Alternating Tree Automata with Qualitative Semantics

We study alternating automata with qualitative semantics over infinite binary trees: Alternation means that two opposing players construct a decoration of the input tree called a run, and the qualitative semantics says that a run of the automaton is accepting if almost all branches of the run are accepting. In this article, we prove a positive and a negative result for the emptiness problem of alternating automata with qualitative semantics. The positive result is the decidability of the emptiness problem for the case of Büchi acceptance condition. An interesting aspect of our approach is that we do not extend the classical solution for solving the emptiness problem of alternating automata, which first constructs an equivalent non-deterministic automaton. Instead, we directly construct an emptiness game making use of imperfect information. The negative result is the undecidability of the emptiness problem for the case of co-Büchi acceptance condition. This result has two direct consequences: The undecidability of monadic second-order logic extended with the qualitative path-measure quantifier and the undecidability of the emptiness problem for alternating tree automata with non-zero semantics, a recently introduced probabilistic model of alternating tree automata

Numerical simulation of strongly nonlinear and dispersive waves using a Green-Naghdi model

We investigate here the ability of a Green-Naghdi model to reproduce strongly nonlinear and dispersive wave propagation. We test in particular the behavior of the new hybrid finite-volume and finite-difference splitting approach recently developed by the authors and collaborators on the challenging benchmark of waves propagating over a submerged bar. Such a configuration requires a model with very good dispersive properties, because of the high-order harmonics generated by topography-induced nonlinear interactions. We thus depart from the aforementioned work and choose to use a new Green-Naghdi system with improved frequency dispersion characteristics. The absence of dry areas also allows us to improve the treatment of the hyperbolic part of the equations. This leads to very satisfying results for the demanding benchmarks under consideration

Theoretical principles of petroleum hydrogeology of the West Siberian megabasin (WSMB)

Comprehensive study of the chemical and gas composition, temperatures, levels, pressure of deep underground water in deep wells is associated with the beginning of the systematic development of the oil and gas potential in Western Siberia and the first discovery of large deposits here. The development of new branches of hydrogeology is due to the fact of more and more available data. Thus, fundamental understandings of the WSMB hydrogeological conditions are being translated into new theories. Geodynamically, the WSMB structure was revised and based on hydrogeological data, regional and local prediction of oil and gas occurrence exploration criteria were developed. Based on the dispersion halo water-dissolved substance theory, exploration methodology of "neglected" deposits were formulated, conceptual issues of technogenic changes of oil and gas hydrogeosphere areas were being developed

Comparison of machine learning algorithms in restaurant revenue prediction

In this paper, we address several aspects of applying classical machine learning algorithms to a regression problem. We compare the predictive power to validate our approach on a data about revenue of a large Russian restaurant chain. We pay special attention to solve two problems: data heterogeneity and a high number of correlated features. We describe methods for considering heterogeneity—observations weighting and estimating models on subsamples. We define a weighting function via Mahalanobis distance in the space of features and show its predictive properties on following methods: ordinary least squares regression, elastic net, support vector regression, and random forest.</p

Analyse préliminaire de la valeur verte pour les logements

Analyse préliminaire de la valeur verte pour les logement

Dilepton production in heavy ion collisions at intermediate energies

We present a unified description of the vector meson and dilepton production in elementary and in heavy ion reactions. The production of vector mesons ($\rho,\omega$) is described via the excitation of nuclear resonances ($R$). The theoretical framework is an extended vector meson dominance model (eVMD). The treatment of the resonance decays $R\longmapsto NV$ with arbitrary spin is covariant and kinematically complete. The eVMD includes thereby excited vector meson states in the transition form factors. This ensures correct asymptotics and provides a unified description of photonic and mesonic decays. The resonance model is successfully applied to the $\omega$ production in $p+p$ reactions. The same model is applied to the dilepton production in elementary reactions ($p+p, p+d$). Corresponding data are well reproduced. However, when the model is applied to heavy ion reactions in the BEVALAC/SIS energy range the experimental dilepton spectra measured by the DLS Collaboration are significantly underestimated at small invariant masses. As a possible solution of this problem the destruction of quantum interference in a dense medium is discussed. A decoherent emission through vector mesons decays enhances the corresponding dilepton yield in heavy ion reactions. In the vicinity of the $\rho/\omega$-peak the reproduction of the data requires further a substantial collisional broadening of the $\rho$ and in particular of the $\omega$ meson.Comment: 32 pages revtex, 19 figures, to appear in PR

Technical note: development of a 3D printed subresolution sandwich phantom for validation of brain SPECT analysis

Purpose: To make an adaptable, head shaped radionuclide phantom to simulate molecular imaging of the brain using clinical acquisition and reconstruction protocols. This will allow the characterization and correction of scanner characteristics, and improve the accuracy of clinical image analysis, including the application of databases of normal subjects. Methods: A fused deposition modeling 3D printer was used to create a head shaped phantom made up of transaxial slabs, derived from a simulated MRI dataset. The attenuation of the printed polylactide (PLA), measured by means of the Hounsfield unit on CT scanning, was set to match that of the brain by adjusting the proportion of plastic filament and air (fill ratio). Transmission measurements were made to verify the attenuation of the printed slabs. The radionuclide distribution within the phantom was created by adding 99mTc pertechnetate to the ink cartridge of a paper printer and printing images of gray and white matter anatomy, segmented from the same MRI data. The complete subresolution sandwich phantom was assembled from alternate 3D printed slabs and radioactive paper sheets, and then imaged on a dual headed gamma camera to simulate an HMPAO SPECT scan. Results: Reconstructions of phantom scans successfully used automated ellipse fitting to apply attenuation correction. This removed the variability inherent in manual application of attenuation correction and registration inherent in existing cylindrical phantom designs. The resulting images were assessed visually and by count profiles and found to be similar to those from an existing elliptical PMMA phantom. Conclusions: The authors have demonstrated the ability to create physically realistic HMPAO SPECT simulations using a novel head-shaped 3D printed subresolution sandwich method phantom. The phantom can be used to validate all neurological SPECT imaging applications. A simple modification of the phantom design to use thinner slabs would make it suitable for use in PET