11,926 research outputs found
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
- …