University of Texas at Austin

About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.

Two-photon luminescence imaging of cancer cells using molecularly targeted gold nanorods.

Abstract: We demonstrate the use of gold nanorods as bright contrast agents for two-photon luminescence (TPL) imaging of cancer cells in a three-dimensional tissue phantom down to 75 μm deep. The TPL intensity from gold-nanorod-labeled cancer cells is 3 orders of magnitude brighter than the two-photon autofluorescence (TPAF) emission intensity from unlabeled cancer cells at 760 nm excitation light. Their strong signal, resistance to photobleaching, chemical stability, ease of synthesis, simplicity of conjugation chemistry, and biocompatibility make gold nanorods an attractive contrast agent for two-photon imaging of epithelial cancer.

On Theories Explaining the Success of the Gravity Equation

Abstract: Examining the accuracy of the monopolistic competition theory's predictions for import volumes, we assess whether this theory accounts for the empirical success of the gravity equation Since certain factor-endowment based theories have the same prediction for import volumes, we employ resampling techniques to address this model identification problem We use extraneous information on the allocation of factor endowments in a given sample to identify which model is driving trade flows We find that the accuracy of the monopolistic competition theory's prediction improves in samples where the factor endowment allocations generate a higher share of differentiated goods trade By an analogous criterion, the Heckscher-Ohlin models make a much less accurate prediction We conclude that the monopolistic competition theory is more likely to account for the gravity equation's success, especially in explaining trade among industrial nations

Clustering on the Unit Hypersphere using von Mises-Fisher Distributions

Abstract: Several large scale data mining applications, such as text categorization and gene expression analysis, involve high-dimensional data that is also inherently directional in nature. Often such data is L2 normalized so that it lies on the surface of a unit hypersphere. Popular models such as (mixtures of) multi-variate Gaussians are inadequate for characterizing such data. This paper proposes a generative mixture-model approach to clustering directional data based on the von Mises-Fisher (vMF) distribution, which arises naturally for data distributed on the unit hypersphere. In particular, we derive and analyze two variants of the Expectation Maximization (EM) framework for estimating the mean and concentration parameters of this mixture. Numerical estimation of the concentration parameters is non-trivial in high dimensions since it involves functional inversion of ratios of Bessel functions. We also formulate two clustering algorithms corresponding to the variants of EM that we derive. Our approach provides a theoretical basis for the use of cosine similarity that has been widely employed by the information retrieval community, and obtains the spherical kmeans algorithm (kmeans with cosine similarity) as a special case of both variants. Empirical results on clustering of high-dimensional text and gene-expression data based on a mixture of vMF distributions show that the ability to estimate the concentration parameter for each vMF component, which is not present in existing approaches, yields superior results, especially for difficult clustering tasks in high-dimensional spaces.