Barriers to Entry in Monopoly Markets: Automobile Distribution in Brazil

O Objetivo deste Trabalho é Analisar os Efeitos da Entrada de uma Segunda Concessionária de Automóveis em Mercados Previamente Monopolizados. para Tanto, Construiu-Se um Banco de Dados com a Localização de Concessionárias de Automóveis em Microrregiões e Características Demográficas e Econômicas Destas Microrregiões. a Partir Desse Banco de Dados e de Modelos de Escolha Binária, Foram Identificadas Variáveis que Condicionam a Existência e o Número de Concessionárias em Microrregiões. Utilizando-Se de um Modelo Adaptado de Bresnahan e Reiss (1990), Foram Estimados os Custos Fixos de Entrada de Concessionárias em Mercados Monopolizados. os Resultados Obtidos Sugerem que as Barreiras À Entrada não são Significativas, o que Aumenta a Probabilidade de que a Cláusula de Exclusividade nos Contratos de Concessão não Cause Danos À Concorrência no Mercado Brasileiro de Distribuição de Automóveis

TactileGCN: A Graph Convolutional Network for Predicting Grasp Stability with Tactile Sensors

Tactile sensors provide useful contact data during the interaction with an object which can be used to accurately learn to determine the stability of a grasp. Most of the works in the literature represented tactile readings as plain feature vectors or matrix-like tactile images, using them to train machine learning models. In this work, we explore an alternative way of exploiting tactile information to predict grasp stability by leveraging graph-like representations of tactile data, which preserve the actual spatial arrangement of the sensor's taxels and their locality. In experimentation, we trained a Graph Neural Network to binary classify grasps as stable or slippery ones. To train such network and prove its predictive capabilities for the problem at hand, we captured a novel dataset of approximately 5000 three-fingered grasps across 41 objects for training and 1000 grasps with 10 unknown objects for testing. Our experiments prove that this novel approach can be effectively used to predict grasp stability

Asylum for Former Mexican Police Officers Persecuted by the Narcos

Since President Felipe Calderón declared war against Mexico’s narcotraffickers in 2006, drug violence has escalated and has claimed the lives of over 2000 Mexican police officers. To successfully petition for asylum in the United States, former Mexican police officers facing persecution by the Narcos must prove that they are members of a particular social group. In past cases, courts have refused to find that persecution by the Narcos qualifies a petitioner as a member of a particular social group. This Article argues, however, that former Mexican police officers facing persecution by the Narcos are members of a particular social group based on a shared past experience and should be granted asylum in the United States

Ethanol energy futures: Identifying hedge ratios, cointegration equations and price bubbles

Ethanol has been the subject of intense debate following the adoption of the Energy Policy Act of 2005 (EPAct) which established that the gasoline supply in the United States (U.S.) must contain 10% ethanol. The subsequent increase in the production of ethanol since 2005 has had an effect on the prices of corn, ethanol, and gasoline. This work seeks to identify hedging ratios using dynamic multivariate GARCH to best identify hedging opportunities in a newly developed futures market. In addition, Cointegration and Vector Error Models are used to identify relationships in price movements between ethanol futures, spot prices and related commodities. Principal findings include the identification of price relationships between futures and spot prices of ethanol as well as with other related commodities. Further, we apply the newly developed Generalized Suprema Augmented Dickey-Fuller (GSADF) methodology to identify both price bubbles in ethanol and corn prices and overlay the results to see if there are price periods where these periods overlap

Interactive 3D object recognition pipeline on mobile GPGPU computing platforms using low-cost RGB-D sensors

We propose the implementation of a 3D object recognition system which will be optimized to operate under demanding time constraints. The system must be robust so that objects can be recognized properly in poor light conditions and cluttered scenes with significant levels of occlusion. An important requirement must be met: The system must exhibit a reasonable performance running on a low power consumption mobile GPU computing platform (NVIDIA Jetson TK1) so that it can be integrated in mobile robotics systems, ambient intelligence or ambient-assisted living applications. The acquisition system is based on the use of color and depth (RGB-D) data streams provided by low-cost 3D sensors like Microsoft Kinect or PrimeSense Carmine. The resulting system is able to recognize objects in a scene in less than 7 seconds, offering an interactive frame rate and thus allowing its deployment on a mobile robotic platform. Because of that, the system has many possible applications, ranging from mobile robot navigation and semantic scene labeling to human–computer interaction systems based on visual information. A video showing the proposed system while performing online object recognition in various scenes is available on our project website (http://www.dtic.ua.es/~agarcia/3dobjrecog-jetsontk1/)

Meson spectroscopy at non-zero temperature using lattice QCD

This thesis explores two main topics: the effects of the temperature on several Quantum Chromodynamics mesonic observables, with a concrete focus on the tem-perature dependence of the mesonic mass spectrum, and numerical spectral recon-struction of lattice correlation functions employing deep neural networks. In the first two chapters, a brief introduction to standard lattice Quantum Chromodynamics and non-zero temperature field theory is provided. Using the tools presented in the intro-ductory chapters, a complete spectroscopy analysis of the temperature dependence of several mesonic ground state masses is developed. From this study, novel results in the restoration of chiral symmetry as a function of the temperature are obtained by studying the degree of degeneracy between the ρ(770) and a1(1260) states. Ad-ditionally, a complete study of the thermal effects affecting the mesonic D(s)-sector below the pseudocritical temperature of the system is provided. A self-contained chapter discussing the pion velocity in the medium is also included in the document. The pion velocity is estimated as a function of the temperature using non-zero tem-perature lattice Quantum Chromodynamics. In addition, after providing a detailed introduction to the field of neural networks, their application to numerical spectral reconstruction is studied. A simple implementation in which deep neural networks are applied to numerical spectral reconstruction is tested in order to explore its limits and applicability