Sport Specific Drinking Motives Among Male Collegiate Hockey Players

Research shows that student athletes are consuming more alcohol than any other population on college campuses. Many studies have suggested that the culture of sport combined with the social setting and pressures of playing on a team put these athletes at a high risk for drinking heavily and suffering multiple alcohol related consequences. One area of research that has not been studied is the concept of sport specific drinking motives. Each individual sport has a unique culture and with it very diverse personalities. For this reason, we hypothesize that individual sports will likewise have unique drinking motives and expectations with regard to alcohol use. In particular, hockey players at UAF may drink for different reasons than the general UAF population

Minimal Reversible Nonsymmetric Rings

Marks showed that $\mathbb{F}_2Q_8$, the $\mathbb{F}_2$ group algebra over the quaternion group, is a reversible nonsymmetric ring, then questioned whether or not this ring is minimal with respect to cardinality. In this work, it is shown that the cardinality of a minimal reversible nonsymmetric ring is indeed 256. Furthermore, it is shown that although $\mathbb{F}_2Q_8$ is a duo ring, there are also examples of minimal reversible nonsymmetric rings which are nonduo

The light from our eyes

In which Max is horrified that 50% of American college students think their eyes illuminate the world. Orin thinks they may be on to something. Meanwhile, Freya is entranced by an expensive array of colored circles

Location-based technologies for learning

Emerging technologies for learning report - Article exploring location based technologies and their potential for educatio

Rates of convergence in active learning

We study the rates of convergence in generalization error achievable by active learning under various types of label noise. Additionally, we study the general problem of model selection for active learning with a nested hierarchy of hypothesis classes and propose an algorithm whose error rate provably converges to the best achievable error among classifiers in the hierarchy at a rate adaptive to both the complexity of the optimal classifier and the noise conditions. In particular, we state sufficient conditions for these rates to be dramatically faster than those achievable by passive learning.Comment: Published in at http://dx.doi.org/10.1214/10-AOS843 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org