Superradiance: exploiting quantum phase transition in the real world

Superradiance (SR), or cooperative spontaneous emission, has been predicted by R. Dicke before the invention of the laser. During the last few years one can see a renaissance of both experimental and theoretical studies of the superradiant phase transition in a variety of media, ranging from quantum dots and Bose condensates through to black holes. Until recently, despite of many years of research, SR has been considered as a phenomenon of pure scientific interest without obvious potential applications. However, recent investigations of the femtosecond SR emission generation from semiconductors have opened up some practical opportunities for the exploitation of this quantum optics phenomenon. Here we present a brief review of some features, advantages and potential applications of the SR generation from semiconductor laser structures

Bayesian Online Changepoint Detection

Changepoints are abrupt variations in the generative parameters of a data sequence. Online detection of changepoints is useful in modelling and prediction of time series in application areas such as finance, biometrics, and robotics. While frequentist methods have yielded online filtering and prediction techniques, most Bayesian papers have focused on the retrospective segmentation problem. Here we examine the case where the model parameters before and after the changepoint are independent and we derive an online algorithm for exact inference of the most recent changepoint. We compute the probability distribution of the length of the current ``run,'' or time since the last changepoint, using a simple message-passing algorithm. Our implementation is highly modular so that the algorithm may be applied to a variety of types of data. We illustrate this modularity by demonstrating the algorithm on three different real-world data sets.