5,116 research outputs found
Cheaper and Better: Selecting Good Workers for Crowdsourcing
Crowdsourcing provides a popular paradigm for data collection at scale. We
study the problem of selecting subsets of workers from a given worker pool to
maximize the accuracy under a budget constraint. One natural question is
whether we should hire as many workers as the budget allows, or restrict on a
small number of top-quality workers. By theoretically analyzing the error rate
of a typical setting in crowdsourcing, we frame the worker selection problem
into a combinatorial optimization problem and propose an algorithm to solve it
efficiently. Empirical results on both simulated and real-world datasets show
that our algorithm is able to select a small number of high-quality workers,
and performs as good as, sometimes even better than, the much larger crowds as
the budget allows
Accelerating AdS black holes as the holographic heat engines in a benchmarking scheme
We investigate the properties of holographic heat engines with an uncharged
accelerating non-rotating AdS black hole as the working substance in a
benchmarking scheme. We find that the efficiencies of the black hole heat
engines can be influenced by both the size of the benchmark circular cycle and
the cosmic string tension as a thermodynamic variable. In general, the
efficiency can be increased by enlarging the cycle, but is still constrained by
a universal bound as expected. A cross-comparison of the
efficiencies of the accelerating black hole heat engines and Schwarzschild-AdS
black hole heat engines suggests that the acceleration also increases the
efficiency although the amount of increase is not remarkable.Comment: 13 pages,4 figure
Examining the cosmic acceleration with the latest Union2 supernova data
In this Letter, by reconstructing the diagnostic and the deceleration
parameter from the latest Union2 Type Ia supernova sample with and without
the systematic error along with the baryon acoustic oscillation (BAO) and the
cosmic microwave background (CMB), we study the cosmic expanding history, using
the Chevallier-Polarski-Linder (CPL) parametrization. We obtain that Union2+BAO
favor an expansion with a decreasing of the acceleration at . However,
once the CMB data is added in the analysis, the cosmic acceleration is found to
be still increasing, indicating a tension between low redshift data and high
redshift one. In order to reduce this tension significantly, two different
methods are considered and thus two different subsamples of Union2 are
selected. We then find that two different subsamples+BAO+CMB give completely
different results on the cosmic expanding history when the systematic error is
ignored, with one suggesting a decreasing cosmic acceleration, the other just
the opposite, although both of them alone with BAO support that the cosmic
acceleration is slowing down. However, once the systematic error is considered,
two different subsamples of Union2 along with BAO and CMB all favor an
increasing of the present cosmic acceleration. Therefore a clear-cut answer on
whether the cosmic acceleration is slowing down calls for more consistent data
and more reliable methods to analyze them.Comment: 17 pages, 6 figures; PLB in pres
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