Automatically Discovering Hidden Transformation Chaining Constraints

Model transformations operate on models conforming to precisely defined metamodels. Consequently, it often seems relatively easy to chain them: the output of a transformation may be given as input to a second one if metamodels match. However, this simple rule has some obvious limitations. For instance, a transformation may only use a subset of a metamodel. Therefore, chaining transformations appropriately requires more information. We present here an approach that automatically discovers more detailed information about actual chaining constraints by statically analyzing transformations. The objective is to provide developers who decide to chain transformations with more data on which to base their choices. This approach has been successfully applied to the case of a library of endogenous transformations. They all have the same source and target metamodel but have some hidden chaining constraints. In such a case, the simple metamodel matching rule given above does not provide any useful information

Multiplier phenomenology in random multiplicative cascade processes

We demonstrate that the correlations observed in conditioned multiplier distributions of the energy dissipation in fully developed turbulence can be understood as an unavoidable artefact of the observation procedure. Taking the latter into account, all reported properties of both unconditioned and conditioned multiplier distributions can be reproduced by cascade models with uncorrelated random weights if their bivariate splitting function is non-energy conserving. For the alpha-model we show that the simulated multiplier distributions converge to a limiting form, which is very close to the experimentally observed one. If random translations of the observation window are accounted for, also the subtle effects found in conditioned multiplier distributions are precisely reproduced.Comment: 4 pages, 3 figure

Determining the Probability of Default of Agricultural Loans in a French Bank

Recently, financial institutions have developed improved internal risk rating systems and emphasized the probability of default and loss given default. Also they have been affected by globalization and it became important to understand the way foreign banks operate. The probability of default is studied for 756 loans from a French bank: CIC- Banque SNVB. A binomial logit regression is used to estimate a model of the probability of default of an agribusiness loan. The results show that leverage, profitability and liquidity at loan origination are good indicators of the probability of default. The loan length is another good indicator of the probability of default. Also it is more accurate to develop a model for each type of collateral (activity).Agricultural Finance,

The Markovian metamorphosis of a simple turbulent cascade model

Markovian properties of a discrete random multiplicative cascade model of log-normal type are discussed. After taking small-scale resummation and breaking of the ultrametric hierarchy into account, qualitative agreement with Kramers-Moyal coefficients, recently deduced from a fully developed turbulent flow, is achieved.Comment: 6 pages, 2 figure

Cumulant ratios in fully developed turbulence

In the context of random multiplicative cascade processes, we derive analytical solutions for one- and two-point cumulants with restored translational invariance. On taking ratios of cumulants in ln epsilon, geometrical effects due to spatial averaging cancel out. These ratios can successfully distinguish between splitting functions while multifractal scaling exponents and multiplier distributions cannot.Comment: 9th Workshop on Multiparticle Production (Torino), 9 pages latex, incl 9 figs and espcrc2.st

Thermal transport driven by charge imbalance in graphene in magnetic field, close to the charge neutrality point at low temperature: Non local resistance

Graphene grown epitaxially on SiC, close to the charge neutrality point (CNP), in an orthogonal magnetic field shows an ambipolar behavior of the transverse resistance accompanied by a puzzling longitudinal magnetoresistance. When injecting a transverse current at one end of the Hall bar, a sizeable non local transverse magnetoresistance is measured at low temperature. While Zeeman spin effect seems not to be able to justify these phenomena, some dissipation involving edge states at the boundaries could explain the order of magnitude of the non local transverse magnetoresistance, but not the asymmetry when the orientation of the orthogonal magnetic field is reversed. As a possible contribution to the explanation of the measured non local magnetoresistance which is odd in the magnetic field, we derive a hydrodynamic approach to transport in this system, which involves particle and hole Dirac carriers, in the form of charge and energy currents. We find that thermal diffusion can take place on a large distance scale, thanks to long recombination times, provided a non insulating bulk of the Hall bar is assumed, as recent models seem to suggest in order to explain the appearance of the longitudinal resistance. In presence of the local source, some leakage of carriers from the edges generates an imbalance of carriers of opposite sign, which are separated in space by the magnetic field and diffuse along the Hall bar generating a non local transverse voltage.Comment: 25 pages, 12 figure

Stochastic energy-cascade model for 1+1 dimensional fully developed turbulence

Geometrical random multiplicative cascade processes are often used to model positive-valued multifractal fields such as the energy dissipation in fully developed turbulence. We propose a dynamical generalization describing the energy dissipation in terms of a continuous and homogeneous stochastic field in one space and one time dimension. In the model, correlations originate in the overlap of the respective spacetime histories of field amplitudes. The theoretical two- and three-point correlation functions are found to be in good agreement with their equal-time counterparts extracted from wind tunnel turbulent shear flow data