Why are very short times so long and very long times so short in elastic waves?

In a first study of thermoelastic waves, such as on the textbook of Landau and Lifshitz, one might at first glance understand that when the given period is very short, waves are isentropic because heat conduction does not set in, while if the given period is very long waves are isothermal because there is enough time for thermalization to be thoroughly accomplished. When one pursues the study of these waves further, by the mathematical inspection of the complete thermoelastic wave equation he finds that if the period is very short, much shorter than a characteristic time of the material, the wave is isothermal, while if it is very long, much longer than the characteristic time, the wave is isentropic. One also learns that this fact is supported by experiments: at low frequencies the elastic waves are isentropic, while they are isothermal when the frequencies are so high that can be attained in few cases. The authors show that there is no contradiction between the first glance understanding and the mathematical treatment of the elastic wave equation: for thermal effects very long periods are so short and very short periods are so long.Comment: 7 pages, submitted to European Journal of Physic

Signatures of Discontinuity in the Exchange-Correlation Energy Functional Derived from the Subband Electronic Structure of Semiconductor Quantum Wells

The discontinuous character of the exact exchange-correlation $(xc)$ energy functional of Density Functional Theory is shown to arise naturally in the subband spectra of semiconductor quantum wells. Using an \emph{ab-initio} $xc$ functional, including exchange exactly and correlation in an exact partial way, a discontinuity appears in the $xc$ potential, each time a subband becomes slightly occupied. Exchange and correlation give opposite contributions to the discontinuity, with correlation overcoming exchange. The jump in the intersubband energy is in excellent agreement with experimental data.Comment: 5 pages, 3 figure

Magnetic correlations in La(2-x)Sr(x)CuO4 from NQR relaxation and specific heat

La-139 and Cu-63 Nuclear Quadrupole Resonance (NQR) relaxation measurements in La(2-x)Sr(x)CuO4 for O = to or less than 0.3 and in the temperature range 1.6 + 450 K are analyzed in terms of Cu(++) magnetic correlations and dynamics. It is described how the magnetic correlations that would result from Cu-Cu exchange are reduced by mobile charge defects related to x-doping. A comprehensive picture is given which explains satisfactorily the x and T dependence of the correlation time, of the correlation length and of the Neel temperature T(sub n)(x) as well as being consistent with known electrical resistivity and magnetic susceptibility measurements. It is discussed how, in the superconducting samples, the mobile defects also cause the decrease, for T yields T(sub c)(+) of the hyperfine Cu electron-nucleus effective interaction, leading to the coexistence of quasi-localized, reduced magnetic moments from 3d Cu electrons and mobile oxygen p-hole carriers. The temperature dependence of the effective hyperfine field around the superconducting transition yields an activation energy which could be related to the pairing energy. New specific heat measurements are also presented and discussed in terms of the above picture

Beyond KernelBoost

In this Technical Report we propose a set of improvements with respect to the KernelBoost classifier presented in [Becker et al., MICCAI 2013]. We start with a scheme inspired by Auto-Context, but that is suitable in situations where the lack of large training sets poses a potential problem of overfitting. The aim is to capture the interactions between neighboring image pixels to better regularize the boundaries of segmented regions. As in Auto-Context [Tu et al., PAMI 2009] the segmentation process is iterative and, at each iteration, the segmentation results for the previous iterations are taken into account in conjunction with the image itself. However, unlike in [Tu et al., PAMI 2009], we organize our recursion so that the classifiers can progressively focus on difficult-to-classify locations. This lets us exploit the power of the decision-tree paradigm while avoiding over-fitting. In the context of this architecture, KernelBoost represents a powerful building block due to its ability to learn on the score maps coming from previous iterations. We first introduce two important mechanisms to empower the KernelBoost classifier, namely pooling and the clustering of positive samples based on the appearance of the corresponding ground-truth. These operations significantly contribute to increase the effectiveness of the system on biomedical images, where texture plays a major role in the recognition of the different image components. We then present some other techniques that can be easily integrated in the KernelBoost framework to further improve the accuracy of the final segmentation. We show extensive results on different medical image datasets, including some multi-label tasks, on which our method is shown to outperform state-of-the-art approaches. The resulting segmentations display high accuracy, neat contours, and reduced noise

Predicting ground-state configurations and electronic properties of the thermoelectric clathrates Ba8_{8}Alx_{x}Si46−x_{46-x} and Sr8_{8}Alx_{x}Si46−x_{46-x}

The structural and electronic properties of the clathrate compounds Ba$_{8}$Al$_{x}$Si$_{46-x}$ and Sr$_{8}$Al$_{x}$Si$_{46-x}$ are studied from first principles, considering an Al content $x$ between 6 and 16. Due to the large number of possible substitutional configurations we make use of a special iterative cluster-expansion approach, to predict ground states and quasi-degenerate structures in a highly efficient way. These are found from a simulated annealing technique where millions of configurations are sampled. For both compounds, we find a linear increase of the lattice constant with the number of Al substituents, confirming experimental observations for Ba$_{8}$Al$_{x}$Si$_{46-x}$. Also the calculated bond distances between high-symmetry sites agree well with experiment for the full compositional range. For $x$ being below 16, all configurations are metallic for both materials. At the charge-balanced composition ($x=16$), the substitutional ordering leads to a metal-semiconductor transition, and the ground states of Ba$_{8}$Al$_{16}$Si$_{30}$ and Sr$_{8}$Al$_{16}$Si$_{30}$ exhibit indirect Kohn-Sham band gaps of 0.36 and 0.30 eV, respectively, while configurations higher in energy are metals. The finding of semiconducting behavior is a promising result in view of exploiting these materials in thermoelectric applications.Comment: 9 figure

Tension fatigue analysis and life prediction for composite laminates

A tension fatigue life prediction methodology for composite laminates is presented. Tension fatigue tests were conducted on quasi-isotropic and orthotropic glass epoxy, graphite epoxy, and glass/graphite epoxy hybrid laminates. Edge delamination onset data were used to generate plots of strain energy release rate as a function of cycles to delamination onset. These plots were then used along with strain energy release rate analyses of delaminations initiating at matrix cracks to predict local delamination onset. Stiffness loss was measured experimentally to account for the accumulation of matrix cracks and for delamination growth. Fatigue failure was predicted by comparing the increase in global strain resulting from stiffness loss to the decrease in laminate failure strain resulting from delaminations forming at matrix cracks through the laminate thickness. Good agreement between measured and predicted lives indicated that the through-thickness damage accumulation model can accurately describe fatigue failure for laminates where the delamination onset behavior in fatigue is well characterized, and stiffness loss can be monitored in real time to account for damage growth