Do Soup Kitchen Meals Contribute to Suboptimal Nutrient Intake & Obesity in the Homeless Population?

The double burden of suboptimal nutrient intake and obesity exists when available foods lack essential nutrients to promote health and provide high amounts of energy. This study evaluated the nutrition content of 41 meals served to the homeless at 3 urban soup kitchens. The mean nutrient content of all meals and of meals from each of the kitchens was compared to two-thirds of the estimated average requirement (EAR). The mean nutrient content of the meals did not provide two-thirds of the EAR for energy, vitamin C, magnesium, zinc, dietary fiber, or calcium but provided 11.8% of calories from saturated fat. On average one meal did not meet homeless individuals’ estimated requirements; however, 2 meals did meet estimated requirements but provided inadequate fiber and high amounts of energy, saturated fat, and sodium. Soup kitchen meals may contribute to the high prevalence of obesity and chronic disease reported in the homeless, food insecure population

A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation

Approximate Bayesian computation (ABC) methods make use of comparisons between simulated and observed summary statistics to overcome the problem of computationally intractable likelihood functions. As the practical implementation of ABC requires computations based on vectors of summary statistics, rather than full data sets, a central question is how to derive low-dimensional summary statistics from the observed data with minimal loss of information. In this article we provide a comprehensive review and comparison of the performance of the principal methods of dimension reduction proposed in the ABC literature. The methods are split into three nonmutually exclusive classes consisting of best subset selection methods, projection techniques and regularization. In addition, we introduce two new methods of dimension reduction. The first is a best subset selection method based on Akaike and Bayesian information criteria, and the second uses ridge regression as a regularization procedure. We illustrate the performance of these dimension reduction techniques through the analysis of three challenging models and data sets.Comment: Published in at http://dx.doi.org/10.1214/12-STS406 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Specific Heat of Zn-Doped YBa_{2}Cu_3O_{6.95}: Possible Evidence for Kondo Screening in the Superconducting State

The magnetic field dependence of the specific heat of Zn-doped single crystals of YBa_{2}Cu_3O_{6.95} was measured between 2 and 10 K and up to 8 Tesla. Doping levels of 0, 0.15%, 0.31%, and 1% were studied and compared. In particular we searched for the Schottky anomaly associated with the Zn-induced magnetic moments.Comment: 5 pages, 6 figure

Persistence of Li Induced Kondo Moments in the Superconducting State of Cuprates

We measure the magnetic susceptibility nearby Li spinless impurities in the superconducting phase of the high Tc cuprate YBaCuO. The induced moment which was found to exist above Tc persists below Tc. In the underdoped regime, it retains its Curie law below Tc. In contrast, near optimal doping, the large Kondo screening observed above Tc (T_K=135 K) is strongly reduced below Tc as expected theoretically when the superconducting gap develops. This moment still extends essentially on its 4 near neighbour Cu, showing the persistence of AF correlations in the superconducting state. A direct comparison with recent STM results of Pan et al. is proposed.Comment: accepted for publication in Phys. Rev. Lett. (issue of 30 april 2001) Revised version : 8 pages including 4 pages of text and 4 figure

Thermal Conductivity of the Spin Peierls Compound CuGeO_3

The thermal conductivity of the Spin-Peierls (SP) compound CuGeO_3 was measured in magnetic fields up to 16 T. Above the SP transition, the heat transport due to spin excitations causes a peak at around 22 K, while below the transition the spin excitations rapidly diminish and the heat transport is dominated by phonons; however, the main scattering process of the phonons is with spin excitations, which demonstrates itself in an unusual peak in the thermal conductivity at about 5.5 K. This low-temperature peak is strongly suppressed with magnetic fields in excess of 12.5 T.Comment: 6 pages, including 2 postscript figure

Numerical Renormalization Group Study of Kondo Effect in Unconventional Superconductors

Orbital degrees of freedom of a Cooper pair play an important role in the unconventional superconductivity. To elucidate the orbital effect in the Kondo problem, we investigated a single magnetic impurity coupled to Cooper pairs with a $p_x +i p_y$ ($d_{x^2-y^2}+id_{xy}$) symmetry using the numerical renormalization group method. It is found that the ground state is always a spin doublet. The analytical solution for the strong coupling limit explicitly shows that the orbital dynamics of the Cooper pair generates the spin 1/2 of the ground state.Comment: 4 pages, 2 figures, JPSJ.sty, to be published in J. Phys. Soc. Jpn. 70 (2001) No. 1

Kondo screening in d-wave superconductors in a Zeeman field and implications for STM spectra of Zn-doped cuprates

We consider the screening of an impurity moment in a d-wave superconductor under the influence of a Zeeman magnetic field. Using the Numerical Renormalization Group technique, we investigate the resulting pseudogap Kondo problem, in particular the field-induced crossover behavior in the vicinity of the zero-field boundary quantum phase transition. The impurity spectral function and the resulting changes in the local host density of states are calculated, giving specific predictions for high-field STM measurements on impurity-doped cuprates.Comment: 5 pages, 4 figs, (v2) remark on c-axis field added, discussion extended, (v3) final version as publishe

Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.Comment: 26 pages, 2 figures, accepted in Journal of Computational and Graphical Statistics (http://www.amstat.org/publications/jcgs.cfm

Non-linear regression models for Approximate Bayesian Computation

Approximate Bayesian inference on the basis of summary statistics is well-suited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior density by introducing two innovations. The new method fits a nonlinear conditional heteroscedastic regression of the parameter on the summary statistics, and then adaptively improves estimation using importance sampling. The new algorithm is compared to the state-of-the-art approximate Bayesian methods, and achieves considerable reduction of the computational burden in two examples of inference in statistical genetics and in a queueing model.Comment: 4 figures; version 3 minor changes; to appear in Statistics and Computin