[This paper focuses on the]... task of [part-of-speech] disambiguation, and particularly on a new algorithm called VOLSUNGA, which avoids syntactic-level analysis, yields about 96% accuracy, and runs in far less time and space than previous attempts. The most recent previous algorithm runs in NP (Non-Polynomial) time, while VOLSUNGA runs in linear time. This is provably optimal; no improvements in the order of its execution time and space are possible. VOLSUNGA is also robust in cases of ungrammaticality. Improvements to this accuracy may be made, perhaps the most potentially significant being to include some higher-level information. With such additions, the accuracy of statistically-based algorithms will approach 100%; and the few remaining cases may be largely those with which humans also find difficulty. In subsequent sections we examine several disambiguation algorithms. Their techniques, accuracies, and efficiencies are analyzed. After presenting the research carried out to date, a discussion of VOLSUNGA's application to the Brown Corpus...

This paper presents a method for designing the illumination in an environment using optimization techniques applied to a radiosity based image synthesis system. An optimization of lighting parameters is performed based on user specified constraints and objectives for the illumination of the environment. The system solves for the "best" possible settings for: light source emissivities, element reflectivities, and spot light directionality parameters so that the design goals, suchastominimize energy or to give the the room an impression of privacy, are met. The system absorbs much of the burden for searching the design space allowing the user to focus on the goals of the illumination design rather than the intricate details of a complete lighting specification. A software implementation is described and some results of using the system are reported.

Abstract–A video-on-demand server must satisfy a large customer base and a diverse archive of movies under changing movie popularity and daily load peaks. These requirements must be satisfied under the constraints imposed by storage device costs, capacities, I/O bandwidths, and geographic locations. In this paper we describe a partitioning of video data (movies) onto a video-on-demand storage hierarchy to achieve efficient storage and I/O bandwidth utilization. Our approach uses a probabilistic model of movie popularity in data distribution and replication to balance user requests with available disk I/O bandwidth. The results can be applied in the design of a distributed video-on-demand system.