic grammars for such tasks, including the ability to relax strong independence assumptions