Neural Network Parameterizations of Electromagnetic Nucleon Form Factors

The electromagnetic nucleon form-factors data are studied with artificial feed forward neural networks. As a result the unbiased model-independent form-factor parametrizations are evaluated together with uncertainties. The Bayesian approach for the neural networks is adapted for chi2 error-like function and applied to the data analysis. The sequence of the feed forward neural networks with one hidden layer of units is considered. The given neural network represents a particular form-factor parametrization. The so-called evidence (the measure of how much the data favor given statistical model) is computed with the Bayesian framework and it is used to determine the best form factor parametrization.Comment: The revised version is divided into 4 sections. The discussion of the prior assumptions is added. The manuscript contains 4 new figures and 2 new tables (32 pages, 15 figures, 2 tables

Kaons production at finite temperature and baryon density in an effective relativistic mean field model

We investigate the kaons production at finite temperature and baryon density by means of an effective relativistic mean-field model with the inclusion of the full octet of baryons. Kaons are considered taking into account of an effective chemical potential depending on the self-consistent interaction between baryons. The obtained results are compared with a minimal coupling scheme, calculated for different values of the anti-kaon optical potential.Comment: 3 pages, contribution presented to the International Conference on Exotic Atoms and Related Topic

2016 European Guidelines on cardiovascular disease prevention in clinical practice

European Society of CardiologyThis is the author accepted manuscript. The final version is available from Oxford University Press via http://dx.doi.org/10.1093/eurheartj/ehw10