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 $2\pi/(\pi+4)$ 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 $Om$ diagnostic and the deceleration parameter $q$ 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 $z<0.3$. 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