Jeb Byers, Associate Professor of Zoology, travels to Australia

Professor James (Jeb) Byers spent the 2007-08 academic year in Australia conducting research on a highly invasive alga species

CIA and the Cold War Era

The purpose of this thesis is to explore the creation of the Central Intelligence Agency and its role in the Cold War. Great detail highlights the timeliness of the CIA’s creation and dynamic role over the years that followed its founding. For half a century, attempts to understand the Union of Soviet Socialist Republics dominated the CIA’s agenda. Thus, careful study of this era is important to understanding the progression of intelligence within the United States. The avenue of research for this thesis was a collaboration of published books, online journals, credible websites, and personal interviews. The development of the CIA consisted of much trial and error. Despite the blunders that the agency made, the CIA’s achievements would make its existence significantly worthwhile

Kinetic resolution of racemic {alpha}-olefins with ansa-zirconocene polymerization catalysts: Enantiomorphic site vs. chain end control

Copolymerization of racemic {alpha}-olefins with ethylene and propylene was carried out in the presence of enantiopure C1-symmetric ansa metallocene, {1,2-(SiMe2)2({eta}5-C5H-3,5-(CHMe2)2)({eta}5-C5H3)}ZrCl2 to probe the effect of the polymer chain end on enantioselection for the R- or S-{alpha}-olefin during the kinetic resolution by polymerization catalysis. Copolymerizations with ethylene revealed that the polymer chain end is an important factor in the enantioselection of the reaction and that for homopolymerization, chain end control generally works cooperatively with enantiomorphic site control. Results from propylene copolymerizations suggested that chain end control arising from a methyl group at the beta carbon along the main chain can drastically affect selectivity, but its importance as a stereo-directing element depends on the identity of the olefin

Improved high voltage insulator for use in vacuum

High voltage insulator for electron bombardment ion thruster has electric field directed through dielectric material and electrons emitted by field emission are constrained in negative junction region. Surface flashover and unstable operation are eliminated, and maximum voltage is limited only by dielectric strength of material, aluminum oxide in this case

TwitterMancer: Predicting Interactions on Twitter Accurately

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions. We have crawled an extensive dataset of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain "higher-dimensional" triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/. Keywords: Graph mining, machine learning, social media, social network

An X-ray diffraction study of the Paleocene-Eocene Flagstaff Formation in the Manti Canyon area of Wasatch Plateau, Central Utah

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TwitterMancer: predicting interactions on Twitter accurately

This paper investigates the interplay between different types of user interactions on Twitter, with respect to predicting missing or unseen interactions. For example, given a set of retweet interactions between Twitter users, how accurately can we predict reply interactions? Is it more difficult to predict retweet or quote interactions between a pair of accounts? Also, how important is time locality, and which features of interaction patterns are most important to enable accurate prediction of specific Twitter interactions? Our empirical study of Twitter interactions contributes initial answers to these questions.We have crawled an extensive data set of Greek-speaking Twitter accounts and their follow, quote, retweet, reply interactions over a period of a month. We find we can accurately predict many interactions of Twitter users. Interestingly, the most predictive features vary with the user profiles, and are not the same across all users. For example, for a pair of users that interact with a large number of other Twitter users, we find that certain “higher-dimensional” triads, i.e., triads that involve multiple types of interactions, are very informative, whereas for less active Twitter users, certain in-degrees and out-degrees play a major role. Finally, we provide various other insights on Twitter user behavior. Our code and data are available at https://github.com/twittermancer/.Accepted manuscrip