Generalized Newton's Method based on Graphical Derivatives

This paper concerns developing a numerical method of the Newton type to solve systems of nonlinear equations described by nonsmooth continuous functions. We propose and justify a new generalized Newton algorithm based on graphical derivatives, which have never been used to derive a Newton-type method for solving nonsmooth equations. Based on advanced techniques of variational analysis and generalized differentiation, we establish the well-posedness of the algorithm, its local superlinear convergence, and its global convergence of the Kantorovich type. Our convergence results hold with no semismoothness assumption, which is illustrated by examples. The algorithm and main results obtained in the paper are compared with well-recognized semismooth and $B$-differentiable versions of Newton's method for nonsmooth Lipschitzian equations

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

Magnetocaloric effect in nano- and polycrystalline manganite La0.7Ca0.3MnO3La_{0.7}Ca_{0.3}MnO_3

$La_{0.7}Ca_{0.3}MnO_3$ samples were prepared in nano- and polycrystalline forms by sol-gel and solid state reaction methods, respectively, and structurally characterized by synchrotron X-ray diffraction. The magnetic properties determined by ac susceptibility and dc magnetization measurements are discussed. The magnetocaloric effect in this nanocrystalline manganite is spread over a broader temperature interval than in the polycrystalline case. The relative cooling power of the poly- and nanocrystalline manganites is used to evaluate a possible application for magnetic cooling below room temperature.Comment: 6 pages, 5 (double) figures, 1 table, 16 references; submitted to Appl. Phys.

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

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

Effects of operational disturbance and subsequent recovery process on microbial community during a pilot-scale anaerobic co-digestion

© 2019 This study investigated changes in microbial community structure and composition in response to operational disturbance and subsequent process recovery by inoculum addition. Amplicon sequencing of 16S rRNA and mcrA marker genes on the Illumina Miseq platform was used for microbial community analysis. The results show that imbalance among core microbial groups caused volatile fatty acid accumulation and subsequent deteriorated biogas production (decreased by 45% of daily volume) and methane content (57% of the total abundance) and reduction of acetogenic and methanogenic microbes (they accounted for <9% and <3% of the total abundance, respectively). Acetogens and methanogens were replenished by inoculum addition to recover digester performance. Although digester performances were similar in stable (prior to disturbance) and post recovery phases, the microbial community did not return to the original state, suggesting the existence of functional redundancy in the community

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