Systemic risk in dynamical networks with stochastic failure criterion

Complex non-linear interactions between banks and assets we model by two time-dependent Erdos-Renyi network models where each node, representing a bank, can invest either to a single asset (model I) or multiple assets (model II). We use a dynamical network approach to evaluate the collective financial failure -systemic risk- quantified by the fraction of active nodes. The systemic risk can be calculated over any future time period, divided into sub-periods, where within each sub-period banks may contiguously fail due to links to either i) assets or ii) other banks, controlled by two parameters, probability of internal failure p and threshold T-h ("solvency" parameter). The systemic risk decreases with the average network degree faster when all assets are equally distributed across banks than if assets are randomly distributed. The more inactive banks each bank can sustain (smaller T-h), the smaller the systemic risk -for some Th values in I we report a discontinuity in systemic risk. When contiguous spreading becomes stochastic ii) controlled by probability p(2) -a condition for the bank to be solvent (active) is stochasticthe- systemic risk decreases with decreasing p(2). We analyse the asset allocation for the U.S. banks. Copyright (C) EPLA, 2014

Hawking radiation from Elko particles tunnelling across black-strings horizon

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Asymmetrical Bloch branes and the hierarchy problem

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Effect of inter-adatoms correlations on the local density of states of graphene

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Organisation and tyrosine hydroxylase and calretinin immunoreactivity in the main olfactory bulb of paca (cuniculus paca): a large caviomorph rodent

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Thick braneworlds and the Gibbons-Kallosh-Linde no-go theorem in the Gauss-Bonnet framework

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Transient and d.c. analysis of the operation mechanism of light-emitting electrochemical cells

Light-emitting electrochemical cells (LECs) made of electroluminescent polymers were studied by d.c. and transient current-voltage and luminance-voltage measurements to elucidate the operation mechanisms of this kind of device. The time and external voltage necessary to form electrical double layers (EDLs) at the electrode interfaces could be determined from the results. In the low-and intermediate-voltage ranges (below 1.1 V), the ionic transport and the electronic diffusion dominate the current, being the device operation better described by an electrodynamic model. For higher voltages, electrochemical doping occurs, giving rise to the formation of a p-i-n junction, according to an electrochemical doping model. Copyright (C) EPLA, 2012

Complex networks analysis of language complexity

Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability. Copyright (c) EPLA, 2012

Critical behavior in a cross-situational lexicon learning scenario

The associationist account for early word learning is based on the co-occurrence between referents and words. Here we introduce a noisy cross-situational learning scenario in which the referent of the uttered word is eliminated from the context with probability gamma, thus modeling the noise produced by out-of-context words. We examine the performance of a simple associative learning algorithm and find a critical value of the noise parameter gamma(c) above which learning is impossible. We use finite-size scaling to show that the sharpness of the transition persists across a region of order tau(-1/2) about gamma(c), where tau is the number of learning trials, as well as to obtain the learning error (scaling function) in the critical region. In addition, we show that the distribution of durations of periods when the learning error is zero is a power law with exponent -3/2 at the critical point. Copyright (C) EPLA, 2012

Word sense disambiguation via high order of learning in complex networks

Complex networks have been employed to model many real systems and as a modeling tool in a myriad of applications. In this paper, we use the framework of complex networks to the problem of supervised classification in the word disambiguation task, which consists in deriving a function from the supervised (or labeled) training data of ambiguous words. Traditional supervised data classification takes into account only topological or physical features of the input data. On the other hand, the human (animal) brain performs both low- and high-level orders of learning and it has facility to identify patterns according to the semantic meaning of the input data. In this paper, we apply a hybrid technique which encompasses both types of learning in the field of word sense disambiguation and show that the high-level order of learning can really improve the accuracy rate of the model. This evidence serves to demonstrate that the internal structures formed by the words do present patterns that, generally, cannot be correctly unveiled by only traditional techniques. Finally, we exhibit the behavior of the model for different weights of the low- and high-level classifiers by plotting decision boundaries. This study helps one to better understand the effectiveness of the model. Copyright (C) EPLA, 2012

