19,706 research outputs found
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
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