Intrinsic Domain Wall Resistance in Ferromagnetic Semiconductors

Transport through zincblende magnetic semiconductors with magnetic domain walls is studied theoretically. We show that these magnetic domain walls have an intrinsic resistance due to the spin-orbit interaction. The intrinsic resistance is independent of the domain wall shape and width when the latter is larger than the Fermi wavelength. For typical parameters, the intrinsic domain wall resistance is comparable to the Sharvin resistance and should be experimentally measurable.Comment: Final versio

Anisotropic Magneto-Thermopower: the Contribution of Interband Relaxation

Spin injection in metallic normal/ferromagnetic junctions is investigated taking into account the anisotropic magnetoresistance (AMR) occurring in the ferromagnetic layer. It is shown, on the basis of a generalized two channel model, that there is an interface resistance contribution due to anisotropic scattering, beyond spin accumulation and giant magnetoresistance (GMR). The corresponding expression of the thermopower is derived and compared with the expression for the thermopower produced by the GMR. First measurements of anisotropic magnetothermopower are presented in electrodeposited Ni nanowires contacted with Ni, Au and Cu. The results of this study show that while the giant magnetoresistance and corresponding thermopower demonstrates the role of spin-flip scattering, the observed anisotropic magnetothermopower indicates interband s-d relaxation mechanisms.Comment: 20 pages, 4 figure

Lateral spin-orbit interaction and spin polarization in quantum point contacts

We study ballistic transport through semiconductor quantum point contact systems under different confinement geometries and applied fields. In particular, we investigate how the {\em lateral} spin-orbit coupling, introduced by asymmetric lateral confinement potentials, affects the spin polarization of the current. We find that even in the absence of external magnetic fields, a variable {\em non-zero spin polarization} can be obtained by controlling the asymmetric shape of the confinement potential. These results suggest a new approach to produce spin polarized electron sources and we study the dependence of this phenomenon on structural parameters and applied magnetic fields. This asymmetry-induced polarization provides also a plausible explanation of our recent observations of a 0.5 conductance plateau (in units of $2e^2/h$) in quantum point contacts made on InAs quantum-well structures. Although our estimates of the required spin-orbit interaction strength in these systems do not support this explanation, they likely play a role in the effects enhanced by electron-electron interactions.Comment: Summited to PRB (2009

Zero Entropy Interval Maps And MMLS-MMA Property

We prove that the flow generated by any interval map with zero topological entropy is minimally mean-attractable (MMA) and minimally mean-L-stable (MMLS). One of the consequences is that any oscillating sequence is linearly disjoint with all flows generated by interval maps with zero topological entropy. In particular, the M\"obius function is orthogonal to all flows generated by interval maps with zero topological entropy (Sarnak's conjecture for interval maps). Another consequence is a non-trivial example of a flow having the discrete spectrum.Comment: 12 page

External Control of a Metal-Insulator Transition in GaMnAs Wires

Quantum transport in disordered ferromagnetic (III,Mn)V semiconductors is studied theoretically. Mesoscopic wires exhibit an Anderson disorder-induced metal-insulator transition that can be controlled by a weak external magnetic field. This metal-insulator transition should also occur in other materials with large anisotropic magneto resistance effects. The transition can be useful for studies of zero-temperature quantum critical phase transitions and fundamental material properties.Comment: Major revised final versio

Quantitative rescattering theory for laser-induced high-energy plateau photoelectron spectra

A comprehensive quantitative rescattering (QRS) theory for describing the production of high-energy photoelectrons generated by intense laser pulses is presented. According to the QRS, the momentum distributions of these electrons can be expressed as the product of a returning electron wave packet with the elastic differential cross sections (DCS) between free electrons with the target ion. We show that the returning electron wave packets are determined mostly by the lasers only, and can be obtained from the strong field approximation. The validity of the QRS model is carefully examined by checking against accurate results from the solution of the time-dependent Schr\"odinger equation for atomic targets within the single active electron approximation. We further show that experimental photoelectron spectra for a wide range of laser intensity and wavelength can be explained by the QRS theory, and that the DCS between electrons and target ions can be extracted from experimental photoelectron spectra. By generalizing the QRS theory to molecular targets, we discuss how few-cycle infrared lasers offer a promising tool for dynamic chemical imaging with temporal resolution of a few femtoseconds.Comment: 19 pages, 19 figure

The Higgs Sector of the Minimal 3 3 1 Model Revisited

The mass spectrum and the eigenstates of the Higgs sector of the minimal 3 3 1 model are revisited in detail. There are discrepancies between our results and previous results by another author.Comment: 20 pages, latex, two figures. One note and one reference are adde

IDENAS: Internal Dependency Exploration for Neural Architecture Search

Machine learning is a powerful tool for extracting valuable information and making various predictions from diverse datasets. Traditional algorithms rely on well-defined input and output variables however, there are scenarios where the distinction between the input and output variables and the underlying, associated (input and output) layers of the model, are unknown. Neural Architecture Search (NAS) and Feature Selection have emerged as promising solutions in such scenarios. This research proposes IDENAS, an Internal Dependency-based Exploration for Neural Architecture Search, integrating NAS with feature selection. The methodology explores internal dependencies in the complete parameter space for classification involving 1D sensor and 2D image data as well. IDENAS employs a modified encoder-decoder model and the Sequential Forward Search (SFS) algorithm, combining input-output configuration search with embedded feature selection. Experimental results demonstrate IDENASs superior performance in comparison to other algorithms, showcasing its effectiveness in model development pipelines and automated machine learning. On average, IDENAS achieved significant modelling improvements, underscoring its significant contribution to advancing the state-of-the-art in neural architecture search and feature selection integration.Comment: 57 pages, 19 figures + appendix, the related software code can be found under the link: https://github.com/viharoszsolt/IDENA