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- Genre: Academic theses
- Creators: Li, Baoxin
- Member of: Theses and Dissertations
Description
Whenever a text is transmitted, or communicated by any means, variations may occur because editors, copyists, and performers are often not careful enough with the source itself. As a result, a flawed text may come to be accepted in good faith through repetition, and may often be preferred over the authentic version because familiarity with the flawed copy has been established. This is certainly the case with regard to Manuel M. Ponce's guitar editions. An inexact edition of a musical work is detrimental to several key components of its performance: musical interpretation, aesthetics, and the original musical concept of the composer. These phenomena may be seen in the case of Manuel Ponce's Suite in D Major for guitar. The single published edition by Peer International Corporation in 1967 with the revision and fingering of Manuel López Ramos contains many copying mistakes and intentional, but unauthorized, changes to the original composition. For the present project, the present writer was able to obtain a little-known copy of the original manuscript of this work, and to document these discrepancies in order to produce a new performance edition that is more closely based on Ponce's original work.
ContributorsReyes Paz, Ricardo (Author) / Koonce, Frank (Thesis advisor) / Solis, Theodore (Committee member) / Rotaru, Catalin (Committee member) / Arizona State University (Publisher)
Created2013
Description
Social networking platforms have redefined communication, serving as conduits forswift global information dissemination on contemporary topics and trends. This research
probes information cascade (IC) dynamics, focusing on viral IC, where user-shared information
gains rapid, widespread attention. Implications of IC span advertising, persuasion,
opinion-shaping, and crisis response.
First, this dissertation aims to unravel the context behind viral content, particularly in
the realm of the digital world, introducing a semi-supervised taxonomy induction framework
(STIF). STIF employs state-of-the-art term representation, topical phrase detection,
and clustering to organize terms into a two-level topic taxonomy. Social scientists then
assess the topic clusters for coherence and completeness. STIF proves effective, significantly
reducing human coding efforts (up to 74%) while accurately inducing taxonomies
and term-to-topic mappings due to the high purity of its topics. Second, to profile the
drivers of virality, this study investigates messaging strategies influencing message virality.
Three content-based hypotheses are formulated and tested, demonstrating that incorporation
of “negativity bias,” “causal arguments,” and “threats to personal or societal core
values” - singularly and jointly - significantly enhances message virality on social media,
quantified by retweet counts. Furthermore, the study highlights framing narratives’ pivotal
role in shaping discourse, particularly in adversarial campaigns. An innovative pipeline
for automatic framing detection is introduced, and tested on a collection of texts on the
Russia-Ukraine conflict. Integrating representation learning, overlapping graph-clustering,
and a unique Topic Actor Graph (TAG) synthesis method, the study achieves remarkable
framing detection accuracy. The developed scoring mechanism maps sentences to automatically
detect framing signatures. This pipeline attains an impressive F1 score of 92%
and a 95% weighted accuracy for framing detection on a real-world dataset.
In essence, this dissertation focuses on the multidimensional exploration of information cascade, uncovering the context and drivers of content virality, and automating framing detection.
Through innovative methodologies like STIF, messaging strategy analysis, and
TAG Frames, the research contributes valuable insights into the mechanics of viral content
spread and framing nuances within the digital landscape, enriching fields such as advertisement,
communication, public discourse, and crisis response strategies.
ContributorsMousavi, Maryam (Author) / Davulcu, Hasan HD (Thesis advisor) / Li, Baoxin (Committee member) / Corman, Steven (Committee member) / McDaniel, Troy (Committee member) / Arizona State University (Publisher)
Created2023
Description
The current project is a study of five violin sonatas by the German Baroque composer Johann Georg Pisendel (1687-1755), arranged for guitar. The first part of the document is comprised of an overview of Pisendel's life and career as a virtuoso violinist, primarily focusing on his time of employment with the Dresden Hofkapelle during the Saxon-Polish Union. This section also examines the history and issues surrounding the Royal Court of Dresden's Schrank II (Cabinet II) music collection, which holds all of Pisendel's manuscripts. Although many of his works were previously lost or attributed wrongly to other composers, new research from the 2008 Deutsche Forschungsgemeinschaft (German Research Foundation) funded project: The Instrumental Music of the Dresden Hofkapelle at the Time of the Saxon-Polish Union aids in providing a comprehensive list and description of each of Pisendel's violin sonatas, either ascertained or conjectural. The second part contains arrangements of five selected violin sonatas for solo guitar. Together with the rationale pertaining to interpretive choices that were made in adapting each sonata for solo guitar, each work includes explanatory notes regarding its history and provenance. The analysis and arrangement of each sonata was conducted from facsimiles of the Schrank II manuscripts, which are currently available to the public through the Saxon State and University Library Dresden (SLUB) online database.
ContributorsFehser, Cheyne Cameron (Author) / Koonce, Frank (Thesis advisor) / Suzuki, Kotoka (Committee member) / Swartz, Jonathan (Committee member) / Arizona State University (Publisher)
Created2017
Description
This thesis presents robust and novel solutions using knowledge distillation with geometric approaches and multimodal data that can address the current challenges in deep learning, providing a comprehensive understanding of the learning process involved in knowledge distillation. Deep learning has attained significant success in various applications, such as health and wellness promotion, smart homes, and intelligent surveillance. In general, stacking more layers or increasing the number of trainable parameters causes deep networks to exhibit improved performance. However, this causes the model to become large, resulting in an additional need for computing and power resources for training, storage, and deployment. These are the core challenges in incorporating such models into small devices with limited power and computational resources. In this thesis, robust solutions aimed at addressing the aforementioned challenges are presented. These proposed methodologies and algorithmic contributions enhance the performance and efficiency of deep learning models. The thesis encompasses a comprehensive exploration of knowledge distillation, an approach that holds promise for creating compact models from high-capacity ones, while preserving their performance. This exploration covers diverse datasets, including both time series and image data, shedding light on the pivotal role of augmentation methods in knowledge distillation. The effects of these methods are rigorously examined through empirical experiments. Furthermore, the study within this thesis delves into the efficient utilization of features derived from two different teacher models, each trained on dissimilar data representations, including time-series and image data. Through these investigations, I present novel approaches to knowledge distillation, leveraging geometric techniques for the analysis of multimodal data. These solutions not only address real-world challenges but also offer valuable insights and recommendations for modeling in new applications.
ContributorsJeon, Eunsom (Author) / Turaga, Pavan (Thesis advisor) / Li, Baoxin (Committee member) / Lee, Hyunglae (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2023