Resonances in Mie scattering by an inhomogeneous atomic cloud

Despite the quantum nature of the process, collective scattering by dense cold samples of two-level atoms can be interpreted classically describing the sample as a macroscopic object with a complex refractive index. We demonstrate that resonances in Mie theory can be easily observable in the cooperative scattering by tuning the frequency of the incident laser field or the atomic number. The solution of the scattering problem is obtained for spherical atomic clouds who have the parabolic density characteristic of BECs, and the cooperative radiation pressure force calculated exhibits resonances in the cloud displacement for dense clouds. At odds with uniform clouds which show a complex structure including narrow peaks, these densities show resonances, yet only under the form of quite regular and contrasted oscillations. Copyright (C) EPLA, 2012

Magnetic field induced charge redistribution in artificially disordered quantum Hall superlattices

The photoluminescence from individual quantum wells of artificially disordered weakly coupled multi-layers embedded in wide AlGaAs parabolic wells was investigated in a strong magnetic field. We show that the response of the individual wells is very different from the average response of the multi-layers studied by transport measurements and that photoluminescence represents a local probe of the quantum Hall state formed in three-dimensional electron system. The observed magnetic field induced variations of the in-layer electron density demonstrate the formation of a new phase in the quasi-three-dimensional electron system. The sudden change in the local electron density found at the Landau filling factor nu = 1 by both the magneto-transport and the magneto-photoluminescence measurements was assigned to the quantum phase transition. Copyright (C) EPLA, 2012

Subcritical fatigue in fuse networks

We obtain the Paris law of fatigue crack propagation in a fuse network model where the accumulated damage in each resistor increases with time as a power law of the local current amplitude. When a resistor reaches its fatigue threshold, it burns irreversibly. Over time, this drives cracks to grow until the system is fractured into two parts. We study the relation between the macroscopic exponent of the crack-growth rate -entering the phenomenological Paris law-and the microscopic damage accumulation exponent, gamma, under the influence of disorder. The way the jumps of the growing crack, Delta a, and the waiting time between successive breaks, Delta t, depend on the type of material, via gamma, are also investigated. We find that the averages of these quantities, <Delta a > and <Delta t >/< t(r)>, scale as power laws of the crack length a, <Delta a > proportional to a(alpha) and <Delta t >/< t(r)> proportional to a(-beta), where < t(r)> is the average rupture time. Strikingly, our results show, for small values of gamma, a decrease in the exponent of the Paris law in comparison with the homogeneous case, leading to an increase in the lifetime of breaking materials. For the particular case of gamma = 0, when fatigue is exclusively ruled by disorder, an analytical treatment confirms the results obtained by simulation. Copyright (C) EPLA, 2012

All-loop finiteness of the two-dimensional noncommutative supersymmetric gauge theory

Within the superfield approach, we discuss the two-dimensional noncommutative super-QED. Its all-order finiteness is explicitly shown. Copyright (C) EPLA, 2012

Unveiling the relationship between complex networks metrics and word senses

The automatic disambiguation of word senses (i.e., the identification of which of the meanings is used in a given context for a word that has multiple meanings) is essential for such applications as machine translation and information retrieval, and represents a key step for developing the so-called Semantic Web. Humans disambiguate words in a straightforward fashion, but this does not apply to computers. In this paper we address the problem of Word Sense Disambiguation (WSD) by treating texts as complex networks, and show that word senses can be distinguished upon characterizing the local structure around ambiguous words. Our goal was not to obtain the best possible disambiguation system, but we nevertheless found that in half of the cases our approach outperforms traditional shallow methods. We show that the hierarchical connectivity and clustering of words are usually the most relevant features for WSD. The results reported here shed light on the relationship between semantic and structural parameters of complex networks. They also indicate that when combined with traditional techniques the complex network approach may be useful to enhance the discrimination of senses in large texts. Copyright (C) EPLA, 2012