Two-step estimators of high-dimensional correlation matrices.

We investigate block diagonal and hierarchical nested stochastic multivariate Gaussian models by studying their sample cross-correlation matrix on high dimensions. By performing numerical simulations, we compare a filtered sample cross-correlation with the population cross-correlation matrices by using several rotationally invariant estimators (RIEs) and hierarchical clustering estimators (HCEs) under several loss functions. We show that at large but finite sample size, sample cross-correlations filtered by RIE estimators are often outperformed by HCE estimators for several of the loss functions.

Analysis of the Structure and Dynamics of European Flight Networks.

We analyze structure and dynamics of flight networks of 50 airlines active in the European airspace in 2017. Our analysis shows that the concentration of the degree of nodes of different flight networks of airlines is markedly heterogeneous among airlines reflecting heterogeneity of the airline business models. We obtain an unsupervised classification of airlines by performing a hierarchical clustering that uses a correlation coefficient computed between the average occurrence profiles of 4-motifs of airline networks as similarity measure.

Core of communities in bipartite networks.

We use the information present in a bipartite network to detect cores of communities of each set of the bipartite system. Cores of communities are found by investigating statistically validated projected networks obtained using information present in the bipartite network. Cores of communities are highly informative and robust with respect to the presence of errors or missing entries in the bipartite network.

An empirically grounded agent based model for modeling directs, conflict detection and resolution operations in air traffic management.

We present an agent based model of the Air Traffic Management socio-technical complex system aiming at modeling the interactions between aircraft and air traffic controllers at a tactical level. The core of the model is given by the conflict detection and resolution module and by the directs module. Directs are flight shortcuts that are given by air controllers to speed up the passage of an aircraft within a certain airspace and therefore to facilitate airline operations.

Bank-firm credit network in Japan: an analysis of a bipartite network.

We investigate the networked nature of the Japanese credit market. Our investigation is performed with tools of network science. In our investigation we perform community detection with an algorithm which is identifying communities composed of both banks and firms.

A comparative analysis of the statistical properties of large mobile phone calling networks.

Mobile phone calling is one of the most widely used communication methods in modern society. The records of calls among mobile phone users provide us a valuable proxy for the understanding of human communication patterns embedded in social networks. Mobile phone users call each other forming a directed calling network.

Multi-scale analysis of the European airspace using network community detection.

We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones.

Quantitative analysis of gender stereotypes and information aggregation in a national election.

By analyzing a database of a questionnaire answered by a large majority of candidates and elected in a parliamentary election, we quantitatively verify that (i) female candidates on average present political profiles which are more compassionate and more concerned with social welfare issues than male candidates and (ii) the voting procedure acts as a process of information aggregation. Our results show that information aggregation proceeds with at least two distinct paths. In the first case candidates characterize themselves with a political profile aiming to describe the profile of the majority of voters.

Happy aged people are all alike, while every unhappy aged person is unhappy in its own way.

Aging of the world's population represents one of the most remarkable success stories of medicine and of humankind, but it is also a source of various challenges. The aim of the collaborative cross-cultural European study of adult well being (ESAW) is to frame the concept of aging successfully within a causal model that embraces physical health and functional status, cognitive efficacy, material security, social support resources, and life activity. Within the framework of this project, we show here that the degree of heterogeneity among people who view aging in a positive light is significantly lower than the degree of heterogeneity of those who hold a negative perception of aging.

Statistically validated networks in bipartite complex systems.

Many complex systems present an intrinsic bipartite structure where elements of one set link to elements of the second set. In these complex systems, such as the system of actors and movies, elements of one set are qualitatively different than elements of the other set. The properties of these complex systems are typically investigated by constructing and analyzing a projected network on one of the two sets (for example the actor network or the movie network).

Role of conditional probability in multiscale stationary markovian processes.

The aim of the paper is to understand how the inclusion of more and more time scales into a stochastic stationary markovian process affects its conditional probability. To this end, we consider two gaussian processes: (i) a short-range correlated process with an infinite set of time scales bounded from above and (ii) a power-law correlated process with an infinite and unbounded set of time scales. For these processes we investigate the equal position conditional probability P(x,t∣x,0) and the mean first passage time Tx(Λ).

Exon deletions of the phenylalanine hydroxylase gene in Italian hyperphenylalaninemics.

A consistent finding of many studies describing the spectrum of mutant phenylalanine hydroxylase (PAH) alleles underlying hyperphenylalaninemia is the impossibility of achieving a 100% mutation ascertainment rate using conventional gene-scanning methods. These methods include denaturing gradient gel electrophoresis (DGGE), denaturing high performance liquid chromatography (DHPLC), and direct sequencing. In recent years, it has been shown that a significant proportion of undetermined alleles consist of large deletions overlapping one or more exons.

Modeling long-range memory with stationary Markovian processes.

In this paper we give explicit examples of long-range correlated stationary Markovian processes y(t) where the stationary probability density function (pdf) shows tails which are Gaussian or exponential. These processes are obtained by simply performing appropriate coordinate transformations of a specific power-law correlated additive process x(t) , already known in the literature, whose pdf shows power-law tails. We give analytical and numerical evidences that although the new processes are Markovian and have Gaussian or exponential tails, their autocorrelation function shows a power-law decay with logarithmic corrections.

Emergence of time-horizon invariant correlation structure in financial returns by subtraction of the market mode.

We investigate the emergence of a structure in the correlation matrix of assets' returns as the time horizon over which returns are computed increases from the minutes to the daily scale. We analyze data from different stock markets (New York, Paris, London, Milano) and with different methods. In addition to the usual correlations, we also analyze those obtained by subtracting the dynamics of the "center of mass" (i.

Scaling and data collapse for the mean exit time of asset prices.

We study theoretical and empirical aspects of the mean exit time (MET) of financial time series. The theoretical modeling is done within the framework of continuous time random walk. We empirically verify that the mean exit time follows a quadratic scaling law and it has associated a prefactor which is specific to the analyzed stock.