Higher-dimensional Wannier interpolation for the modern theory of the Dzyaloshinskii-Moriya interaction: Application to Co-based trilayers

We present an advanced first-principles formalism to evaluate the Dzyaloshinskii-Moriya interaction (DMI) in its modern theory as well as Berry curvatures in complex spaces based on a higher-dimensional Wannier interpolation. Our method is applied to the Co-based trilayer systems Ir$_\delta$Pt$_{1-\delta}$/Co/Pt and Au$_\gamma$Pt$_{1-\gamma}$/Co/Pt, where we gain insights into the correlations between the electronic structure and the DMI, and we uncover prominent sign changes of the chiral interaction with the overlayer composition. Beyond the discussed phenomena, the scope of applications of our Wannier-based scheme is particularly broad as it is ideally suited to study efficiently the Hamiltonian evolution under the slow variation of very general parameters.Comment: 8 pages, 3 figures, contribution to Special Topics "New ab initio approaches to explore emergent phenomena in quantum matters" in J. Phys. Soc. Jp

Higher dimensional Wannier functions of multi-parameter Hamiltonians

When using Wannier functions to study the electronic structure of multi-parameter Hamiltonians $H^{(\boldsymbol k,\bf \lambda)}$ carrying a dependence on crystal momentum $\boldsymbol k$ and an additional periodic parameter $\bf\lambda$, one usually constructs several sets of Wannier functions for a set of values of $\bf\lambda$. We present the concept of higher dimensional Wannier functions (HDWFs), which provide a minimal and accurate description of the electronic structure of multi-parameter Hamiltonians based on a single set of HDWFs. The obstacle of non-orthogonality of Bloch functions at different $\bf\lambda$ is overcome by introducing an auxiliary real space, which is reciprocal to the parameter $\bf\lambda$. We derive a generalized interpolation scheme and emphasize the essential conceptual and computational simplifications in using the formalism, for instance, in the evaluation of linear response coefficients. We further implement the necessary machinery to construct HDWFs from ab initio within the full-potential linearized augmented plane-wave method (FLAPW). We apply our implementation to accurately interpolate the Hamiltonian of a one-dimensional magnetic chain of Mn atoms in two important cases of $\bf\lambda$: (i) the spin-spiral vector $\boldsymbol q$, and (ii) the direction of the ferromagnetic magnetization $\hat{\boldsymbol m}$. Using the generalized interpolation of the energy, we extract the corresponding values of magneto-crystalline anisotropy energy, Heisenberg exchange constants, and spin stiffness, which compare very well with the values obtained from direct first principles calculations. For toy models we demonstrate that the method of HDWFs can also be used in applications such as the virtual crystal approximation, ferroelectric polarization and spin torques.Comment: 23 pages, 11 figure

Advancing the discussion about Clinical Decision Support Systems to tackle Adverse Drug Events: a ‘problematizing’ approach

Clinical decision support systems (CDSS) can prevent situations in which doctors prescribe a drug to a patient that causes a harmful reaction with another drug that a patient already takes (adverse drug events (ADE)). This can be achieved through generating medication alerts in the moment that a drug is prescribed. Researchers have paid considerable attention to how to design these alerts in the best possible ways, however, largely with inconclusive results. We tackle this body of literature using a ‘problematizing’ approach that enables to understand why research results are inconclusive by disclosing underlying assumptions in a body of literature that have over time shaped a scholarly debate into a particular direction. We uncover four problematic assumptions, offer alternatives to these assumptions and outline potentials to implement our ideas in future research projects

Induction motors improvement for a variable speed drive

New improvement way of induction motors for a variable speed drive when changing mass dimension indices has been proposed. It allows for improvement of energy indices and reduction of running costs. The analysis of simulation results has been carried out and calculation results of economic efficiency of the achieved methods of approach to energy effective induction motors design have been suggested

Statistical Learning Analysis in Neuroscience: Aiming for Transparency

Encouraged by a rise of reciprocal interest between the machine learning and neuroscience communities, several recent studies have demonstrated the explanatory power of statistical learning techniques for the analysis of neural data. In order to facilitate a wider adoption of these methods, neuroscientific research needs to ensure a maximum of transparency to allow for comprehensive evaluation of the employed procedures. We argue that such transparency requires “neuroscience-aware” technology for the performance of multivariate pattern analyses of neural data that can be documented in a comprehensive, yet comprehensible way. Recently, we introduced PyMVPA, a specialized Python framework for machine learning based data analysis that addresses this demand. Here, we review its features and applicability to various neural data modalities

Task relevance modulates the cortical representation of feature conjunctions in the target template

AbstractLittle is known about the cortical regions involved in representing task-related content in preparation for visual task performance. Here we used representational similarity analysis (RSA) to investigate the BOLD response pattern similarity between task relevant and task irrelevant feature dimensions during conjunction viewing and target template maintenance prior to visual search. Subjects were cued to search for a spatial frequency (SF) or orientation of a Gabor grating and we measured BOLD signal during cue and delay periods before the onset of a search display. RSA of delay period activity revealed that widespread regions in frontal, posterior parietal, and occipitotemporal cortices showed general representational differences between task relevant and task irrelevant dimensions (e.g., orientation vs. SF). In contrast, RSA of cue period activity revealed sensory-related representational differences between cue images (regardless of task) at the occipital pole and additionally in the frontal pole. Our data show that task and sensory information are represented differently during viewing and during target template maintenance, and that task relevance modulates the representation of visual information across the cortex.</jats:p

Topological magneto-optical effects and their quantization in noncoplanar antiferromagnets

Reflecting the fundamental interactions of polarized light with magnetic matter, magneto-optical effects are well known since more than a century. The emergence of these phenomena is commonly attributed to the interplay between exchange splitting and spin-orbit coupling in the electronic structure of magnets. Using theoretical arguments, we demonstrate that topological magneto-optical effects can arise in noncoplanar antiferromagnets due to the finite scalar spin chirality, without any reference to exchange splitting or spin-orbit coupling. We propose spectral integrals of certain magneto-optical quantities that uncover the unique topological nature of the discovered effect. We also find that the Kerr and Faraday rotation angles can be quantized in insulating topological antiferromagnets in the low-frequency limit, owing to nontrivial global properties that manifest in quantum topological magneto-optical effects. Although the predicted topological and quantum topological magneto-optical effects are fundamentally distinct from conventional light-matter interactions, they can be measured by readily available experimental techniques.Comment: 10 pages, 5 figure