4,260 research outputs found
Learning to Predict the Wisdom of Crowds
The problem of "approximating the crowd" is that of estimating the crowd's
majority opinion by querying only a subset of it. Algorithms that approximate
the crowd can intelligently stretch a limited budget for a crowdsourcing task.
We present an algorithm, "CrowdSense," that works in an online fashion to
dynamically sample subsets of labelers based on an exploration/exploitation
criterion. The algorithm produces a weighted combination of a subset of the
labelers' votes that approximates the crowd's opinion.Comment: Presented at Collective Intelligence conference, 2012
(arXiv:1204.2991
Reactive point processes: A new approach to predicting power failures in underground electrical systems
Reactive point processes (RPPs) are a new statistical model designed for
predicting discrete events in time based on past history. RPPs were developed
to handle an important problem within the domain of electrical grid
reliability: short-term prediction of electrical grid failures ("manhole
events"), including outages, fires, explosions and smoking manholes, which can
cause threats to public safety and reliability of electrical service in cities.
RPPs incorporate self-exciting, self-regulating and saturating components. The
self-excitement occurs as a result of a past event, which causes a temporary
rise in vulner ability to future events. The self-regulation occurs as a result
of an external inspection which temporarily lowers vulnerability to future
events. RPPs can saturate when too many events or inspections occur close
together, which ensures that the probability of an event stays within a
realistic range. Two of the operational challenges for power companies are (i)
making continuous-time failure predictions, and (ii) cost/benefit analysis for
decision making and proactive maintenance. RPPs are naturally suited for
handling both of these challenges. We use the model to predict power-grid
failures in Manhattan over a short-term horizon, and to provide a cost/benefit
analysis of different proactive maintenance programs.Comment: Published at http://dx.doi.org/10.1214/14-AOAS789 in the Annals of
Applied Statistics (http://www.imstat.org/aoas/) by the Institute of
Mathematical Statistics (http://www.imstat.org
Point-Defect Optical Transitions and Thermal Ionization Energies from Quantum Monte Carlo Methods: Application to F-center Defect in MgO
We present an approach to calculation of point defect optical and thermal
ionization energies based on the highly accurate quantum Monte Carlo methods.
The use of an inherently many-body theory that directly treats electron
correlation offers many improvements over the typically-employed density
functional theory Kohn-Sham description. In particular, the use of quantum
Monte Carlo methods can help overcome the band gap problem and obviate the need
for ad-hoc corrections. We demonstrate our approach to the calculation of the
optical and thermal ionization energies of the F-center defect in magnesium
oxide, and obtain excellent agreement with experimental and/or other
high-accuracy computational results
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