Related papers: Novel Giant Magnetoresistance Model using Multiple…
Graph generation is a crucial task in many fields, including network science and bioinformatics, as it enables the creation of synthetic graphs that mimic the properties of real-world networks for various applications. Graph Generative…
We theoretically investigate spin-dependent transport in a TMD-based vertical spin valve, taking WSe$_2$ as a representative example. Using effective Hamiltonians for the heterostructure and the Landauer formula, we derive the transmission…
We outline the duality between the extraordinary magnetoresistance (EMR), observed in semiconductor-metal hybrids, and non-symmetric gravity coupled to a diffusive $U(1)$ gauge field. The corresponding gravity theory may be interpreted as…
Estimation of spatially-varying parameters for computationally expensive forward models governed by partial differential equations is addressed. A novel multiscale Bayesian inference approach is introduced based on deep probabilistic…
Graph Neural Networks (GNNs) and Graph Transformers (GTs) are now a fundamental paradigm for graph learning, combining the representation-learning capabilities of deep models with the sample efficiency induced by their inductive biases.…
The spin-current model of electric polarization of spin origin is developed for a magnetic structure with anisotropic tensor gyromagnetic ratio (g-factor). Three mechanisms of the magnetoelectric effect are proposed, caused by the symmetric…
Exact reflection and transmission coefficients for supersymmetric shape-invariant potentials barriers are calculated by an analytical continuation of the asymptotic wave functions obtained via the introduction of new generalized ladder…
Study on the electronic transport of a large scale two dimensional system by the transfer matrix method (TMM) based on the Sch\"{o}rdinger equation suffers from the numerical instability. To address this problem, we propose a renormalized…
We study the effect of the shape of different potential barriers on the transmitted charged current whose fermionic population is not monoenergetic but is described by means of an energy spectrum. The generalised Kappa Fermi-Dirac…
We present a circuit-compatible compact model of the intrinsic capacitances of graphene field-effect transistors (GFETs). Together with a compact drain current model, a large-signal model of GFETs is developed combining both models as a…
Current studies on adversarial robustness mainly focus on aggregating local robustness results from a set of data samples to evaluate and rank different models. However, the local statistics may not well represent the true global robustness…
Multiple-subject network data are fast emerging in recent years, where a separate connectivity matrix is measured over a common set of nodes for each individual subject, along with subject covariates information. In this article, we propose…
The energy transition is causing many stability-related challenges for power systems. Transient stability refers to the ability of a power grid's bus angles to retain synchronism after the occurrence of a major fault. In this paper a…
Many real-world phenomena can be modeled as a graph, making them extremely valuable due to their ubiquitous presence. GNNs excel at capturing those relationships and patterns within these graphs, enabling effective learning and prediction…
Multi-output regression models must exploit dependencies between outputs to maximise predictive performance. The application of Gaussian processes (GPs) to this setting typically yields models that are computationally demanding and have…
A phenomenological model for the asymmetric giant magnetoimpedance (GMI) in field-annealed amorphous ribbons is developed. The effect of a surface crystalline layer on the GMI response is described in terms of an effective bias field…
Solid state theory, density functional theory and its generalizations for correlated systems together with numerical simulations on supercomputers allow nowadays to model magnetic systems realistically and in detail and can be even used to…
We developed a method to calculate the magnetoresistance of magnetic nanostructures. We discretize a magnetic disk in small cells and numerically solve the Landau-Lifshitz-Gilbert (LLG) equation in order to obtain its magnetization profile.…
We present an approach for continual learning (CL) that is based on fully probabilistic (or generative) models of machine learning. In contrast to, e.g., GANs that are "generative" in the sense that they can generate samples, fully…
We develop a theory to compute the domain-wall magnetoresistance (DWMR) in antiferromagnetic (AFM) metals with different spin structures. In the diffusive transport regime, the DWMR can be either {\it negative} or positive depending on the…