Band edge noise spectroscopy of a magnetic tunnel junction

We propose a conceptually new way to gather information on the electron bands of buried metal(semiconductor)/insulator interfaces. The bias dependence of low frequency noise in Fe$_{1-x}$V$_{x}$/MgO/Fe (0 $<$ x $<$ 0.25) tunnel junctions show clear anomalies at specific applied voltages, reflecting electron tunneling to the band edges of the magnetic electrodes. The change in magnitude of these noise anomalies with the magnetic state allows evaluating the degree of spin mixing between the spin polarized bands at the ferromagnet/insulator interface. Our results are in qualitative agreement with numerical calculations

Online bayesian inference in some time-frequency representations of non-stationary processes

The use of Bayesian inference in the inference of time-frequency representations has, thus far, been limited to offline analysis of signals, using a smoothing spline based model of the time-frequency plane. In this paper we introduce a new framework that allows the routine use of Bayesian inference for online estimation of the time-varying spectral density of a locally stationary Gaussian process. The core of our approach is the use of a likelihood inspired by a local Whittle approximation. This choice, along with the use of a recursive algorithm for non-parametric estimation of the local spectral density, permits the use of a particle filter for estimating the time-varying spectral density online. We provide demonstrations of the algorithm through tracking chirps and the analysis of musical data

Unlocking the gender diversity-group performance link: the moderating role of relative cultural distance

PurposeThis study aims to shed light on the relationship between gender diversity and group performance by considering the moderating role of relative cultural distance. Drawing from the categorization-elaboration model (CEM), the authors hypothesize that gender-diverse collaborative learning groups perform better when a low level of relative cultural distance in country-level individualism-collectivism or power distance exists among group members.Design/methodology/approachTo test this hypothesis, the authors conducted a study on 539 undergraduate students organized into 94 groups. The assessment of group performance was based on scores given by external raters.FindingsThe authors found that relative cultural distance significantly moderated the gender diversity-group performance relationship such that gender diversity was positively related to group performance when the collaborative learning group included members who similarly valued individualism-collectivism or power distance (i.e. relative cultural distance was low) and was negatively related to group performance when the collaborative learning group comprised members who differently valued individualism-collectivism or power distance (i.e. relative cultural distance was high).Originality/valueThis study contributes to understanding when gender diversity is positively associated with group performance by expanding the range of previously examined diversity dimensions to include relative cultural distance in country-level individualism-collectivism and power distance

Size effect on magnetism of Fe thin films in Fe/Ir superlattices

In ferromagnetic thin films, the Curie temperature variation with the thickness is always considered as continuous when the thickness is varied from $n$ to $n+1$ atomic planes. We show that it is not the case for Fe in Fe/Ir superlattices. For an integer number of atomic planes, a unique magnetic transition is observed by susceptibility measurements, whereas two magnetic transitions are observed for fractional numbers of planes. This behavior is attributed to successive transitions of areas with $n$ and $n+1$ atomic planes, for which the $T_c$'s are not the same. Indeed, the magnetic correlation length is presumably shorter than the average size of the terraces. Monte carlo simulations are performed to support this explanation.Comment: LaTeX file with Revtex, 5 pages, 5 eps figures, to appear in Phys. Rev. Let

Finite-size scaling in thin Fe/Ir(100) layers

The critical temperature of thin Fe layers on Ir(100) is measured through M\"o{\ss}bauer spectroscopy as a function of the layer thickness. From a phenomenological finite-size scaling analysis, we find an effective shift exponent lambda = 3.15 +/- 0.15, which is twice as large as the value expected from the conventional finite-size scaling prediction lambda=1/nu, where nu is the correlation length critical exponent. Taking corrections to finite-size scaling into account, we derive the effective shift exponent lambda=(1+2\Delta_1)/nu, where Delta_1 describes the leading corrections to scaling. For the 3D Heisenberg universality class, this leads to lambda = 3.0 +/- 0.1, in agreement with the experimental data. Earlier data by Ambrose and Chien on the effective shift exponent in CoO films are also explained.Comment: Latex, 4 pages, with 2 figures, to appear in Phys. Rev. Lett

Time series prediction via aggregation : an oracle bound including numerical cost

We address the problem of forecasting a time series meeting the Causal Bernoulli Shift model, using a parametric set of predictors. The aggregation technique provides a predictor with well established and quite satisfying theoretical properties expressed by an oracle inequality for the prediction risk. The numerical computation of the aggregated predictor usually relies on a Markov chain Monte Carlo method whose convergence should be evaluated. In particular, it is crucial to bound the number of simulations needed to achieve a numerical precision of the same order as the prediction risk. In this direction we present a fairly general result which can be seen as an oracle inequality including the numerical cost of the predictor computation. The numerical cost appears by letting the oracle inequality depend on the number of simulations required in the Monte Carlo approximation. Some numerical experiments are then carried out to support our findings

White-handed gibbons discriminate context-specific songs compositions

This research project has been funded by the Leverhulme Trust (Research Leadership Award F/00268/AP), the European Research Council (grant number FP7; PRILANG GA283871) and the Swiss National Science Foundation (310030_185324).White-handed gibbons produce loud and acoustically complex songs when interacting with their neighbours or when encountering predators. In both contexts, songs are assembled from a small number of units although their composition differs in context-specific ways. Here, we investigated whether wild gibbons could infer the ‘meaning’ when hearing exemplars recorded in both contexts (i.e. ‘duet songs’ vs. ‘predator songs’). We carried out a playback experiment by which we simulated the presence of a neighbouring group producing either its duet or a predator song in order to compare subjects’ vocal and locomotor responses. When hearing a recording of a duet song, subjects reliably responded with their own duet song, which sometimes elicited further duet songs in adjacent groups. When hearing a recording of a predator song, however, subjects typically remained silent, apart from one of six groups which replied with its own predator song. Moreover, in two of six trials, playbacks of predator songs elicited predator song replies in non-adjacent groups. Finally, all groups showed strong anti-predator behaviour to predator songs but never to duet songs. We concluded that white-handed gibbons discriminated between the two song types and were able to infer meaning from them. We discuss the implications of these findings in light of the current debate on the evolutionary origins of syntax.Publisher PDFPeer reviewe

Bayesian Parameter Estimation for Latent Markov Random Fields and Social Networks

Undirected graphical models are widely used in statistics, physics and machine vision. However Bayesian parameter estimation for undirected models is extremely challenging, since evaluation of the posterior typically involves the calculation of an intractable normalising constant. This problem has received much attention, but very little of this has focussed on the important practical case where the data consists of noisy or incomplete observations of the underlying hidden structure. This paper specifically addresses this problem, comparing two alternative methodologies. In the first of these approaches particle Markov chain Monte Carlo (Andrieu et al., 2010) is used to efficiently explore the parameter space, combined with the exchange algorithm (Murray et al., 2006) for avoiding the calculation of the intractable normalising constant (a proof showing that this combination targets the correct distribution in found in a supplementary appendix online). This approach is compared with approximate Bayesian computation (Pritchard et al., 1999). Applications to estimating the parameters of Ising models and exponential random graphs from noisy data are presented. Each algorithm used in the paper targets an approximation to the true posterior due to the use of MCMC to simulate from the latent graphical model, in lieu of being able to do this exactly in general. The supplementary appendix also describes the nature of the resulting approximation.Comment: 26 pages, 2 figures, accepted in Journal of Computational and Graphical Statistics (http://www.amstat.org/publications/jcgs.cfm

Increased risk of breast cancer among female relatives of patients with ataxia-telangiectasia: a causal relationship?

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