Nonparametric Bayesian sparse factor models with application to gene expression modeling

A nonparametric Bayesian extension of Factor Analysis (FA) is proposed where observed data $\mathbf{Y}$ is modeled as a linear superposition, $\mathbf{G}$, of a potentially infinite number of hidden factors, $\mathbf{X}$. The Indian Buffet Process (IBP) is used as a prior on $\mathbf{G}$ to incorporate sparsity and to allow the number of latent features to be inferred. The model's utility for modeling gene expression data is investigated using randomly generated data sets based on a known sparse connectivity matrix for E. Coli, and on three biological data sets of increasing complexity.

Unsteady transition in an axial-flow turbine. Part 2. Cascade measurements and modeling

Part 1 of this paper reanalyzed previously published measurements from the rotor of a low-speed, single-stage, axial-flow turbine, which highlighted the unsteady nature of the suction surface transition process. Part 2 investigates the significance of the wake jet and the unsteady frequency parameter. Supporting experiments carried out in a linear cascade with varying inlet turbulence are described, together with a simple unsteady transition model explaining the features of seen in the turbine.