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