From Anecdote to Evidence: Assessing the Status and Condition of Arts Education at the State Level

Without solid evidence about the status and condition of arts education in the nation's public schools, it is difficult to make a convincing case for the arts. This research and policy brief draws on the experiences of five states -- Illinois, Kentucky, New Jersey, Rhode Island, and Washington -- as the basis for a discussion of various approaches and methodologies for conducting statewide arts education research

What to learn from dilepton transverse momentum spectra in heavy-ion collisions?

Recently the NA60 collaboration has presented high precision measurements of dimuon spectra double differential in invariant mass $M$ and transverse pair momentum $p_T$ in In-In collisions at $158 {\rm AGeV}$. While the $M$-dependence is important for an understanding of in-medium changes of light vector mesons and is $p_T$ integrated insensitive to collective expansion, the $p_T$-dependence arises from an interplay between emission temperature and collective transverse flow. This fact can be exploited to derive constraints on the evolution model and in particular on the contributions of different phases of the evolution to dimuon radiation into a given $M$ window. We present arguments that a thermalized evolution phase with $T > 170 {\rm MeV}$ leaves its imprint on the spectra.Comment: Contributed to 19th International Conference on Ultrarelativistic Nucleus-Nucleus Collisions: Quark Matter 2006 (QM 2006), Shanghai, China, 14- 20 Nov 200

A Bayesian Multivariate Functional Dynamic Linear Model

We present a Bayesian approach for modeling multivariate, dependent functional data. To account for the three dominant structural features in the data--functional, time dependent, and multivariate components--we extend hierarchical dynamic linear models for multivariate time series to the functional data setting. We also develop Bayesian spline theory in a more general constrained optimization framework. The proposed methods identify a time-invariant functional basis for the functional observations, which is smooth and interpretable, and can be made common across multivariate observations for additional information sharing. The Bayesian framework permits joint estimation of the model parameters, provides exact inference (up to MCMC error) on specific parameters, and allows generalized dependence structures. Sampling from the posterior distribution is accomplished with an efficient Gibbs sampling algorithm. We illustrate the proposed framework with two applications: (1) multi-economy yield curve data from the recent global recession, and (2) local field potential brain signals in rats, for which we develop a multivariate functional time series approach for multivariate time-frequency analysis. Supplementary materials, including R code and the multi-economy yield curve data, are available online

Critical Evidence: How the Arts Benefit Student Achievement

Examines the decline of funding for arts education in public schools. Describes how the study of specific art forms can advance math and reading comprehension, contributes to cognitive and social skills, and increases overall learning