21,593 research outputs found
Early Accurate Results for Advanced Analytics on MapReduce
Approximate results based on samples often provide the only way in which
advanced analytical applications on very massive data sets can satisfy their
time and resource constraints. Unfortunately, methods and tools for the
computation of accurate early results are currently not supported in
MapReduce-oriented systems although these are intended for `big data'.
Therefore, we proposed and implemented a non-parametric extension of Hadoop
which allows the incremental computation of early results for arbitrary
work-flows, along with reliable on-line estimates of the degree of accuracy
achieved so far in the computation. These estimates are based on a technique
called bootstrapping that has been widely employed in statistics and can be
applied to arbitrary functions and data distributions. In this paper, we
describe our Early Accurate Result Library (EARL) for Hadoop that was designed
to minimize the changes required to the MapReduce framework. Various tests of
EARL of Hadoop are presented to characterize the frequent situations where EARL
can provide major speed-ups over the current version of Hadoop.Comment: VLDB201
Information Content in Data Sets for a Nucleated-Polymerization Model
We illustrate the use of tools (asymptotic theories of standard error
quantification using appropriate statistical models, bootstrapping, model
comparison techniques) in addition to sensitivity that may be employed to
determine the information content in data sets. We do this in the context of
recent models [23] for nucleated polymerization in proteins, about which very
little is known regarding the underlying mechanisms; thus the methodology we
develop here may be of great help to experimentalists
Deep Residual Reinforcement Learning
We revisit residual algorithms in both model-free and model-based
reinforcement learning settings. We propose the bidirectional target network
technique to stabilize residual algorithms, yielding a residual version of DDPG
that significantly outperforms vanilla DDPG in the DeepMind Control Suite
benchmark. Moreover, we find the residual algorithm an effective approach to
the distribution mismatch problem in model-based planning. Compared with the
existing TD() method, our residual-based method makes weaker assumptions
about the model and yields a greater performance boost.Comment: AAMAS 202
ESTIMATION OF IMPERFECT COMPETITION IN FOOD MARKETING: A DYNAMIC ANALYSIS OF THE GERMAN BANANA MARKET
Several studies have estimated the welfare effects of recent changes in the European Union's common policy on banana imports, based upon the assumption that the market is perfectly competitive. However, if the market is imperfectly competitive, predictions about changes in banana policy may be inaccurate. The objective of this paper is to estimate the degree of market imperfection in the German market for banana imports using dynamic methods. The hypothesis that this market is perfectly competitive is rejected, and, in addition, the degree of market imperfection is estimated to be higher using a dynamic model compared to previous static estimates.Marketing,
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