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The most critical step in maize (Zea mays ssp. mays) domestication was the liberation of the kernel from the hardened, protective casing that envelops the kernel in the maize progenitor, teosinte. This evolutionary step exposed the kernel on the surface of the ear, such that it could readily be used by humans as a food source. Here we show that this key(More)
Context-aware pervasive software is responsive to various contexts and their changes. A faulty implementation of the context-aware features may lead to unpredictable behavior with adverse effects. In software testing, one of the most important research issues is to determine the sufficiency of a test suite to verify the software under test. Existing(More)
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Advances in data collection and storage technologies have resulted in large and dynamically growing data sets at many organizations. Database and data mining researchers often use sampling with great effect to scale up performance on these data sets with small cost to accuracy. However, existing techniques often ignore the cost of computing a sample. This(More)
Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in(More)
teosinte glume architecture1 (tga1), a member of the SBP-box gene family of transcriptional regulators, has been identified as the gene conferring naked kernels in maize vs. encased kernels in its wild progenitor, teosinte. However, the identity of the causative polymorphism within tga1 that produces these different phenotypes has remained unknown. Using(More)
Over the past decade, advances in computational and sensor technology have enabled us to dynamically collect vast amounts of data from observations, health screening tests, simulations, and experiments at an ever-increasing pace. Knowledge discovery and data mining is an iterative process concerned with deriving interesting, non-obvious, and useful patterns(More)
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