Matching Items (660)
Filtering by
- All Subjects: Songs (High voice) with piano
ContributorsWasbotten, Leia (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-30
Description
Libby Larsen is one of the most performed and acclaimed composers today. She is a spirited, compelling, and sensitive composer whose music enhances the poetry of America's most prominent authors. Notable among her works are song cycles for soprano based on the poetry of female writers, among them novelist and poet Willa Cather (1873-1947). Larsen has produced two song cycles on works from Cather's substantial output of fiction: one based on Cather's short story, "Eric Hermannson's Soul," titled Margaret Songs: Three Songs from Willa Cather (1996); and later, My Antonia (2000), based on Cather's novel of the same title. In Margaret Songs, Cather's poetry and short stories--specifically the character of Margaret Elliot--combine with Larsen's unique compositional style to create a surprising collaboration. This study explores how Larsen in these songs delves into the emotional and psychological depths of Margaret's character, not fully formed by Cather. It is only through Larsen's music and Cather's poetry that Margaret's journey through self-discovery and love become fully realized. This song cycle is a glimpse through the eyes of two prominent female artists on the societal pressures placed upon Margaret's character, many of which still resonate with women in today's culture. This study examines the work Margaret Songs by discussing Willa Cather, her musical influences, and the conditions surrounding the writing of "Eric Hermannson's Soul." It looks also into Cather's influence on Libby Larsen and the commission leading to Margaret Songs. Finally, a description of the musical, dramatic, and textual content of the songs completes this interpretation of the interactions of Willa Cather, Libby Larsen, and the character of Margaret Elliot.
ContributorsMcLain, Christi Marie (Author) / FitzPatrick, Carole (Thesis advisor) / Dreyfoos, Dale (Committee member) / Holbrook, Amy (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
Discovering Puerto Rican art song: a research project on four art song works by Héctor Campos Parsi
Description
Puerto Rico has produced many important composers who have contributed to the musical culture of the nation during the last 200 years. However, a considerable amount of their music has proven to be difficult to access and may contain numerous errors. This research project intends to contribute to the accessibility of such music and to encourage similar studies of Puerto Rican music. This study focuses on the music of Héctor Campos Parsi (1922-1998), one of the most prominent composers of the 20th century in Puerto Rico. After an overview of the historical background of music on the island and the biography of the composer, four works from his art song repertoire are given for detailed examination. A product of this study is the first corrected edition of his cycles Canciones de Cielo y Agua, Tres Poemas de Corretjer, Los Paréntesis, and the song Majestad Negra. These compositions date from 1947 to 1959, and reflect both the European and nationalistic writing styles of the composer during this time. Data for these corrections have been obtained from the composer's manuscripts, published and unpublished editions, and published recordings. The corrected scores are ready for publication and a compact disc of this repertoire, performed by soprano Melliangee Pérez and the author, has been recorded to bring to life these revisions. Despite the best intentions of the author, the various copyright issues have yet to be resolved. It is hoped that this document will provide the foundation for a resolution and that these important works will be available for public performance and study in the near future.
ContributorsRodríguez Morales, Luis F., 1980- (Author) / Campbell, Andrew (Thesis advisor) / Buck, Elizabeth (Committee member) / Holbrook, Amy (Committee member) / Kopta, Anne (Committee member) / Ryan, Russell (Committee member) / Arizona State University (Publisher)
Created2013
ContributorsYi, Joyce (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-22
ContributorsCummiskey, Hannah (Performer) / Kim, Olga (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-23
ContributorsEvans, Emily (Performer) / Sherrill, Amanda (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-02
Description
Implementing a distributed algorithm is more complicated than implementing a non-distributed algorithm. This is because distributed systems involve coordination of different processes each of which has a partial view of the global system state. The only way to share information in a distributed system is by message passing. Task that are straightforward in a non-distributed system, like deciding on the value of a global system state, can be quite complicated to achieve in a distributed system [1]. On top of the difficulties caused by the distributed nature of the computations, distributed systems typically need to be able to operate normally even if some of the nodes in the system are faulty which further adds to the uncertainty that processes have about the global state. Many factors make the implementation of a distributed algorithms difficult. Design patterns [2] are useful in simplifying the development of general algorithms. A design pattern describes a high level solution to a common, abstract problem that many systems may face. Common structural, creational, and behavioral problems are identified and elegantly solved by design patterns. By identifying features that an algorithm uses, and framing each feature as one of the common problems that a specific design pattern solves, designing a robust implementation of an algorithm becomes more manageable. In this way, design patterns can aid the implementation of algorithms. Unfortunately, design patterns are typically not discussed when developing