La conveniencia de regular la responsabilidad solidaria de los titulares de plataformas digitales en las relaciones de consumo

El presente trabajo de investigación se sumerge en la problemática de la responsabilidad administrativa que asumen los denominados marketplaces con respecto a las diversas relaciones de consumo electrónicas que albergan en sus infraestructuras digitales, al cumplir un determinado rol en dicha relación subyacente. Con ese objetivo, analizamos diversos conceptos multidisciplinarios como el de sociedad de la información, el comercio electrónico, la economía digital, la economía colaborativa y las plataformas digitales de intermediación. Específicamente, nos enfocamos en la definición y naturaleza jurídica de los marketplaces como un tipo de plataforma de intermediación bastante popular actualmente en el mercado, para posteriormente realizar un estudio concreto de las principales plataformas de este tipo que operan frente a consumidores nacionales y los términos contractuales que rigen sus relaciones comerciales. Producto de este análisis logramos uniformizar los usos y costumbres de estas plataformas, derivados de su autonomía privada, en base de los cuales podemos determinar el rol que cumplen estos agentes dentro de las relaciones comerciales electrónicas subyacentes. La naturaleza de la participación de estos agentes nos ayuda a determinar el nivel de responsabilidad que deben asumir respecto a los incumplimientos contractuales y normativos que perjudiquen a los consumidores. Para ello, nos apoyamos en la doctrina, jurisprudencia y legislación nacional e internacional que ya ha desarrollado respecto de la responsabilidad de los agentes intermediarios en las relaciones electrónicas. Finalmente, producto del desarrollo de la investigación, se propone una modificación normativa al Código de Protección y Defensa del Consumidor, como hipótesis ante nuestro problema de investigación, con el objetivo de incluir a la figura de los operadores de marketplaces y de esta forma contribuir con el aumento de la confianza en el comercio electrónico

Fast identification of transits from light-curves

We present an algorithm that allows fast and efficient detection of transits, including planetary transits, from light-curves. The method is based on building an ensemble of fiducial models and compressing the data using the MOPED algorithm. We describe the method and demonstrate its efficiency by finding planet-like transits in simulated Pan-STARRS light-curves. We show that that our method is independent of the size of the search space of transit parameters. In large sets of light-curves, we achieve speed up factors of order of $10^{8}$ times over the full $\chi2$ search. We discuss how the algorithm can be used in forthcoming large surveys like Pan-STARRS and LSST and how it may be optimized for future space missions like Kepler and COROT where most of the processing must be done on board.Comment: 9 pages, 9 figure

Combined fit to the spectrum and composition data measured by the Pierre Auger Observatory including magnetic horizon effects

The measurements by the Pierre Auger Observatory of the energy spectrum and mass composition of cosmic rays can be interpreted assuming the presence of two extragalactic source populations, one dominating the flux at energies above a few EeV and the other below. To fit the data ignoring magnetic field effects, the high-energy population needs to accelerate a mixture of nuclei with very hard spectra, at odds with the approximate E$^{-2}$ shape expected from diffusive shock acceleration. The presence of turbulent extragalactic magnetic fields in the region between the closest sources and the Earth can significantly modify the observed CR spectrum with respect to that emitted by the sources, reducing the flux of low-rigidity particles that reach the Earth. We here take into account this magnetic horizon effect in the combined fit of the spectrum and shower depth distributions, exploring the possibility that a spectrum for the high-energy population sources with a shape closer to E$^{-2}$ be able to explain the observations

Extraction of the Muon Signals Recorded with the Surface Detector of the Pierre Auger Observatory Using Recurrent Neural Networks

We present a method based on the use of Recurrent Neural Networks to extract the muon component from the time traces registered with water-Cherenkov detector (WCD) stations of the Surface Detector of the Pierre Auger Observatory. The design of the WCDs does not allow to separate the contribution of muons to the time traces obtained from the WCDs from those of photons, electrons and positrons for all events. Separating the muon and electromagnetic components is crucial for the determination of the nature of the primary cosmic rays and properties of the hadronic interactions at ultra-high energies. We trained a neural network to extract the muon and the electromagnetic components from the WCD traces using a large set of simulated air showers, with around 450 000 simulated events. For training and evaluating the performance of the neural network, simulated events with energies between 1018.5, eV and 1020 eV and zenith angles below 60 degrees were used. We also study the performance of this method on experimental data of the Pierre Auger Observatory and show that our predicted muon lateral distributions agree with the parameterizations obtained by the AGASA collaboration