Matching Items (384)
Description
ABSTRACT Many musicians, both amateur and professional alike, are continuously seeking to expand and explore their performance literature and repertory. Introducing new works into the standard repertory is an exciting endeavor for any active musician. Establishing connections, commissioning new works, and collaborating on performances can all work together toward the acceptance and success of a composer's music within an instrument community. For the flute, one such composer is Daniel Dorff (b. 1956). Dorff, a Philadelphia-based composer, has written for symphony orchestra, clarinet, contrabassoon, and others; however, his award-winning works for flute and piccolo are earning him much recognition. He has written works for such illustrious flutists as Mimi Stillman, Walfrid Kujala, and Gary Schocker; his flute works have been recorded by Laurel Zucker, Pamela Youngblood and Lois Bliss Herbine; and his pieces have been performed and premiered at each of the National Flute Association Conventions from 2004 to 2009. Despite this success, little has been written about Dorff's life, compositional style, and contributions to the flute repertory. In order to further promote the flute works of Daniel Dorff, the primary focus of this study is the creation of a compact disc recording of Dorff's most prominent works for flute: April Whirlwind, 9 Walks Down 7th Avenue, both for flute and piano, and Nocturne Caprice for solo flute. In support of this recording, the study also provides biographical information regarding Daniel Dorff, discusses his compositional methods and ideology, and presents background information, description, and performance notes for each piece. Interviews with Daniel Dorff regarding biographical and compositional details serve as the primary source for this document. Suggestions for the performance of the three flute works were gathered through interviews with prominent flutists who have studied and performed Dorff's pieces. Additional performance suggestions for Nocturne Caprice were gathered through a coaching session between the author and the composer. This project is meant to promote the flute works of Daniel Dorff and to help establish their role in the standard flute repertory.
ContributorsRich, Angela Marie (Contributor) / Novak, Gail (Pianist) (Performer) / Buck, Elizabeth Y (Thesis advisor) / Hill, Gary W. (Committee member) / Holbrook, Amy (Committee member) / Schuring, Martin (Committee member) / Arizona State University (Publisher)
Created2010
Description
In the current millennium, extensive use of computers and the internet caused an exponential increase in information. Few research areas are as important as information extraction, which primarily involves extracting concepts and the relations between them from free text. Limitations in the size of training data, lack of lexicons and lack of relationship patterns are major factors for poor performance in information extraction. This is because the training data cannot possibly contain all concepts and their synonyms; and it contains only limited examples of relationship patterns between concepts. Creating training data, lexicons and relationship patterns is expensive, especially in the biomedical domain (including clinical notes) because of the depth of domain knowledge required of the curators. Dictionary-based approaches for concept extraction in this domain are not sufficient to effectively overcome the complexities that arise because of the descriptive nature of human languages. For example, there is a relatively higher amount of abbreviations (not all of them present in lexicons) compared to everyday English text. Sometimes abbreviations are modifiers of an adjective (e.g. CD4-negative) rather than nouns (and hence, not usually considered named entities). There are many chemical names with numbers, commas, hyphens and parentheses (e.g. t(3;3)(q21;q26)), which will be separated by most tokenizers. In addition, partial words are used in place of full words (e.g. up- and downregulate); and some of the words used are highly specialized for the domain. Clinical notes contain peculiar drug names, anatomical nomenclature, other specialized names and phrases that are not standard in everyday English or in published articles (e.g. "l shoulder inj"). State of the art concept extraction systems use machine learning algorithms to overcome some of these challenges. However, they need a large annotated corpus for every concept class that needs to be extracted. A novel natural language processing approach to minimize this limitation in concept extraction is proposed here using distributional semantics. Distributional semantics is an emerging field arising from the notion that the meaning or semantics of a piece of text (discourse) depends on the distribution of the elements of that discourse in relation to its surroundings. Distributional information from large unlabeled data is used to automatically create lexicons for the concepts to be tagged, clusters of contextually similar words, and thesauri of distributionally similar words. These automatically generated lexical resources are shown here to be more useful than manually created lexicons for extracting concepts from both literature and narratives. Further, machine learning features based on distributional semantics are shown to improve the accuracy of BANNER, and could be used in other machine learning systems such as cTakes to improve their performance. In addition, in order to simplify the sentence patterns and facilitate association extraction, a new algorithm using a "shotgun" approach is proposed. The goal of sentence simplification has traditionally been to reduce the grammatical complexity of sentences while retaining the relevant information content and meaning to enable better readability for humans and enhanced processing by parsers. Sentence simplification is shown here to improve the performance of association extraction systems for both biomedical literature and clinical notes. It helps improve the accuracy of protein-protein interaction extraction from the literature and also improves relationship extraction from clinical notes (such as between medical problems, tests and treatments). Overall, the two main contributions of this work include the application of sentence simplification to association extraction as described above, and the use of distributional semantics for concept extraction. The proposed work on concept extraction amalgamates for the first time two diverse research areas -distributional semantics and information extraction. This approach renders all the advantages offered in other semi-supervised machine learning systems, and, unlike other proposed semi-supervised approaches, it can be used on top of different basic frameworks and algorithms.
