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

Diversity and critical behavior in prisoner's dilemma game

The prisoner's dilemma (PD) game is a simple model for understanding cooperative patterns in complex systems consisting of selfish individuals. Here, we study a PD game problem in scale-free networks containing hierarchically organized modules and controllable shortcuts connecting separated hubs. We find that cooperator clusters exhibit a percolation transition in the parameter space (p,b), where p is the occupation probability of shortcuts and b is the temptation payoff in the PD game. The cluster size distribution follows a power law at the transition point. Such a critical behavior, resulting from the combined effect of stochastic processes in the PD game and the heterogeneous structure of complex networks, illustrates the diversity of social relationships and the self-organization of cooperator communities in real-world systems

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

The Impact of Monetary Policy Shocks on Stock Prices: Evidence from Canada and the United States

monetary policy shocks; stock prices; open economy; structural vector autoregressive model

Cluster Formation in Protostellar Outflow-Driven Turbulence

Most, perhaps all, stars go through a phase of vigorous outflow during formation. We examine, through 3D MHD simulation, the effects of protostellar outflows on cluster formation. We find that the initial turbulence in the cluster-forming region is quickly replaced by motions generated by outflows. The protostellar outflow-driven turbulence (``protostellar turbulence'' for short) can keep the region close to a virial equilibrium long after the initial turbulence has decayed away. We argue that there exist two types of turbulence in star-forming clouds: a primordial (or ``interstellar'') turbulence and a protostellar turbulence, with the former transformed into the latter mostly in embedded clusters such as NGC 1333. Since the majority of stars are thought to form in clusters, an implication is that the stellar initial mass function is determined to a large extent by the stars themselves, through outflows which individually limit the mass accretion onto forming stars and collectively shape the environments (density structure and velocity field) in which most cluster members form. We speculate that massive cluster-forming clumps supported by protostellar turbulence gradually evolve towards a highly centrally condensed ``pivotal'' state, culminating in rapid formation of massive stars in the densest part through accretion.Comment: 11 pages (aastex format), 2 figures submitted to ApJ