Representation of Minorities Displayed on College Webpages

Advertisements play an important role in nearly everyone\u27s life. Every single day people view a wide range of advertisements. With the population of America becoming more and more diverse in terms of race it would make sense that advertisements would reflect that same concept. However, it does not appear that advertisements actually reflect this idea. In the 2010 ads for the Super Bowl, only four out of the sixty-seven featured an African American in an ad. (Lapchick, 2010) Not only are minorities not being adequately represented in advertisements but they also only get featured for certain ads. African Americans find themselves featured in ads for beauty whereas Asians are found more in ads for technology. (Sheehan, 2014) There is obviously a misrepresentation of minorities in ads however, what is currently unknown is if this is true when it comes to college ads. Nearly every college, if not all, are advertising their schools so that more and more people will attend their school. The true question is whether or not college ads have continued with the mainstream ideals of advertising. A way to analyze this is by selecting 3 universities and 3 community or junior colleges by random and then looking at students presented on their website front page. Students who were being showcased on the website are students interacting with each other and are singled out for a variety of reasons. By comparing how many times a minority student appears in an advertisement versus what the overall percentage that minority group makes up at that specific school, one could determine whether or not that minority group is being adequately represented in ads. This is important because schools could possibly not be representing what their student demographics show. Just in all types of other advertisements this could affect how someone views a school and potentially how someone selects a school. This deeply impacts both the students and the schools

Are Brazilian corn farmers overconfident about prices?

This paper aims to identify signs of overconfidence among corn producers in south and central-west Brazil. Over the period from October to November 2008, farmers were chosen to answer questions regarding their knowledge of futures markets and price expectations. A large part of subjects responded they do not trade futures contracts because they do not have enough information on the subject. The results show respondents were miscalibrated when estimating directly-stated and the indirectly-stated expected prices. In addition, for certain respondents, subjective variance for corn proves to be significantly less than the market variance. Finally, the paper concludes that the overconfidence effect can partially explain the low use of futures markets by Brazilian corn producers.overconfidence, subjective probability, probability elicitation, Institutional and Behavioral Economics,

BRAIN COMPUTER INTERFACE - Application of an Adaptive Bi-stage Classifier based on RBF-HMM

Brain Computer Interface is an emerging technology that allows new output paths to communicate the users intentions without the use of normal output paths, such as muscles or nerves. In order to obtain their objective, BCI devices make use of classifiers which translate inputs from the users brain signals into commands for external devices. This paper describes an adaptive bi-stage classifier. The first stage is based on Radial Basis Function neural networks, which provides sequences of pre-assignations to the second stage, that it is based on three different Hidden Markov Models, each one trained with pre-assignation sequences from the cognitive activities between classifying. The segment of EEG signal is assigned to the HMMwith the highest probability of generating the pre-assignation sequence. The algorithm is tested with real samples of electroencephalografic signal, from five healthy volunteers using the cross-validation method. The results allow to conclude that it is possible to implement this algorithm in an on-line BCI device. The results also shown the huge dependency of the percentage of the correct classification from the user and the setup parameters of the classifier

Sharp weighted estimates for approximating dyadic operators

We give a new proof of the sharp weighted $L^2$ inequality ||T||_{L^2(w)} \leq c [w]_{A_2} where $T$ is the Hilbert transform, a Riesz transform, the Beurling-Ahlfors operator or any operator that can be approximated by Haar shift operators. Our proof avoids the Bellman function technique and two weight norm inequalities. We use instead a recent result due to A. Lerner to estimate the oscillation of dyadic operators.Comment: To appear in the Electronic Research Announcements in Mathematical Science