Relative importance of crystal field versus bandwidth to the high pressure spin transition in transition metal monoxides

The crystal field splitting and d bandwidth of the 3d transition metal monoxides MnO, FeO, CoO and NiO are analyzed as a function of pressure within density functional theory. In all four cases the 3d bandwidth is significantly larger than the crystal field splitting over a wide range of compressions. The bandwidth actually increases more as pressure is increased than the crystal field splitting. Therefore the role of increasing bandwidth must be considered in any explanation of a possible spin collapse that these materials may exhibit under pressure.Comment: 7 pages, 4 figure

Comprehension as social and intellectual practice: Rebuilding curriculum in low socioeconomic and cultural minority schools

This article reframes the concept of comprehension as a social and intellectual practice. It reviews current approaches to reading instruction for linguistically and culturally diverse and low socioeconomic students, noting an emphasis on comprehension as autonomous skills. The Four Resources model (Freebody & Luke, 1990) is used to make the case for the integration of comprehension instruction with an emphasis on student cultural and community knowledge, and substantive intellectual and sociocultural content in elementary school curricula. Illustrations are drawn from research underway on the teaching of literacy in primary schools in low SES communities

The general dielectric tensor for bi-kappa magnetized plasmas

In this paper we derive the dielectric tensor for a plasma containing particles described by an anisotropic superthermal (bi-kappa) velocity distribution function. The tensor components are written in terms of the two-variables kappa plasma special functions, recently defined by Gaelzer and Ziebell [Phys. Plasmas 23, 022110 (2016)]. We also obtain various new mathematical properties for these functions, which are useful for the analytical treatment, numerical implementation and evaluation of the functions and, consequently, of the dielectric tensor. The formalism developed here and in the previous paper provides a mathematical framework for the study of electromagnetic waves propagating at arbitrary angles and polarizations in a superthermal plasma.Comment: Accepted for publication in Physics of Plasma

Obliquely propagating electromagnetic waves in magnetized kappa plasmas

Velocity distribution functions (VDFs) that exhibit a power-law dependence on the high-energy tail have been the subject of intense research by the plasma physics community. Such functions, known as kappa or superthermal distributions, have been found to provide a better fitting to the VDFs measured by spacecraft in the solar wind. One of the problems that is being addressed on this new light is the temperature anisotropy of solar wind protons and electrons. In the literature, the general treatment for waves excited by (bi-)Maxwellian plasmas is well-established. However, for kappa distributions, the wave characteristics have been studied mostly for the limiting cases of purely parallel or perpendicular propagation, relative to the ambient magnetic field. Contributions to the general case of obliquely-propagating electromagnetic waves have been scarcely reported so far. The absence of a general treatment prevents a complete analysis of the wave-particle interaction in kappa plasmas, since some instabilities can operate simultaneously both in the parallel and oblique directions. In a recent work, Gaelzer and Ziebell [J. Geophys. Res. 119, 9334 (2014)] obtained expressions for the dielectric tensor and dispersion relations for the low-frequency, quasi-perpendicular dispersive Alfv\'en waves resulting from a kappa VDF. In the present work, the formalism introduced by Ref. 1 is generalized for the general case of electrostatic and/or electromagnetic waves propagating in a kappa plasma in any frequency range and for arbitrary angles. An isotropic distribution is considered, but the methods used here can be easily applied to more general anisotropic distributions, such as the bi-kappa or product-bi-kappa.Comment: Accepted for publication in Physics of Plasmas; added references for section

Muon spin rotation/relaxation measurements of the non-centrosymmetric superconductor Mg10Ir19B16

We have searched for time-reversal symmetry breaking fields in the non-centrosymmetric superconductor Mg$_{10}$Ir$_{19}$B$_{16}$ via muon spin relaxation in zero applied field, and we measured the temperature dependence of the superfluid density by muon spin rotation in transverse field to investigate the superconducting pairing symmetry in two polycrystalline samples of signficantly different purities. In the high purity sample, we detected no time-reversal symmetry breaking fields greater than 0.05 G. The superfluid density was also found to be exponentially-flat as T$\to$0, and so can be fit to a single-gap BCS model. In contrast, the lower purity sample showed an increase in the zero-field $\mu$SR relaxation rate below T$_c$ corresponding to a characteristic field strength of 0.6 G. While the temperature-dependence of the superfluid density was also found to be consistent with a single-gap BCS model, the magnitude as T$\to$0 was found to be much lower for a given applied field than in the case of the high purity sample. These findings suggest that the dominant pairing symmetry in high quality Mg$_{10}$Ir$_{19}$B$_{16}$ samples corresponds to the spin-singlet channel, while sample quality drastically affects the superconducting properties of this system.Comment: 6 pages, 5 figures, revised version resubmitted to PR

The genes that carcinogens act upon

No abstract available

Deep learning cardiac motion analysis for human survival prediction

Motion analysis is used in computer vision to understand the behaviour of moving objects in sequences of images. Optimising the interpretation of dynamic biological systems requires accurate and precise motion tracking as well as efficient representations of high-dimensional motion trajectories so that these can be used for prediction tasks. Here we use image sequences of the heart, acquired using cardiac magnetic resonance imaging, to create time-resolved three-dimensional segmentations using a fully convolutional network trained on anatomical shape priors. This dense motion model formed the input to a supervised denoising autoencoder (4Dsurvival), which is a hybrid network consisting of an autoencoder that learns a task-specific latent code representation trained on observed outcome data, yielding a latent representation optimised for survival prediction. To handle right-censored survival outcomes, our network used a Cox partial likelihood loss function. In a study of 302 patients the predictive accuracy (quantified by Harrell's C-index) was significantly higher (p < .0001) for our model C=0.73 (95$\%$ CI: 0.68 - 0.78) than the human benchmark of C=0.59 (95$\%$ CI: 0.53 - 0.65). This work demonstrates how a complex computer vision task using high-dimensional medical image data can efficiently predict human survival