Magnetic properties of Co nanoparticles in zirconia matrix

Granular films composed of nanometric Co particles embedded in an insulating ZrO₂ matrix were prepared by pulsed laser deposition in a wide range of Co volume concentrations (0.06<x_v<0.42). High-resolution electron microscopy (HREM) shows very sharp interfaces between the crystalline particles and the amorphous matrix, with no evidence of intermixing. The mean particles size and width of the distribution determined by fitting the low-field magnetic susceptibility and magnetization curves in the paramagnetic regime to a distribution of Langevin functions are in agreement with the parameters extracted from direct TEM observations. Ferromagnetic correlations between Co particles are evident in the field-cooled state when increasing Co concentration. The effective anisotropy constant estimated from magnetic measurements is about two orders of magnitude larger than the bulk value, and decreases as particle size increases.     

Long range ordering in self-assembled Ni arrays on patterned Si

We have succeeded in aligning self-assembled structures by using a lithographically defined stripe. The 140 nm wide by 100 nm high SiO₂ strip is shown to guide the assembly of 500 nm latex spheres so that spheres are aligned along the strip and are in registration on either side of the strip. This method can be used to increase long-range ordering in magnetic storage systems without compromising the density. Inverse sphere Ni arrays were made by electrodeposition through the latex template. We also show that the hexagonal symmetry of the resulting inverse sphere Ni arrays can be simulated using the approach presented below.     

Random, but not so much a parameterization for the returns and correlation matrix of financial time series

A parameterization that is a modified version of a previous work is proposed for the returns and correlation matrix of financial time series and its properties are studied. This parameterization allows easy introduction of non-stationarity and it shows several of the characteristics of the true, observed realizations, such as fat tails, volatility clustering, and a spectrum of eigenvalues of the correlation matrix that can be understood as an extension of Random Matrix Theory results. The predicted behavior of this parameterization for the eigenvalues is compared with the eigenvalues of Brazilian assets and it is shown that those predictions fit the data better than Random Matrix Theory.     

How random is your heart beat?

We measure the content of random uncorrelated noise in heart rate variability using a general method of noise level estimation using a coarse-grained entropy. We show that usually, except for atrial fibrillation, the level of such noise is within 5-15% of the variance of the data and that the variability due to the linearly correlated processes is dominant in all cases analyzed but atrial fibrillation. The nonlinear deterministic content of heart rate variability remains significant and may not be ignored.     

Noise sensitivity of portfolio selection in constant conditional correlation GARCH models

This paper investigates the efficiency of minimum variance portfolio optimization for stock price movements following the Constant Conditional Correlation GARCH process proposed by Bollerslev. Simulations show that the quality of portfolio selection can be improved substantially by computing optimal portfolio weights from conditional covariances instead of unconditional ones. Measurement noise can be further reduced by applying some filtering method on the conditional correlation matrix (such as Random Matrix Theory based filtering). As an empirical support for the simulation results, the analysis is also carried out for a time series of S&P500 stock prices.     

From unweighted to weighted networks with local information

In this paper, we analyze an evolving model with local information which can generate a class of networks by choosing different values of the parameter p. The model introduced exhibits the transition from unweighted networks to weighted networks because the distribution of the edge weight can be widely tuned. With the increase in the local information, the degree correlation of the network transforms from assortative to disassortative. We also study the distribution of the degree, strength and edge weight, which all show crossover between exponential and scale-free. Finally, an application of the proposed model to the study of the synchronization is considered. It is concluded that the synchronizability is enhanced when the heterogeneity of the edge weight is reduced.     

Multiresolution information measures applied to speech recognition

Considerable advances in automatic speech recognition have been made in the last decades, thanks specially to the use of hidden Markov models. In the field of speech signal analysis, different techniques have been developed. However, deterioration in the performance of the speech recognizers has been observed when they are trained with clean signal and tested with noisy signals. This is still an open problem in this field. Continuous multiresolution entropy has been shown to be robust to additive noise in applications to different physiological signals. In previous works we have included Shannon and Tsallis entropies, and their corresponding divergences, in different speech analysis and recognition systems. In this paper we present an extension of the continuous multiresolution entropy to different divergences and we propose them as new dimensions for the pre-processing stage of a speech recognition system. This approach takes into account information about changes in the dynamics of speech signal at different scales. The methods proposed here are tested with speech signals corrupted with babble and white noise. Their performance is compared with classical mel cepstral parametrization. The results suggest that these continuous multiresolution entropy related measures provide valuable information to the speech recognition system and that they could be considered to be included as an extra component in the pre-processing stage.     

Initial growth of Boltzmann entropy and chaos in a large assembly of weakly interacting systems

We introduce a high-dimensional symplectic map, modeling a large system, to analyze the interplay between single-particle chaotic dynamics and particles interactions in thermodynamic systems. We study the initial growth of the Boltzmann entropy, S_B, as a function of the coarse-graining resolution (the late stage of the evolution is trivial, as the system is subjected to no external drivings). We show that a characteristic scale emerges, and that the behavior of S_B vs t, at variance with the Gibbs entropy, does not depend on the resolution, as far as it is finer than this scale. The interaction among particles is crucial to achieve this result, while the rate of entropy growth, in its early stage, depends essentially on the single-particle chaotic dynamics. It is possible to interpret the basic features of the dynamics in terms of a suitable Markov approximation.     

Stochastic resonance in an asymmetric bistable system with coloured noises and periodic rectangular signal

In this paper, we consider the phenomenon of stochastic resonance (SR) in an asymmetric bistable system with coloured noises and periodic rectangular signal. Expression of the signal-to-noise ratio (SNR) has been obtained under the adiabatic limit. We investigate the effect of any system parameter (such as p, q, r, τ₁, τ₂) on the SNR. The plot of SNR-τ₁ shows SR for some values of the additive noise self-correlation time τ₂, but not for the whole range of τ₂. The system bias r suppresses the SNR. When the intensity of additive noise q is increased, the SR phenomenon disappears in the plot of SNR-p, but the plot of SNR-q presents SR for almost all values of the multiplicative noise intensity p.     

On different implementations of spatial evolutionary games

Evolutionary spatial 2×2 games between unconditional agents are analysed introducing several variants of cellular automata (CA).