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The problem of finding unusual time series has recently attracted much attention, and several promising methods are now in the literature. However, virtually all proposed methods assume that the data reside in main memory. For many real-world problems this is not be the case. For example , in astronomy, multi-terabyte time series datasets are the norm. Most(More)
Catalogs of periodic variable stars contain large numbers of periodic light-curves (photometric time series data from the astrophysics domain). Separating anomalous objects from well-known classes is an important step towards the discovery of new classes of astronomical objects. Most anomaly detection methods for time series data assume either a single(More)
Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface sulfur deposits. These deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada,(More)
hat do citation screening for evidence-based medicine and generating land-cover maps of the Earth have in common? Both are real-world problems for which we have applied machine-learning techniques to assist human experts, and in each case doing so has motivated the development of novel machine-learning methods. Our research group works closely with domain(More)
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