Controlled Perturbation is a framework for perturbing geometric sets to make the processes that use them more robust for fixed-precision manipulation. We present a Controlled Perturbation scheme for sets of line segments in IR (CPLS, for short). CPLS iteratively perturbs the endpoints of the line segments to eliminate potential degeneracies that may cause round-off errors when using fixed-preci...

Active learning identifies data points from a pool of unlabeled examples whose labels, if made available, are most likely to improve the predictions of a supervised model. Most research on active learning assumes that an agent has access to the entire pool of unlabeled data and can ask for labels of any data points during an initial training phase. However, when incorporated in a larger task, a...

Division-free arithmetic computations can be boiled down to summation due to Dekker/Veltkamp’s algorithm of 1971. The known double-precision numerical algorithms for summation are highly effective but limited by rounding errors. Our new summation algorithms relaxe this limitation, although they still almost entirely amount to double-precision additions. The efficiency of the algorithms is confi...