In deep sub-micron ICs, growing amounts of ondie memory and scaling effects make embedded memories increasingly vulnerable to reliability and yield problems. As scaling progresses...
Jangwoo Kim, Nikos Hardavellas, Ken Mai, Babak Fal...
In previous work, we have proposed a novel approach to data clustering based on the explicit optimization of a partitioning with respect to two complementary clustering objectives ...
This paper presents a practical technique to automatically compute approximations of polygonal representations of 3D objects. It is based on a previously developed model simplific...
Background: The availability of microarrays measuring thousands of genes simultaneously across hundreds of biological conditions represents an opportunity to understand both indiv...
Curtis Huttenhower, Avi I. Flamholz, Jessica N. La...
Clustering is one of the most widely used statistical tools for data analysis. Among all existing clustering techniques, k-means is a very popular method because of its ease of pr...