Why does Latin America Grow More Slowly?

In order to analyze how satisfactory the growth process in Latin America has been over the past 40 years it is important to make relevant comparisons with other experiences. To tackle this issue, the authors focus on the per capita economic growth rate and its contributing factors, comparing the experience of the typical country in Latin America (LAC) with that of benchmark countries, namely a typical country of the rest of the world (ROW) and of its subsets of developed countries (DEV) and East Asian countries (EASIA). They provide some econometric evidence suggesting that the worse institutional quality of Latin America relative to rest of the world, and to a lesser extent, the lower degree of openness and the higher degree of macroeconomic instability, were important factors behind these differences in productivity growth. The rest of the paper includes a description of economic performance of Latin America during the last four decades and a comparison it with the experience of the benchmark countries, accounting exercises in order to examine the contributions of various factors to the differences in performance observed, an econometric model to explore the role of policy and institutional variables as drivers of these contributions, and a conclusion.Economic Development & Growth, Region 1, Latin America

The Unruh Quantum Otto Engine

We introduce a quantum heat engine performing an Otto cycle by using the thermal properties of the quantum vacuum. Since Hawking and Unruh, it has been established that the vacuum space, either near a black hole or for an accelerated observer, behaves as a bath of thermal radiation. In this work, we present a fully quantum Otto cycle, which relies on the Unruh effect for a single quantum bit (qubit) in contact with quantum vacuum fluctuations. By using the notions of quantum thermodynamics and perturbation theory we obtain that the quantum vacuum can exchange heat and produce work on the qubit. Moreover, we obtain the efficiency and derive the conditions to have both a thermodynamic and a kinematic cycle in terms of the initial populations of the excited state, which define a range of allowed accelerations for the Unruh engine.Comment: 31 pages, 11 figure

Schema Independent Relational Learning

Learning novel concepts and relations from relational databases is an important problem with many applications in database systems and machine learning. Relational learning algorithms learn the definition of a new relation in terms of existing relations in the database. Nevertheless, the same data set may be represented under different schemas for various reasons, such as efficiency, data quality, and usability. Unfortunately, the output of current relational learning algorithms tends to vary quite substantially over the choice of schema, both in terms of learning accuracy and efficiency. This variation complicates their off-the-shelf application. In this paper, we introduce and formalize the property of schema independence of relational learning algorithms, and study both the theoretical and empirical dependence of existing algorithms on the common class of (de) composition schema transformations. We study both sample-based learning algorithms, which learn from sets of labeled examples, and query-based algorithms, which learn by asking queries to an oracle. We prove that current relational learning algorithms are generally not schema independent. For query-based learning algorithms we show that the (de) composition transformations influence their query complexity. We propose Castor, a sample-based relational learning algorithm that achieves schema independence by leveraging data dependencies. We support the theoretical results with an empirical study that demonstrates the schema dependence/independence of several algorithms on existing benchmark and real-world datasets under (de) compositions