This article reviews static and dynamic interfacial effects in magnetism, focusing on interfacially-driven magnetic effects and phenomena associated with spin-orbit coupling and intrinsic symmetry breaking at interfaces. It provides a historical background and literature survey, but focuses on recent progress, identifying the most exciting new scientific results and pointing to promising future research directions. It starts with an introduction and overview of how basic magnetic properties are affected by interfaces, then turns to a discussion of charge and spin transport through and near interfaces and how these can be used to control the properties of the magnetic layer. Important concepts include spin accumulation, spin currents, spin transfer torque, and spin pumping. An overview is provided to the current state of knowledge and existing review literature on interfacial effects such as exchange bias, exchange spring magnets, spin Hall effect, oxide heterostructures, and topological insulators. The article highlights recent discoveries of interface-induced magnetism and non-collinear spin textures, non-linear dynamics including spin torque transfer and magnetization reversal induced by interfaces, and interfacial effects in ultrafast magnetization processes.

https://link.aps.org/accepted/10.1103/RevModPhys.89.025006

Interface-induced phenomena in magnetism

Introduction: Preliminaries.- SSA Methodology and the Structure of the Book.- SSA Topics Outside the Scope of this Book.- Common Symbols and Acronyms.- Basic SSA: The Main Algorithm.- Potential of Basic SSA.- Models of Time Series and SSA Objectives.- Choice of Parameters in Basic SSA.- Some Variations of Basic SSA.- SSA for Forecasting, interpolation, Filtration and Estimation: SSA Forecasting Algorithms.- LRR and Associated Characteristic Polynomials.- Recurrent Forecasting as Approximate Continuation.- Confidence Bounds for the Forecast.- Summary and Recommendations on Forecasting Parameters.- Case Study: 'Fortified Wine'.- Missing Value Imputation.- Subspace-Based Methods and Estimation of Signal Parameters.- SSA and Filters.

Singular Spectrum Analysis for Time Series

A numerical study of laminar incompressible flows in symmetric plane sudden expansions was carried out. Computations were performed for various Reynolds number and expansion ratios. The results revealed that the flow remains symmetric up to a certain Reynolds number depending on the expansion ratio, while asymmetries appear at higher Reynolds numbers. The computations indicated that the critical Reynolds number of the symmetry‐breaking bifurcation reduces when increasing the expansion ratio while the flow regains symmetry downstream of an initial channel length. The flow asymmetries were verified by comparing several discretization schemes up to fourth order of accuracy as well as various iterative solvers.

Bifurcation phenomena in incompressible sudden expansion flows

Feature extraction is of high importance for effective data classification in hyperspectral imaging (HSI). Considering the high correlation among band images, spectral-domain feature extraction is widely employed. For effective spatial information extraction, a 2-D extension to singular spectrum analysis (2D-SSA), which is a recent technique for generic data mining and temporal signal analysis, is proposed. With 2D-SSA applied to HSI, each band image is decomposed into varying trends, oscillations, and noise. Using the trend and the selected oscillations as features, the reconstructed signal, with noise highly suppressed, becomes more robust and effective for data classification. Three publicly available data sets for HSI remote sensing data classification are used in our experiments. Comprehensive results using a support vector machine classifier have quantitatively evaluated the efficacy of the proposed approach. Benchmarked with several state-of-the-art methods including 2-D empirical mode decomposition (2D-EMD), it is found that our proposed 2D-SSA approach generates the best results in most cases. Unlike 2D-EMD that requires sequential transforms to obtain detailed decomposition, 2D-SSA extracts all components simultaneously. As a result, the execution time in feature extraction can be also dramatically reduced. The superiority in terms of enhanced discrimination ability from 2D-SSA is further validated when a relatively weak classifier, i.e., the k-nearest neighbor, is used for data classification. In addition, the combination of 2D-SSA with 1-D principal component analysis (2D-SSA-PCA) has generated the best results among several other approaches, demonstrating the great potential in combining 2D-SSA with other approaches for effective spatial-spectral feature extraction and dimension reduction in HSI.

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Novel Two-Dimensional Singular Spectrum Analysis for Effective Feature Extraction and Data Classification in Hyperspectral Imaging

Non-constant positive steady states of the Sel'kov model ☆

A selection of the invited talks given at the Malvern seminar on Dynamical Systems held at RSRE, Malvern in April 1985, presenting very recent developments in the fields of fluids, chemical reactions, non-linear and quantum optics, and theories of Hamiltonian and dissipative systems.

Sarben Sarkar

Papers

Nonlinear phenomena and chaos