San Luis, Argentina

National University of San Luis

www.unsl.edu.ar/
San Luis, Argentina

The National University of San Luis is a public university in Argentina, with its seat in the city of San Luis, capital of the province of the same name, in the Cuyo region. It was created in 1973, along with the National University of San Juan, split off the National University of Cuyo based in Mendoza. Wikipedia.

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Alvarez E.,National University of San Luis | Alvarez E.,SLAC
Physical Review D - Particles, Fields, Gravitation and Cosmology | Year: 2012

We study how the significance of top quark induced charge asymmetries at the LHC may be enhanced exploiting the tt̄ total transverse momentum to enrich the fraction of quark-fusion events in the sample. We combine this variable with previous variables related to the boost and the invariant mass of the tt̄ pair to find an optimum cut which maximizes the significance of the asymmetry when systematic and statistic errors are taken into account. We find that including the tt̄ transverse momentum in the analysis of the expected 2012 LHC data provides a considerable enhancement in the significance of the asymmetry. © 2012 American Physical Society.


Costanza G.,National University of San Luis
Physica A: Statistical Mechanics and its Applications | Year: 2012

The proof of a theorem that allows one to construct deterministic evolution equations from a set, with two subsets, containing two types of discrete stochastic evolution equation is developed. One subset evolves Markovianly and the other non-Markovianly. As an illustrative example, the deterministic evolution equations of quantum electrodynamics are derived from two sets of Markovian and non-Markovian stochastic evolution equations, of different type, after an average over realization, using the theorem. This example shows that deterministic differential equations that contain both first-order and second-order time derivatives can be derived after a Taylor series expansion of the dynamical variables. It is shown that the derivation of such deterministic differential equations can be done by solving a set of linear equations. Two explicit examples, the first containing updating rules that depend on one previous time step and the second containing updating rules that depend on two previous time steps, are given in detail in order to show step by step the linear transformations that allow one to obtain the deterministic differential equations. © 2011 Elsevier B.V. All rights reserved.


The proof of a new extension of a theorem that allows to construct deterministic evolution equations from a set of discrete stochastic evolution equation is developed. The present extension allows to handle evolution equations of dynamical variables that are tensors of any rank. Due that the almost paradigmatic field that uses tensors is relativity, an illustrative example is given and the equations that allows to find the geodesics is derived from a set of discrete stochastic evolution equations. Extension to dynamical variables described by spinor indices or "arbitrary labels" are given.


Jara E.C.,National University of San Luis
Genetic Programming and Evolvable Machines | Year: 2011

Real-world time series have certain properties, such as stationarity, seasonality, linearity, among others, which determine their underlying behaviour. There is a particular class of time series called long-memory processes, characterized by a persistent temporal dependence between distant observations, that is, the time series values depend not only on recent past values but also on observations of much prior time periods. The main purpose of this research is the development, application, and evaluation of a computational intelligence method specifically tailored for long memory time series forecasting, with emphasis on manystep- ahead prediction. The method proposed here is a hybrid combining genetic programming and the fractionally integrated (long-memory) component of autoregressive fractionally integrated moving average (ARFIMA) models. Another objective of this study is the discovery of useful comprehensible novel knowledge, represented as time series predictive models. In this respect, a new evolutionary multi-objective search method is proposed to limit complexity of evolved solutions and to improve predictive quality. Using these methods allows for obtaining lower complexity (and possibly more comprehensible) models with high predictive quality, keeping run time and memory requirements low, and avoiding bloat and over-fitting. The methods are assessed on five real-world long memory time series and their performance is compared to that of statistical models reported in the literature. Experimental results show the proposed methods' advantages in long memory time series forecasting. © Springer Science+Business Media, LLC 2011.


Costanza G.,National University of San Luis
Physica A: Statistical Mechanics and its Applications | Year: 2014

Non-Markovian continuum stochastic and deterministic equations are derived from a set of discrete stochastic and deterministic evolution equations. Examples are given of discrete evolution equations whose updating rules depend on two or more previous time steps. Among them, the continuum stochastic evolution equation of the Newton second law, the stochastic evolution equation of a wave equation, the stochastic evolution equation for the scalar meson field, etc. are obtained as special cases. Extension to systems of evolution equations and other extensions are considered and examples are given. The concept of isomorphism and almost isomorphism are introduced in order to compare the coefficients of the continuum evolution equations of two different smoothing procedures that arise from two different approaches. Usually these discrepancies arising from two sources: On the one hand, the use of different representations of the generalized functions appearing in the models and, on the other hand, the different approaches used to describe the models. These new concept allows to overcome controversies that were appearing during decades in the literature. © 2014 Elsevier B.V.


