**DARPA Open Catalog**

Welcome to the DARPA Open Catalog, which contains a curated list of DARPA-sponsored software and peer-reviewed publications. DARPA sponsors fundamental and applied research in a variety of areas that may lead to experimental results and reusable technology designed to benefit multiple government domains. The DARPA Open Catalog organizes publicly releasable material from DARPA programs. DARPA has an open strategy to help increase the impact of government investments. DARPA is interested in building communities around government-funded research. DARPA plans to continue to make available information generated by DARPA programs, including software, publications, data, and experimental results. The table on this page lists the programs currently participating in the catalog. … **Lloyd-Max**

In computer science and electrical engineering, Lloyd’s algorithm, also known as Voronoi iteration or relaxation, is an algorithm named after Stuart P. Lloyd for finding evenly spaced sets of points in subsets of Euclidean spaces, and partitions of these subsets into well-shaped and uniformly sized convex cells. Like the closely related k-means clustering algorithm, it repeatedly finds the centroid of each set in the partition, and then re-partitions the input according to which of these centroids is closest. However, Lloyd’s algorithm differs from k-means clustering in that its input is a continuous geometric region rather than a discrete set of points. Thus, when re-partitioning the input, Lloyd’s algorithm uses Voronoi diagrams rather than simply determining the nearest center to each of a finite set of points as the k-means algorithm does. Although the algorithm may be applied most directly to the Euclidean plane, similar algorithms may also be applied to higher-dimensional spaces or to spaces with other non-Euclidean metrics. Lloyd’s algorithm can be used to construct close approximations to centroidal Voronoi tessellations of the input, which can be used for quantization, dithering, and stippling. Other applications of Lloyd’s algorithm include smoothing of triangle meshes in the finite element method.

➚ “Compressive K-means” … **F-Measure**

In statistical analysis of binary classification, the F1 score (also F-score or F-measure) is a measure of a test’s accuracy. It considers both the precision p and the recall r of the test to compute the score: p is the number of correct positive results divided by the number of all positive results, and r is the number of correct positive results divided by the number of positive results that should have been returned. The F1 score can be interpreted as a weighted average of the precision and recall, where an F1 score reaches its best value at 1 and worst score at 0. The traditional F-measure or balanced F-score (F1 score) is the harmonic mean of precision and recall.

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