Bayesian Conditional Tensor Factorizations for High-Dimensional Classification

In many application areas, data are collected on a categorical response and high-dimensional categorical predictors, with the goals being to build a parsimonious model for classification while doing inferences on the important predictors. In settings such as genomics, there can be complex interactions among the predictors. By using a carefully-structured Tucker factorization, we define a model that can characterize any conditional probability, while facilitating variable selection and modeling of higher-order interactions. Following a Bayesian approach, we propose a Markov chain Monte Carlo algorithm for posterior computation accommodating uncertainty in the predictors to be included. Under near sparsity assumptions, the posterior distribution for the conditional probability is shown to achieve close to the parametric rate of contraction even in ultra high-dimensional settings. The methods are illustrated using simulation examples and biomedical applications

Non-Supersymmetric Attractors in BI black holes

We study attractor mechanism in extremal black holes of Einstein-Born-Infeld theories in four dimensions. We look for solutions which are regular near the horizon and show that they exist and enjoy the attractor behavior. The attractor point is determined by extremization of the effective potential at the horizon. This analysis includes the backreaction and supports the validity of non-supersymmetric attractors in the presence of higher derivative interactions in the gauge field part.Comment: 15 pages, minor corrections, references adde

Magnetically Regulated Star Formation in Turbulent Clouds

We investigate numerically the combined effects of supersonic turbulence, strong magnetic fields and ambipolar diffusion on cloud evolution leading to star formation. We find that, in clouds that are initially magnetically subcritical, supersonic turbulence can speed up star formation, through enhanced ambipolar diffusion in shocks. The speedup overcomes a major objection to the standard scenario of low-mass star formation involving ambipolar diffusion, since the diffusion time scale at the average density of a molecular cloud is typically longer than the cloud life time. At the same time, the strong magnetic field can prevent the large-scale supersonic turbulence from converting most of the cloud mass into stars in one (short) turbulence crossing time, and thus alleviate the high efficiency problem associated with the turbulence-controlled picture for low-mass star formation. We propose that relatively rapid but inefficient star formation results from supersonic collisions of somewhat subcritical gas in strongly magnetized, turbulent clouds. The salient features of this shock-accelerated, ambipolar diffusion-regulated scenario are demonstrated with numerical experiments.Comment: 10 pages, 3 figures, accepted for publication in ApJ

The In-Hospital Mortality Rates of Slaves and Freemen: Evidence from Touro Infirmary, New Orleans, Louisiana, 1855–1860

Using a rich sample of admission records from New Orleans Touro Infirmary, we examine the in-hospital mortality risk of free and enslaved patients. Despite a higher mortality rate in the general population, slaves were significantly less likely to die in the hospital than the whites. We analyze the determinants of in-hospital mortality at Touro using Oaxaca-type decomposition to aggregate our regression results. After controlling for differences in characteristics and maladies, we find that much of the mortality gap remains unexplained. In conclusion, we propose an alternative explanation for the mortality gap based on the selective hospital admission of slaves.hospital, slavery, Oaxaca-type decomposition, New Orleans, Touro