Can the initial singularity be detected by cosmological tests?

In the present paper we raise the question whether initial cosmological singularity can be proved from the cosmological tests. The classical general relativity predict the existence of singularity in the past if only some energy conditions are satisfied. On the other hand the latest quantum gravity applications to cosmology suggest of possibility of avoiding the singularity and replace it with the bounce. The distant type Ia supernovae data are used to constraints on bouncing evolutional scenario where square of the Hubble function $H^2$ is given by formulae $H^2=H^2_0[\Omega_{m,0}(1+z)^{m}-\Omega_{n,0}(1+z)^{n}]$, where $\Omega_{m,0}, \Omega_{n,0}>0$ are density parameters and $n>m>0$. We show that the on the base of the SNIa data standard bouncing models can be ruled out on the $4\sigma$ confidence level. If we add the cosmological constant to the standard bouncing model then we obtain as the best-fit that the parameter $\Omega_{n,0}$ is equal zero which means that the SNIa data do not support the bouncing term in the model. The bounce term is statistically insignificant the present epoch. We also demonstrate that BBN offer the possibility of obtaining stringent constraints of the extra term $\Omega_{n,0}$. The other observational test methods like CMB and the age of oldest objects in the Universe are used. We also use the Akaike informative criterion to select a model according to the goodness of fit and we conclude that this term should be ruled out by Occam's razor, which makes that the big bang is favored rather then bouncing scenario.Comment: 30 pages, 7 figures improved versio

Statistics of football dynamics

We investigate the dynamics of football matches. Our goal is to characterize statistically the temporal sequence of ball movements in this collective sport game, searching for traits of complex behavior. Data were collected over a variety of matches in South American, European and World championships throughout 2005 and 2006. We show that the statistics of ball touches presents power-law tails and can be described by $q$-gamma distributions. To explain such behavior we propose a model that provides information on the characteristics of football dynamics. Furthermore, we discuss the statistics of duration of out-of-play intervals, not directly related to the previous scenario.Comment: 7 page

Dynamic fluctuations in the superconductivity of NbN films from microwave conductivity measurements

We have measured the frequency and temperature dependences of complex ac conductivity, \sigma(\omega)=\sigma_1(\omega)-i\sigma_2(\omega), of NbN films in zero magnetic field between 0.1 to 10 GHz using a microwave broadband technique. In the vicinity of superconducting critical temperature, Tc, both \sigma_1(\omega) and \sigma_2(\omega) showed a rapid increase in the low frequency limit owing to the fluctuation effect of superconductivity. For the films thinner than 300 nm, frequency and temperature dependences of fluctuation conductivity, \sigma(\omega,T), were successfully scaled onto one scaling function, which was consistent with the Aslamazov and Larkin model for two dimensional (2D) cases. For thicker films, \sigma(\omega,T) data could not be scaled, but indicated that the dimensional crossover from three dimensions (3D) to 2D occurred as the temperature approached Tc from above. This provides a good reference of ac fluctuation conductivity for more exotic superconductors of current interest.Comment: 8 pages, 7 Figures, 1 Table, Accepted for publication in PR

Probability Models for Degree Distributions of Protein Interaction Networks

The degree distribution of many biological and technological networks has been described as a power-law distribution. While the degree distribution does not capture all aspects of a network, it has often been suggested that its functional form contains important clues as to underlying evolutionary processes that have shaped the network. Generally, the functional form for the degree distribution has been determined in an ad-hoc fashion, with clear power-law like behaviour often only extending over a limited range of connectivities. Here we apply formal model selection techniques to decide which probability distribution best describes the degree distributions of protein interaction networks. Contrary to previous studies this well defined approach suggests that the degree distribution of many molecular networks is often better described by distributions other than the popular power-law distribution. This, in turn, suggests that simple, if elegant, models may not necessarily help in the quantitative understanding of complex biological processes.

Model selection in High-Dimensions: A Quadratic-risk based approach

In this article we propose a general class of risk measures which can be used for data based evaluation of parametric models. The loss function is defined as generalized quadratic distance between the true density and the proposed model. These distances are characterized by a simple quadratic form structure that is adaptable through the choice of a nonnegative definite kernel and a bandwidth parameter. Using asymptotic results for the quadratic distances we build a quick-to-compute approximation for the risk function. Its derivation is analogous to the Akaike Information Criterion (AIC), but unlike AIC, the quadratic risk is a global comparison tool. The method does not require resampling, a great advantage when point estimators are expensive to compute. The method is illustrated using the problem of selecting the number of components in a mixture model, where it is shown that, by using an appropriate kernel, the method is computationally straightforward in arbitrarily high data dimensions. In this same context it is shown that the method has some clear advantages over AIC and BIC.Comment: Updated with reviewer suggestion

Detecting periodicity in experimental data using linear modeling techniques

Fourier spectral estimates and, to a lesser extent, the autocorrelation function are the primary tools to detect periodicities in experimental data in the physical and biological sciences. We propose a new method which is more reliable than traditional techniques, and is able to make clear identification of periodic behavior when traditional techniques do not. This technique is based on an information theoretic reduction of linear (autoregressive) models so that only the essential features of an autoregressive model are retained. These models we call reduced autoregressive models (RARM). The essential features of reduced autoregressive models include any periodicity present in the data. We provide theoretical and numerical evidence from both experimental and artificial data, to demonstrate that this technique will reliably detect periodicities if and only if they are present in the data. There are strong information theoretic arguments to support the statement that RARM detects periodicities if they are present. Surrogate data techniques are used to ensure the converse. Furthermore, our calculations demonstrate that RARM is more robust, more accurate, and more sensitive, than traditional spectral techniques.Comment: 10 pages (revtex) and 6 figures. To appear in Phys Rev E. Modified styl

Inferring Social Ties in Academic Networks Using Short-Range Wireless Communications

International audienceWiFi base stations are increasingly deployed in both public spaces and private companies, and the increase in their density poses a significant threat to the privacy of connected users. Prior studies have provided evidence that it is possible to infer the social ties of users from their location and co-location traces but they lack one important component: the comparison of the inference accuracy between an internal attacker (e.g., a curious application running on a mobile device) and a realistic external eavesdropper in the same field trial. In this paper, we experimentally show that such an eavesdropper is able to infer the type of social relationships between mobile users better than an internal attacker. Moreover, our results indicate that by exploiting the underlying social community structure of mobile users, the accuracy of the inference attacks doubles. Based on our findings, we propose countermeasures to help users protect their privacy against eavesdroppers