Phonetic convergence in temporal organization during shadowed speech

The goal of this study was to examine phonetic convergence (when one imitates the phonetic characteristics of another talker) in various measures of temporal organization during shadowed speech across different American English dialects. Participants from the Northern and Midland American English dialect regions, plus several "mobile" talkers, were asked to read 72 sentences to establish a baseline for temporal organization, and then to repeat the same 72 sentences after Northern, Midland, and Southern model talkers. Measures of temporal organization (i.e., %V, ΔC, ΔV, rPVI-C, and nPVI-V) were calculated for the read sentences, shadowed sentences, and model talker sentences. Statistical analysis of the differences in distance between the model talker sentences and the shadowers' read and shadowed sentences, respectively, revealed significant convergence by all three shadowing groups toward the model dialects for ΔV, and significant divergence by Mobile talkers away from the model talkers for nPVI-V. Though the result of divergence by Mobile talkers was unexpected, both results provide evidence that support previous studies, which claim that social perception is a large contributing factor in convergence and divergence. These results are also consistent with previous findings demonstrating variation across dialects in temporal organization and, in addition, provide evidence for variation across dialects in convergence in temporal organization.The Ohio State University College of Arts and Sciences Undergraduate Research ScholarshipNo embargoAcademic Major: Linguistic

Are regional stages necessary?

The bipartition of the Carboniferous System adopted by the International Commission of Stratigraphy for the world stratigraphic chart is impractical in Gondwana. Correlation with thePaleoequatorialrealm, from the Late Viséan to the Early Permian inclusive, is n ot possible by paleontologic means because ofendemism ofthebiotaFil: Gonzalez, Carlos Roberto. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Fundación Miguel Lillo. Dirección de Geología; Argentin

Discontinuities in the level density of small quantum dots under strong magnetic fields

Exact diagonalization studies of the level density in a six-electron quantum dot under magnetic fields around 7 T (``filling factor'' around 1/2) are reported. In any spin-polarization channel, two regimes are visible in the dot excitation spectrum: one corresponding to interacting quasiparticles (i.e. composite fermions) for excitation energies below 0.4 meV, and a second one for energies above 0.4 meV, in which the level density (exponentially) increases at the same rate as in the non-interacting composite-fermion model.Comment: Accepted in Phys. Rev.

Data Augmentation in the Bayesian Multivariate Probit Model

This paper is concerned with the Bayesian estimation of a Multivariate Probit model. In particular, this paper provides an algorithm that obtains draws with low correlation much faster than a pure Gibbs sampling algorithm. The algorithm consists in sampling some characteristics of slope and variance parameters marginally on the latent data. Estimations with simulated datasets illustrate that the proposed algorithm can be much faster than a pure Gibbs sampling algorithm. For some datasets, the algorithm is also much faster than the e±cient algorithm proposed by Liu and Wu (1999) in the context of the univariate Probit model.

Bayesian model averaging in the instrumental variable regression model

This paper considers the instrumental variable regression model when there is uncertainty about the set of instruments, exogeneity restrictions, the validity of identifying restrictions and the set of exogenous regressors. This uncertainty can result in a huge number of models. To avoid statistical problems associated with standard model selection procedures, we develop a reversible jump Markov chain Monte Carlo algorithm that allows us to do Bayesian model averaging. The algorithm is very flexible and can be easily adapted to analyze any of the different priors that have been proposed in the Bayesian instrumental variables literature. We show how to calculate the probability of any relevant restriction (e.g. the posterior probability that over-identifying restrictions hold) and discuss diagnostic checking using the posterior distribution of discrepancy vectors. We illustrate our methods in a returns-to-schooling application

Polarized thermal emission from X-ray Dim Isolated Neutron Stars: the case of RX J1856.5-3754

The observed polarization properties of thermal radiation from isolated, cooling neutron stars depend on both the emission processes at the surface and the effects of the magnetized vacuum which surrounds the star. Here we investigate the polarized thermal emission from X-ray Dim Isolated Neutron Stars, taking RX J1856.5-3754 as a representative case. The physical conditions of the star outermost layers in these sources is still debated, and so we consider emission from a magnetized atmosphere and a condensed surface, accounting for the effects of vacuum polarization as the radiation propagates in the star magnetosphere. We have found that, for a significant range of viewing geometries, measurement of the phase-averaged polarization fraction and phase-averaged polarization angle at both optical and X-ray wavelengths allow us to determine whether this neutron star has an atmosphere or a condensed surface. Our results may therefore be relevant in view of future developments of soft X-ray polarimeters.Comment: 12 pages, 12 figures, accepted for publication in MNRA

Robust Benefit Function Transfer: A Bayesian Model Averaging Approach

A Benefit Function Transfer obtains estimates of Willingness-to-Pay (WTP) for the evaluation of a given policy at a site by combining existing information from different study sites. This has the advantage that more efficient estimates are obtained, but it relies on the assumption that the heterogeneity between sites is appropriately captured in the Benefit Transfer model. A more expensive alternative to estimate WTP is to analyse only data from the policy site in question while ignoring information from other sites. We make use of the fact that these two choices can be viewed as a model selection problem and extend the set of models to allow for the hypothesis that the benefit function is only applicable to a subset of sites. We show how Bayesian Model Averaging (BMA) techniques can be used to optimally combine information from all models. The Bayesian algorithm searches for the set of sites that can form the basis for estimating a Benefit function and reveals whether such information can be transferred to new sites for which only a small dataset is available. We illustrate the method with a sample of 42 forests from U.K. and Ireland. We find that BMA benefit function transfer produces reliable estimates and can increase about 8 times the information content of a small sample when the forest is ‘poolable’.Benefit Transfer; Bayesian Model Averaging; Exchangeability; Non-market Valuation; Panel Data