Masuelli M.A.,National University of San Luis
International Journal of Biological Macromolecules | Year: 2011

Hydrodynamic properties are important parameters affecting the performance of pectin. This polysaccharide is used as a thickening and gelling agent in food and pharmaceutical industries. The most common and economical of the hydrodynamic properties is the determination of viscosity, in which are determined the intrinsic viscosity and the diffusion coefficient. They indirectly measure the molecular weight (Mw); hydrodynamic radius (RH); number of Simha, (ν(a/b)); Perrin parameter (P); Scheraga-Mandelkern parameter (β); and Flory parameters (φ0 and P0). All the hydrodynamic parameters are dependent on temperature. Normally these parameters are reported at a temperature of 25°C, which limits their application to different temperatures. This work studies pectin dependence on temperature, finding that this biopolymer in aqueous solution presents a conformation of rod-like with ν(a/b)=10.5, and a value from 0.8232 to 0.8129. Pectin behavior in this system indicates that it behaves like a colloidal particle that tends to compact with increasing temperature (RH decrease). The molecular weight calculated for pectin is 180,000g/mol. Mark-Houwink-Sakurada (M-H-S) equation constants, a and k, for pectin in water solvent-temperature systems have been already reported. © 2010 Elsevier B.V.


Roma F.,National University of San Luis
Physical Review B - Condensed Matter and Materials Physics | Year: 2010

The nonequilibrium dynamics of the three-dimensional Edwards-Anderson spin-glass model with different bond distributions is investigated by means of Monte Carlo simulation. A numerical method is used to determine the critical temperature and the scaling exponents of the correlation and the integrated response functions. The results obtained agree with those calculated in equilibrium simulations and suggest that the universality class does not depend on the exact form of the bond distribution. © 2010 The American Physical Society.


Costanza G.,National University of San Luis
Physica A: Statistical Mechanics and its Applications | Year: 2011

Deterministic evolution equations of classical as well as quantum mechanical models are derived from a set of non-Markovian stochastic evolution equations after an average over realization using a theorem. Examples are given, show that deterministic differential equations that contain derivatives with respect to time higher than or equal to two can be derived after a Taylor series expansion of the dynamical variables. It is shown that the derivation of such deterministic differential equations can be done by solving a set of linear equations that increase in number after increasing the number of previous time steps in the updating rules that define a given model. Two explicit examples, the first containing updating rules that depend on two previous time steps and the second on three, are worked in some detail in order to show some features of the linear transformation that allow one to obtain the deterministic differential equations. © 2011 Elsevier B.V. All rights reserved.


Costanza G.,National University of San Luis
Physica A: Statistical Mechanics and its Applications | Year: 2011

The deterministic evolution equations of classical as well as quantum mechanical models are derived from a set of stochastic evolution equations after taking an average over realizations using a theorem. Examples are given that show that deterministic quantum mechanical evolution equations, obtained initially by R.P. Feynman and subsequently studied by Boghosian and Taylor IV [B.M. Boghosian, W. Taylor IV, Phys. Rev. E 57 (1998) 54. See also arXiv:quant-ph/9904035] and Meyer [D.A. Meyer, Phys. Rev. E 55 (1997) 5261], among others, are derived from a set of stochastic evolution equations. In addition, a deterministic classical evolution equation for the diffusion of monomers, similar to the second Fick law, is also obtained. © 2010 Elsevier B.V. All rights reserved.


Carreno Jara E.,National University of San Luis
IEEE Transactions on Evolutionary Computation | Year: 2014

In this paper, a novel general class of optimality criteria is defined and proposed to solve multi-objective optimization problems by using evolutionary algorithms. These criteria, named p-optimality criteria, allow us to value (assess) the relative importance of those solutions with outstanding performance in very few objectives and poor performance in all others, regarding those solutions with an equilibrium (balance) among all the objectives. The optimality criteria avoid interrelating the relative values of the different objectives, respecting the integrity of each one in a rational way. As an example, a simple multi-objective approach based on the p-optimality criteria and genetic algorithms is designed, where solutions used to generate new solutions are selected according to the proposed optimality criteria. It is implemented and applied on several benchmark test problems, and its performance is compared to that of the nondominated sort genetic algorithm-II method, in order to analyze the contribution and potential of these new optimality criteria. © 1997-2012 IEEE.

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