Motivated by the problem of identifying correlations between genes or features of two related biological systems, we propose a model of feature selection in which only a subset of the predictors Xt are dependent on the multidimensional variate Y, and the remainder of the predictors constitute a “noise set” Xu independent of Y. Using Monte Carlo simulations, we investigated the relative performance of two methods: thresholding and singular-value decomposition, in combination with stochastic optimization to determine “empirical bounds” on the small-sample accuracy of an asymptotic approximation. We demonstrate utility of the thresholding and SVD feature selection methods to with respect to a recent infant intestinal gene expression and metagenomics dataset.

This paper presents a vision of the near future in which computer interaction is characterized by natural face-to-face conversations with lifelike characters that speak, emote, and gesture. These animated agents will converse with people much like people converse effectively with assistants in a variety of focused applications. Despite the research advances required to realize this vision, and the lack of strong experimental evidence that animated agents improve human-computer interaction, we argue that initial prototypes of perceptive animated interfaces can be developed today, and that the resulting systems will provide more effective and engaging communication experiences than existing systems. In support of this hypothesis, we first describe initial experiments using an animated character to teach speech and language skills to children with hearing problems, and classroom subjects and social skills to children with autistic spectrum disorder. We then show how existing dialogue system architectures can be transformed into perceptive animated interfaces by integrating computer vision and animation capabilities. We conclude by describing the Colorado Literacy Tutor, a computer-based literacy program that provides an ideal testbed for research and development of perceptive animated interfaces, and consider next steps required to realize the vision.

The local similarity problem is to determine the similar regions within two given sequences. We recently developed a dynamic programming algorithm for the local similarity problem that requires only space proportional to the sum of the two sequence lengths, whereas earlier methods use space proportional to the product of the lengths. In this paper, we describe how to parallelize the new algorithm and present results of experimental studies on an Intel hypercube. The parallel method provides rapid, high-resolution alignments for users of our software toolkit for pairwise sequence comparison, as illustrated here by a comparison of the chloroplast genomes of tobacco and liverwort.