Metallic characteristics in superlattices composed of insulators, NdMnO3/SrMnO3/LaMnO3

We report on the electronic properties of superlattices composed of three different antiferromagnetic insulators, NdMnO3/SrMnO3/LaMnO3 grown on SrTiO3 substrates. Photoemission spectra obtained by tuning the x-ray energy at the Mn 2p -> 3d edge show a Fermi cut-off, indicating metallic behavior mainly originating from Mn e_g electrons. Furthermore, the density of states near the Fermi energy and the magnetization obey a similar temperature dependence, suggesting a correlation between the spin and charge degrees of freedom at the interfaces of these oxides

Transport properties in Simplified Double Exchange model

Transport properties of the manganites by the double-exchange mechanism are considered. The system is modeled by a simplified double-exchange model, i.e. the Hund coupling of the itinerant electron spins and local spins is simplified to the Ising-type one. The transport properties such as the electronic resistivity, the thermal conductivity, and the thermal power are calculated by using Dynamical mean-field theory. The transport quantities obtained qualitatively reproduce the ones observed in the manganites. The results suggest that the Simplified double exchange model underlies the key properties of the manganites.Comment: 5 pages, 5 eps figure

Magnetic Reconnection and Intermittent Turbulence in the Solar Wind

A statistical relationship between magnetic reconnection, current sheets and intermittent turbulence in the solar wind is reported for the first time using in-situ measurements from the Wind spacecraft at 1 AU. We identify intermittency as non-Gaussian fluctuations in increments of the magnetic field vector, $\mathbf{B}$, that are spatially and temporally non-uniform. The reconnection events and current sheets are found to be concentrated in intervals of intermittent turbulence, identified using the partial variance of increments method: within the most non-Gaussian 1% of fluctuations in $\mathbf{B}$, we find 87%-92% of reconnection exhausts and $\sim$9% of current sheets. Also, the likelihood that an identified current sheet will also correspond to a reconnection exhaust increases dramatically as the least intermittent fluctuations are removed from the dataset. Hence, the turbulent solar wind contains a hierarchy of intermittent magnetic field structures that are increasingly linked to current sheets, which in turn are progressively more likely to correspond to sites of magnetic reconnection. These results could have far reaching implications for laboratory and astrophysical plasmas where turbulence and magnetic reconnection are ubiquitous.Comment: 5 pages, 3 figures, submitted to Physical Review Letter

From unsupervised to semi-supervised adversarial domain adaptation in EEG-based sleep staging.

OBJECTIVE: The recent breakthrough of wearable sleep monitoring devices results in large amounts of sleep data. However, as limited labels are available, interpreting these data requires automated sleep stage classification methods with a small need for labeled training data. Transfer learning and domain adaptation offer possible solutions by enabling models to learn on a source dataset and adapt to a target dataset. APPROACH: In this paper, we investigate adversarial domain adaptation applied to real use cases with wearable sleep datasets acquired from diseased patient populations. Different practical aspects of the adversarial domain adaptation framework \hl{are examined}, including the added value of (pseudo-)labels from the target dataset and the influence of domain mismatch between the source and target data. The method is also implemented for personalization to specific patients. MAIN RESULTS: The results show that adversarial domain adaptation is effective in the application of sleep staging on wearable data. When compared to a model applied on a target dataset without any adaptation, the domain adaptation method in its simplest form achieves relative gains of 7%-27% in accuracy. The performance on the target domain is further boosted by adding pseudo-labels and real target domain labels when available, and by choosing an appropriate source dataset. Furthermore, unsupervised adversarial domain adaptation can also personalize a model, improving the performance by 1%-2% compared to a non-personal model. SIGNIFICANCE: In conclusion, adversarial domain adaptation provides a flexible framework for semi-supervised and unsupervised transfer learning. This is particularly useful in sleep staging and other wearable EEG applications

Inverse lift: a signature of the elasticity of complex fluids?

To understand the mechanics of a complex fluid such as a foam we propose a model experiment (a bidimensional flow around an obstacle) for which an external sollicitation is applied, and a local response is measured, simultaneously. We observe that an asymmetric obstacle (cambered airfoil profile) experiences a downards lift, opposite to the lift usually known (in a different context) in aerodynamics. Correlations of velocity, deformations and pressure fields yield a clear explanation of this inverse lift, involving the elasticity of the foam. We argue that such an inverse lift is likely common to complex fluids with elasticity.Comment: 4 pages, 4 figures, revised version, submitted to PR

Linear response within the projection-based renormalization method: Many-body corrections beyond the random phase approximation

The explicit evaluation of linear response coefficients for interacting many-particle systems still poses a considerable challenge to theoreticians. In this work we use a novel many-particle renormalization technique, the so-called projector-based renormalization method, to show how such coefficients can systematically be evaluated. To demonstrate the prospects and power of our approach we consider the dynamical wave-vector dependent spin susceptibility of the two-dimensional Hubbard model and also determine the subsequent magnetic phase diagram close to half-filling. We show that the superior treatment of (Coulomb) correlation and fluctuation effects within the projector-based renormalization method significantly improves the standard random phase approximation results.Comment: 17 pages, 7 figures, revised versio

Confinement effect on solar thermal heating process of TiN solutions

We propose a theoretical approach to describe quantitatively the heating process in aqueous solutions of dispersed TiN nanoparticles under solar illumination. The temperature gradients of solution with different concentrations of TiN nanoparticles are calculated when confinement effects of the container on the solar absorption are taken into account. We find that the average penetration of solar radiation into the solution is significantly reduced with increasing the nanoparticle concentration. At high concentrations, our numerical results show that photons are localized near the surface of the solution. Moreover, the heat energy balance equation at the vapor-liquid interface is used to describe the solar steam generation. The theoretical time dependence of temperature rise and vaporization weight losses is consistent with experiments. Our calculations give strong evidence that the substantially localized heating near the vapor-liquid interface is the main reason for the more efficient steam generation process by floating plasmonic membranes when compared to randomly dispersed nanoparticles. The validated theoretical model suggests that our approach can be applied towards new predictions and other experimental data descriptions.Comment: 6 pages, 3 figures, accepted for publication in PCC