ContributorsJonnalagadda, Siddhartha Reddy (Author) / Gonzalez, Graciela H (Thesis advisor) / Cohen, Trevor A (Committee member) / Greenes, Robert A (Committee member) / Fridsma, Douglas B (Committee member) / Arizona State University (Publisher)
Created2011
Description
Contraceptive methods are vital in maintaining women’s health and preventing unintended pregnancy. When a woman uses a method that reflects her personal preferences and lifestyle, the chances of low adoption and misuse decreases. The research aim of this project is to develop a web-based decision aid tailored to college women that assists in the selection of contraceptive methods. For this reason, My Contraceptive Choice (MCC) is built using the gaps identified in existing resources provided by Planned Parenthood and Bedsider, along with feedback from a university student focus group. The tool is a short quiz that is followed by two pages of information and resources for a variety of different contraceptive methods commonly used by college women. The evaluation phase of this project includes simulated test cases, a Google Forms survey, and a second focus group to assess the tool for accuracy and usability. From the survey, 130 of the 150 (80.7%) responses believe that the recommendations provided can help them select a birth control method. Furthermore, 136 of the 150 (90.0%) responses believe that the layout of the tool made it easy to navigate. The second focus group feedback suggests that the MCC tool is perceived to be accurate, usable, and useful to the college population. Participants believe that the MCC tool performs better overall compared to the Planned Parenthood quiz in creating a customized recommendation and Bedsider in overall usability. The test cases reveal that there are further improvements that could be made to create a more accurate recommendation to the user. In conclusion, the new MCC tool accomplishes the aim of creating a beneficial resource to college women in assisting with the birth control selection process.
ContributorsRedman, Molly (Author) / Wang, Dongwen (Thesis director) / Brian, Jennifer (Committee member) / College of Health Solutions (Contributor) / Department of Information Systems (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
ContributorsChen, Zuoquan (Performer) / Jiang, Zhou (Performer) / ASU Library. Music Library (Publisher)
Created2018-04-02
ContributorsDavis, Kelly (Performer) / Novak, Gail (Pianist) (Performer) / ASU Library. Music Library (Publisher)
Created2018-11-18
ContributorsLee, Ji Young (Performer) / ASU Library. Music Library (Publisher)
Created2019-04-06
ContributorsBrouwer, Monique (Performer) / Hickman, Miriam, 1955- (Performer) / Schimming, Paul (Performer) / ASU Library. Music Library (Publisher)
Created2003-04-26
ContributorsCarter, Deborah (Performer) / Smith, William (Performer) / ASU Library. Music Library (Publisher)
Created1985-02-11
ContributorsChavis, Dawnya (Performer) / Baker, Dian (Performer) / ASU Library. Music Library (Publisher)
Created2008-04-20
ContributorsRobinson, Kiann (Performer) / Shin, Hyunok (Performer) / Hakes, Roy (Performer) / Sego, David (Performer) / Fearey, Mark (Performer) / ASU Library. Music Library (Publisher)
Created1995-04-06