Senior Recital: Levi Vernon Cull, percussion

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Music Education. Mr. Cull studies percussion with John Lawless.https://digitalcommons.kennesaw.edu/musicprograms/1446/thumbnail.jp

Senior Recital: Katie Riess, trombone

This recital is presented in partial fulfillment of requirements for the degree Bachelor of Music in Performance. Ms. Riess studies trombone with Wes Funderburk.https://digitalcommons.kennesaw.edu/musicprograms/1194/thumbnail.jp

Senior Recital: Thomas Kieffer, alto saxophone and Tyler Elvidge, trumpet

This recital is presented in partial fulfillment of requirements for the degrees Bachelor of Music in Music Education. Mr. Kieffer studies saxophone with Sam Skelton. Mr. Elvidge studies trumpet with Douglas Lindsey.https://digitalcommons.kennesaw.edu/musicprograms/1179/thumbnail.jp

First-Year Spectroscopy for the SDSS-II Supernova Survey

This paper presents spectroscopy of supernovae discovered in the first season of the Sloan Digital Sky Survey-II Supernova Survey. This program searches for and measures multi-band light curves of supernovae in the redshift range z = 0.05 - 0.4, complementing existing surveys at lower and higher redshifts. Our goal is to better characterize the supernova population, with a particular focus on SNe Ia, improving their utility as cosmological distance indicators and as probes of dark energy. Our supernova spectroscopy program features rapid-response observations using telescopes of a range of apertures, and provides confirmation of the supernova and host-galaxy types as well as precise redshifts. We describe here the target identification and prioritization, data reduction, redshift measurement, and classification of 129 SNe Ia, 16 spectroscopically probable SNe Ia, 7 SNe Ib/c, and 11 SNe II from the first season. We also describe our efforts to measure and remove the substantial host galaxy contamination existing in the majority of our SN spectra.Comment: Accepted for publication in The Astronomical Journal(47pages, 9 figures

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.Peer reviewe

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency-Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical science. © The Author(s) 2019. Published by Oxford University Press