Are traffic violators criminals? Searching for answers in experiences of European countries

The connection between road traffic safety and criminal behavior has recently become a topic of interest in the literature, although little emphasis placed on the relationship with road accidents. Evidence worldwide shows that people who commit other offences characteristic of antisocial attitudes, are more prone to suffer road traffic accidents and infringe traffic laws. Here we examine the records of the 28 current member states of the European Union over the period 1999 - 2010. Our aim is to test the hypothesis that crime rates (and specifically, motor vehicle-related crimes) may be considered as predictors of fatal road traffic accidents. If they may, this could justify, at least prima facie, the tendency in several countries to consider traffic offences as crimes in their penal codes and to toughen the punishment imposed on those who commit them. We also analyze the effect of the severity of the legal system applied to traffic offences. Our results reveal that road traffic fatality rates are higher in countries whose inhabitants have more aggressive behavior, while the rates are lower in countries with more severe penal systems.

The internal wave field in Sau reservoir : Observation and modeling of a third vertical mode

Water withdrawal from Mediterranean reservoirs in summer is usually very high. Because of this, stratification is often continuous and far from the typical two-layered structure, favoring the excitation of higher vertical modes. The analysis of wind, temperature, and current data from Sau reservoir (Spain) shows that the third vertical mode of the internal seiche (baroclinic mode) dominated the internal wave field at the beginning of September 2003. We used a continuous stratification two-dimensional model to calculate the period and velocity distribution of the various modes of the internal seiche, and we calculated that the period of the third vertical mode is ;24 h, which coincides with the period of the dominating winds. As a result of the resonance between the third mode and the wind, the other oscillation modes were not excited during this period

Modeling and Classifying Breast Tissue Density in Mammograms

We present a new approach to model and classify breast parenchymal tissue. Given a mammogram, first, we will discover the distribution of the different tissue densities in an unsupervised manner, and second, we will use this tissue distribution to perform the classification. We achieve this using a classifier based on local descriptors and probabilistic Latent Semantic Analysis (pLSA), a generative model from the statistical text literature. We studied the influence of different descriptors like texture and SIFT features at the classification stage showing that textons outperform SIFT in all cases. Moreover we demonstrate that pLSA automatically extracts meaningful latent aspects generating a compact tissue representation based on their densities, useful for discriminating on mammogram classification. We show the results of tissue classification over the MIAS and DDSM datasets. We compare our method with approaches that classified these same datasets showing a better performance of our proposal