distributed algorithms. Because correctly developing a distributed algorithm is difficult, many papers (eg. [1], [3], [4]) focus on verifying the correctness of the developed algorithm. Papers that are more practical ([5], [6]) establish the correctness of their algorithm and that their algorithm is efficient enough to be practical. However, papers on distributed algorithms usually make little mention of design patterns. The goal of this work was to gain experience implementing distributed systems including learning the application of design patterns and the application of related technical topics. This was achieved by implementing a currently unpublished algorithm that is tentatively called Bakery Consensus. Bakery Consensus is a replicated state-machine protocol that can tolerate servers with Byzantine faults, but assumes non-faulty clients. The algorithm also establishes non-skipping timestamps for each operation completed by the replicated state-machine. The design of the structure, communication, and creation of the different system parts depended heavily upon the book Design Patterns [2]. After implementing the system, the success of the in implementing its various parts was based upon their ability to satisfy the SOLID [7] principles as well as their ability to establish low coupling and high cohesion [8]. The rest of this paper is organized as follows. We begin by providing background information about distributed algorithms, including replicated state-machine protocols and the Bakery Consensus algorithm. Section 3 gives a background on several design patterns and software engineering principles that were used in the development process. Section 4 discusses the well designed parts of the system that used design patterns, and how these design patterns were chosen. Section 5 discusses well designed system parts that relied upon other technical topics. Section 6 discusses system parts that need redesign. The conclusion summarizes what was accomplished by the implementation process and the lessons learned about design patterns for distributed algorithms.
ContributorsStoutenburg, Tristan Kaleb (Author) / Bazzi, Rida (Thesis director) / Richa, Andrea (Committee member) / Computer Science and Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
ContributorsMartorana, Gabrielle (Performer) / Olarte, Aida (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-20
Description
Large-scale $\ell_1$-regularized loss minimization problems arise in high-dimensional applications such as compressed sensing and high-dimensional supervised learning, including classification and regression problems. In many applications, it remains challenging to apply the sparse learning model to large-scale problems that have massive data samples with high-dimensional features. One popular and promising strategy is to scaling up the optimization problem in parallel. Parallel solvers run multiple cores on a shared memory system or a distributed environment to speed up the computation, while the practical usage is limited by the huge dimension in the feature space and synchronization problems.
In this dissertation, I carry out the research along the direction with particular focuses on scaling up the optimization of sparse learning for supervised and unsupervised learning problems. For the supervised learning, I firstly propose an asynchronous parallel solver to optimize the large-scale sparse learning model in a multithreading environment. Moreover, I propose a distributed framework to conduct the learning process when the dataset is distributed stored among different machines. Then the proposed model is further extended to the studies of risk genetic factors for Alzheimer's Disease (AD) among different research institutions, integrating a group feature selection framework to rank the top risk SNPs for AD. For the unsupervised learning problem, I propose a highly efficient solver, termed Stochastic Coordinate Coding (SCC), scaling up the optimization of dictionary learning and sparse coding problems. The common issue for the medical imaging research is that the longitudinal features of patients among different time points are beneficial to study together. To further improve the dictionary learning model, I propose a multi-task dictionary learning method, learning the different task simultaneously and utilizing shared and individual dictionary to encode both consistent and changing imaging features.
In this dissertation, I carry out the research along the direction with particular focuses on scaling up the optimization of sparse learning for supervised and unsupervised learning problems. For the supervised learning, I firstly propose an asynchronous parallel solver to optimize the large-scale sparse learning model in a multithreading environment. Moreover, I propose a distributed framework to conduct the learning process when the dataset is distributed stored among different machines. Then the proposed model is further extended to the studies of risk genetic factors for Alzheimer's Disease (AD) among different research institutions, integrating a group feature selection framework to rank the top risk SNPs for AD. For the unsupervised learning problem, I propose a highly efficient solver, termed Stochastic Coordinate Coding (SCC), scaling up the optimization of dictionary learning and sparse coding problems. The common issue for the medical imaging research is that the longitudinal features of patients among different time points are beneficial to study together. To further improve the dictionary learning model, I propose a multi-task dictionary learning method, learning the different task simultaneously and utilizing shared and individual dictionary to encode both consistent and changing imaging features.
ContributorsLi, Qingyang (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Thesis advisor) / He, Jingrui (Committee member) / Wang, Yalin (Committee member) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2017