Vitiligo-like manifestations of graft-versus-host disease in a pediatric population

Vitiligo-like changes are an uncommon cutaneous manifestation of graft-versus-host disease (GVHD). We report three cases and review the literature of pediatric patients with vitiligo-like changes associated with GVHD. Improved characterization of this phenomenon may lend insight into the biologic pathways that underlie both vitiligo and GVHD

Variational inference formulation for a model-free simulation of a dynamical system with unknown parameters by a recurrent neural network

We propose a recurrent neural network for a "model-free" simulation of a dynamical system with unknown parameters without prior knowledge. The deep learning model aims to jointly learn the nonlinear time marching operator and the effects of the unknown parameters from a time series dataset. We assume that the time series data set consists of an ensemble of trajectories for a range of the parameters. The learning task is formulated as a statistical inference problem by considering the unknown parameters as random variables. A latent variable is introduced to model the effects of the unknown parameters, and a variational inference method is employed to simultaneously train probabilistic models for the time marching operator and an approximate posterior distribution for the latent variable. Unlike the classical variational inference, where a factorized distribution is used to approximate the posterior, we employ a feedforward neural network supplemented by an encoder recurrent neural network to develop a more flexible probabilistic model. The approximate posterior distribution makes an inference on a trajectory to identify the effects of the unknown parameters. The time marching operator is approximated by a recurrent neural network, which takes a latent state sampled from the approximate posterior distribution as one of the input variables, to compute the time evolution of the probability distribution conditioned on the latent variable. In the numerical experiments, it is shown that the proposed variational inference model makes a more accurate simulation compared to the standard recurrent neural networks. It is found that the proposed deep learning model is capable of correctly identifying the dimensions of the random parameters and learning a representation of complex time series data

Detecting a stochastic gravitational wave background with the Laser Interferometer Space Antenna

The random superposition of many weak sources will produce a stochastic background of gravitational waves that may dominate the response of the LISA (Laser Interferometer Space Antenna) gravitational wave observatory. Unless something can be done to distinguish between a stochastic background and detector noise, the two will combine to form an effective noise floor for the detector. Two methods have been proposed to solve this problem. The first is to cross-correlate the output of two independent interferometers. The second is an ingenious scheme for monitoring the instrument noise by operating LISA as a Sagnac interferometer. Here we derive the optimal orbital alignment for cross-correlating a pair of LISA detectors, and provide the first analytic derivation of the Sagnac sensitivity curve.Comment: 9 pages, 11 figures. Significant changes to the noise estimate

The Effects of Voluntary Disclosure and Dividend Propensity on Prices Leading Earnings

We investigate the joint effects of dividend propensity (i.e. whether a firm pays cash dividends) and voluntary disclosure on the relationship between current stock returns and future earnings. We examine whether dividend propensity and voluntary disclosure act as substitutes or complements in the financial communication process. We also examine whether the effects of dividend propensity and voluntary disclosure vary between high- and lowgrowth firms. Consistent with prior studies, we find that share price anticipation of earnings improves with increasing levels of annual report narrative disclosure, and that firms that pay dividends exhibit higher levels of share price anticipation of earnings than non-dividend-paying firms. The paper adds to the literature on share price anticipation of earnings in two crucial respects. First we show that the associations of voluntary disclosure and dividend propensity with share price anticipation of earnings are statistically significant for high-growth firms and insignificant for low-growth firms. Second we show that the significant effects we find for dividend propensity and voluntary disclosure in high-growth firms are not perfectly additive

Lateral Distribution of Muons in IceCube Cosmic Ray Events

In cosmic ray air showers, the muon lateral separation from the center of the shower is a measure of the transverse momentum that the muon parent acquired in the cosmic ray interaction. IceCube has observed cosmic ray interactions that produce muons laterally separated by up to 400 m from the shower core, a factor of 6 larger distance than previous measurements. These muons originate in high pT (> 2 GeV/c) interactions from the incident cosmic ray, or high-energy secondary interactions. The separation distribution shows a transition to a power law at large values, indicating the presence of a hard pT component that can be described by perturbative quantum chromodynamics. However, the rates and the zenith angle distributions of these events are not well reproduced with the cosmic ray models tested here, even those that include charm interactions. This discrepancy may be explained by a larger fraction of kaons and charmed particles than is currently incorporated in the simulations

Search for Relativistic Magnetic Monopoles with IceCube

We present the first results in the search for relativistic magnetic monopoles with the IceCube detector, a subsurface neutrino telescope located in the South Polar ice cap containing a volume of 1 km$^{3}$. This analysis searches data taken on the partially completed detector during 2007 when roughly 0.2 km$^{3}$ of ice was instrumented. The lack of candidate events leads to an upper limit on the flux of relativistic magnetic monopoles of \Phi_{\mathrm{90%C.L.}}\sim 3\e{-18}\fluxunits for $\beta\geq0.8$. This is a factor of 4 improvement over the previous best experimental flux limits up to a Lorentz boost $\gamma$ below $10^{7}$. This result is then interpreted for a wide range of mass and kinetic energy values.Comment: 11 pages, 11 figures. v2 is minor text edits, no changes to resul