University of Texas at Austin

About: University of Texas at Austin is a education organization based out in Austin, Texas, United States. It is known for research contribution in the topics: Population & Poison control. The organization has 94352 authors who have published 206297 publications receiving 9070052 citations. The organization is also known as: UT-Austin & UT Austin.

Evolution of a Species' Range

Abstract: Gene flow from the center of a species' range can stymie adaptation at the periphery and prevent the range from expanding outward. We study this process using simple models that track both demography and the evolution of a quantitative trait in a population that is continuously distributed in space. Stabilizing selection acts on the trait and favors an optimum phenotype that changes linearly across the habitat. One of three outcomes is possible: the species will become extinct, expand to fill all of the available habitat, or be confined to a limited range in which it is sufficiently adapted to allow population growth. When the environment changes rapidly in space, increased migration inhibits local adaptation and so decreases the species' total population size. Gene flow can cause enough maladaptation that the peripheral half of a species' range acts as a demographic sink. The trait's genetic variance has little effect on species persistence or the size of the range when gene flow is sufficiently...

Homomorphic Encryption from Learning with Errors: Conceptually-Simpler, Asymptotically-Faster, Attribute-Based

Abstract: We describe a comparatively simple fully homomorphic encryption (FHE) scheme based on the learning with errors (LWE) problem. In previous LWE-based FHE schemes, multiplication is a complicated and expensive step involving “relinearization”. In this work, we propose a new technique for building FHE schemes that we call the approximate eigenvector method. In our scheme, for the most part, homomorphic addition and multiplication are just matrix addition and multiplication. This makes our scheme both asymptotically faster and (we believe) easier to understand.