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Abstract—This paper explores the recent explosion in university patenting as a source of insight into the changing relationship between the university and the private sector. Before the mid-1980s, university patents were more highly cited, and were cited by more diverse patents, than a random sample of all patents. More recently several significant shifts in university patenting behavior have led to the disappearance of this difference. Thus our results suggest that between 1965 and 1988 the rate of increase of important patents from universities was much less than their overall rate of increase of patenting. I.

In this book we aim to present, in a unified framework, a broad spectrum of mathematical theory that has grown in connection with the study of problems of optimization, equilibrium, control, and stability of linear and nonlinear systems. The title Variational Analysis reflects this breadth. For a

In this paper, we present a detailed analysis of the MediaBench benchmark suite. MediaBench consists of a number of popular embedded applications for communications and multimedia. MediaBench performance characteristics were examined by running MediaBench under the SimpleScalar simulation

Principal component analysis (PCA) is a ubiquitous technique for data analysis and processing, but one which is not based upon a probability model. In this paper we demonstrate how the principal axes of a set of observed data vectors may be determined through maximum-likelihood estimation

Probabilistic Latent Semantic Analysis is a novel statistical technique for the analysis of two--mode and co-occurrence data, which has applications in information retrieval and filtering, natural language processing, machine learning from text, and in related areas. Compared to standard Latent

There have been various developments in shape analysis in the last decade. We describe here some relationships of shape analysis with directional statistics. For shape, rotations are to be integrated out or to be optimized over whilst they are the basis for directional statistics. However, various

A common problem encountered in such disciplines as statistics, data analysis, signal processing, and neural network research, is nding a suitable representation of multivariate data. For computational and conceptual simplicity, such a representation is often sought as a linear transformation

. Multiresolution analysis offers a simple, unified, and theoretically sound approach to dealing with these problems. Lounsbery et al. have recently developed a technique for creating multiresolution representations for a restricted class of meshes with subdivision connectivity. Unfortunately, meshes encountered

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