Magnetic interaction in exchange-biased bilayers: a first-order reversal curve analysis

The distributions of coercivities and magnetic interactions in a set of polycrystalline Ni(0.8)Fe(0.2)/FeMn bilayers have been determined using the first-order reversal curve (FORC) formalism. The thickness of the permalloy (Py) film was fixed at 10 nm (nominal), while that of the FeMn film varied within the range 0-20 nm. The FORC diagrams of each bilayer displayed two clearly distinguishable regions. The main region was generated by Py particles whose coercivities were enhanced in comparison with those in which the FeMn film was absent (sample O). The minor region was produced by Py particles with coercivities similar to or slightly higher than those of particles in the Py film of sample O. Each sample presented two distributions of interaction fields, one for each region, and both were centred slightly below the exchange-bias field, thus indicating a prevalence of magnetizing interactions. These results are consistent with a grain size distribution in the Py layer and the presence of uncompensated antiferromagnetic moments.

First-order-reversal-curve analysis of Pr-Fe-B-based exchange spring magnets

Ribbons of nominal composition (Pr(9.5)Fe(84.5)B(6))(0.96)Cr(0.01)(TiC)(0.03) were produced by arc-melting and melt-spinning the alloys on a Cu wheel. X-ray diffraction (XRD) reveals two main phases, one based upon alpha-Fe and the other upon Pr(2)Fe(14)B. The ribbons show exchange spring behavior with H (c) = 12.5 kOe and (BH)(max) = 13.6 MGOe when these two phases are well coupled. Transmission electron microscopy revealed the coupled behavior is observed when the microstructure consists predominantly of alpha-Fe grains (diameter similar to 100 nm.) surrounded by hard material containing Pr(2)Fe(14)B. The microstructure is discussed in terms of a calculation by Skomski and Coey. A first-order-reversal-curve (FORC) analysis was performed for both a well-coupled sample and a poorly coupled sample. The FORC diagrams show two strong peaks for both the poorly coupled sample and for the well-coupled material. In both cases, the localization of the FORC probability suggests magnetizing interactions between particles. Switching field distributions were calculated and are consistent with the sample microstructure.

First-order-reversal-curve analysis of Pr-Fe-B-based nanocomposites

Ribbons of nominal composition (Pr(9.5)Fe(84.5)B(6))(0.96)Cr(0.01)(TiC)(0.03) were produced by arc-melting and melt-spinning the alloys on a Cu wheel. X-ray diffraction reveals two main phases, one based upon alpha-Fe and the other upon Pr(2)Fe(14)B. The ribbons show exchange spring behavior with H(c)=12.5 kOe and (BH)(max)= 13.6 MGOe when these two phases are well coupled. Transmission electron microscopy revealed that the coupled behavior is observed when the microstructure consists predominantly of alpha-Fe grains(diameter similar to 100 nm.) surrounded by hard material containing Pr(2)Fe(14)B. A first-order-reversal-curve (FORC) analysis was performed for both a well-coupled sample and a partially-coupled sample. The FORC diagrams show two strong peaks for both the partially-coupled sample and for the well coupled material. In both cases, the localization of the FORC probability suggests demagnetizing interactions between particles. Switching field distributions were calculated and are consistent with the sample microstructure. (C) 2009 Elsevier B.V. All rights reserved.

First order reversal curve analysis of nanocrystalline Pd(80)Co(20) alloy films

Films of isotropic nanocrystalline Pd(80)Co(20) alloys were obtained by electrodeposition onto brass substrate in plating baths maintained at different pH values. Increasing the pH of the plating bath led to an increase in mean grain size without inducing significant changes in the composition of the alloy. The magnetocrystalline anisotropy constant was estimated and the value was of the same order of magnitude as that reported for samples with perpendicular magnetic anisotropy. First order reversal curve (FORC) analysis revealed the presence of an important component of reversible magnetization. Also, FORC diagrams obtained at different sweep rate of the applied magnetic field, revealed that this reversible component is strongly affected by kinetic effect. The slight bias observed in the irreversible part of the FORC distribution suggested the dominance of magnetizing intergrain exchange coupling over demagnetizing dipolar interactions and microstructural disorder. (c) 2009 Elsevier B.V. All rights reserved.

Influence diagnostics for linear models with first-order autoregressive elliptical errors

We introduce in this paper the class of linear models with first-order autoregressive elliptical errors. The score functions and the Fisher information matrices are derived for the parameters of interest and an iterative process is proposed for the parameter estimation. Some robustness aspects of the maximum likelihood estimates are discussed. The normal curvatures of local influence are also derived for some usual perturbation schemes whereas diagnostic graphics to assess the sensitivity of the maximum likelihood estimates are proposed. The methodology is applied to analyse the daily log excess return on the Microsoft whose empirical distributions appear to have AR(1) and heavy-tailed errors. (C) 2008 Elsevier B.V. All rights reserved.

Recording from Two Neurons: Second-Order Stimulus Reconstruction from Spike Trains and Population Coding

We study the reconstruction of visual stimuli from spike trains, representing the reconstructed stimulus by a Volterra series up to second order. We illustrate this procedure in a prominent example of spiking neurons, recording simultaneously from the two H1 neurons located in the lobula plate of the fly Chrysomya megacephala. The fly views two types of stimuli, corresponding to rotational and translational displacements. Second-order reconstructions require the manipulation of potentially very large matrices, which obstructs the use of this approach when there are many neurons. We avoid the computation and inversion of these matrices using a convenient set of basis functions to expand our variables in. This requires approximating the spike train four-point functions by combinations of two-point functions similar to relations, which would be true for gaussian stochastic processes. In our test case, this approximation does not reduce the quality of the reconstruction. The overall contribution to stimulus reconstruction of the second-order kernels, measured by the mean squared error, is only about 5% of the first-order contribution. Yet at specific stimulus-dependent instants, the addition of second-order kernels represents up to 100% improvement, but only for rotational stimuli. We present a perturbative scheme to facilitate the application of our method to weakly correlated neurons.

Second-order negative-curvature methods for box-constrained and general constrained optimization

A Nonlinear Programming algorithm that converges to second-order stationary points is introduced in this paper. The main tool is a second-order negative-curvature method for box-constrained minimization of a certain class of functions that do not possess continuous second derivatives. This method is used to define an Augmented Lagrangian algorithm of PHR (Powell-Hestenes-Rockafellar) type. Convergence proofs under weak constraint qualifications are given. Numerical examples showing that the new method converges to second-order stationary points in situations in which first-order methods fail are exhibited.