Neutrino oscillations in the front form of Hamiltonian dynamics

Since future, precise theory of neutrino oscillations should include the understanding of the neutrino mass generation and a precise, relativistic description of hadrons, and observing that such a future theory may require Dirac's FF of Hamiltonian dynamics, we provide a preliminary FF description of neutrino oscillations using the Feynman--Gell-Mann-Levy version of an effective theory in which leptons interact directly with whole nucleons and pions, instead of with quarks via intermediate bosons. The interactions are treated in the lowest-order perturbative expansion in the coupling constants in the effective theory, including a perturbative solution of the coupled constraint equations. Despite missing quarks and their binding mechanism, the effective Hamiltonian description is sufficiently precise for showing that the standard oscillation formula results from the interference of amplitudes with different neutrinos in virtual intermediate states. This holds provided that the inherent experimental uncertainties of preparing beams of incoming and measuring rates of production of outgoing particles are large enough for all of the different neutrino intermediate states to contribute as alternative virtual paths through which the long-baseline scattering process can manifest itself. The result that an approximate, effective FF theory reproduces the standard oscillation formula at the level of transition rates for currently considered long-baseline experiments--even though the space-time development of scattering is traced differently and the relevant interaction Hamiltonians are constructed differently than in the commonly used IF of dynamics--has two implications. It shows that the common interpretation of experimental results is not the only one, and it opens the possibility of considering more precise theories taking advantage of the features of the FF that are not available in the IF.Comment: revtex4, 10 page

The Spectatorship of Portraits by Naïve Beholders

The spectatorship of portraits by naïve viewers (beholders) was explored in a single experiment. Twenty-five participants rated their liking for 142 portraits painted by Courbet (36 paintings), Fantin-Latour (36 paintings) and Manet (70 paintings) on a 4-point Likert scale. The portraits were classified in terms of focussed versus ambiguous nature of sitter gaze and the presence of salient features in the context beyond sitters. Participants rated portraits while having their eye movements recorded. The portraits were split into regions of interest (ROIs) defined by faces, bodies and context. Participants also completed individual difference measures of attention and task focus. Results showed naïve spectatorship to be subject to attentional capture by faces. Paradoxically, the presence of salient features in the context amplified the attentional capture by faces through increasing participants liking of portraits. Attentional capture by faces was also influenced by sitter gaze and task focus. Unsurprisingly, the spectatorship of portraits by naïve beholders is dominated by faces, but the extent of this dominance is influenced by exogenous and endogenous attentional factors

Neutrino oscillations in the formal theory of scattering

Scattering theory in the Gell-Mann and Goldberger formulation is slightly extended to render a Hamiltonian quantum mechanical description of the neutrino oscillations.Comment: revtex4, 4 page

The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts

The 42nd Symposium Chromatographic Methods of Investigating Organic Compounds : Book of abstracts. June 4-7, 2019, Szczyrk, Polan

Nonparametric statistical analysis for multiple comparison of machine learning regression algorithms

In the paper we present some guidelines for the application of nonparametric statistical tests and post-hoc procedures devised to perform multiple comparisons of machine learning algorithms. We emphasize that it is necessary to distinguish between pairwise and multiple comparison tests. We show that the pairwise Wilcoxon test, when employed to multiple comparisons, will lead to overoptimistic conclusions. We carry out intensive normality examination employing ten different tests showing that the output of machine learning algorithms for regression problems does not satisfy normality requirements. We conduct experiments on nonparametric statistical tests and post-hoc procedures designed for multiple 1 x N and N x N comparisons with six different neural regression algorithms over 29 benchmark regression data sets. Our investigation proves the usefulness and strength of multiple comparison statistical procedures to analyse and select machine learning algorithms