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- 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
Description
This dissertation presents three essays in economics. Firstly, I study the problem of allocating an indivisible good between two agents under incomplete information. I provide a characterization of mechanisms that maximize the sum of the expected utilities of the agents among all feasible strategy-proof mechanisms: Any optimal mechanism must be a convex combination of two fixed price mechanisms and two option mechanisms. Secondly, I study the problem of allocating a non-excludable public good between two agents under incomplete information. An equal-cost sharing mechanism which maximizes the sum of the expected utilities of the agents among all feasible strategy-proof mechanisms is proved to be optimal. Under the equal-cost sharing mechanism, when the built cost is low, the public good is provided whenever one of the agents is willing to fund it at half cost; when the cost is high, the public good is provided only if both agents are willing to fund it. Thirdly, I analyze the problem of matching two heterogeneous populations. If the payoff from a match exhibits complementarities, it is well known that absent any friction positive assortative matching is optimal. Coarse matching refers to a situation in which the populations into a finite number of classes, then randomly matched within these classes. The focus of this essay is the performance of coarse matching schemes with a finite number of classes. The main results of this essay are the following ones. First, assuming a multiplicative match payoff function, I derive a lower bound on the performance of n-class coarse matching under mild conditions on the distributions of agents' characteristics. Second, I prove that this result generalizes to a large class of match payoff functions. Third, I show that these results are applicable to a broad class of applications, including a monopoly pricing problem with incomplete information, as well as to a cost-sharing problem with incomplete information. In these problems, standard models predict that optimal contracts sort types completely. The third result implies that a monopolist can capture a large fraction of the second-best profits by offering pooling contracts with a small number of qualities.
ContributorsShao, Ran (Author) / Manelli, Alejandro (Thesis advisor) / Chade, Hector (Thesis advisor) / Schlee, Edward (Committee member) / Kovrijnykh, Natalia (Committee member) / Arizona State University (Publisher)
Created2011
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
Data privacy is emerging as one of the most serious concerns of big data analytics, particularly with the growing use of personal data and the ever-improving capability of data analysis. This dissertation first investigates the relation between different privacy notions, and then puts the main focus on developing economic foundations for a market model of trading private data.
The first part characterizes differential privacy, identifiability and mutual-information privacy by their privacy--distortion functions, which is the optimal achievable privacy level as a function of the maximum allowable distortion. The results show that these notions are fundamentally related and exhibit certain consistency: (1) The gap between the privacy--distortion functions of identifiability and differential privacy is upper bounded by a constant determined by the prior. (2) Identifiability and mutual-information privacy share the same optimal mechanism. (3) The mutual-information optimal mechanism satisfies differential privacy with a level at most a constant away from the optimal level.
The second part studies a market model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The value of epsilon units of privacy is measured by the minimum payment such that an individual's equilibrium strategy is to report data in an epsilon-differentially private manner. For the setting with binary private data that represents individuals' knowledge about a common underlying state, asymptotically tight lower and upper bounds on the value of privacy are established as the number of individuals becomes large, and the payment--accuracy tradeoff for learning the state is obtained. The lower bound assures the impossibility of using lower payment to buy epsilon units of privacy, and the upper bound is given by a designed reward mechanism. When the individuals' valuations of privacy are unknown to the data collector, mechanisms with possible negative payments (aiming to penalize individuals with "unacceptably" high privacy valuations) are designed to fulfill the accuracy goal and drive the total payment to zero. For the setting with binary private data following a general joint probability distribution with some symmetry, asymptotically optimal mechanisms are designed in the high data quality regime.
The first part characterizes differential privacy, identifiability and mutual-information privacy by their privacy--distortion functions, which is the optimal achievable privacy level as a function of the maximum allowable distortion. The results show that these notions are fundamentally related and exhibit certain consistency: (1) The gap between the privacy--distortion functions of identifiability and differential privacy is upper bounded by a constant determined by the prior. (2) Identifiability and mutual-information privacy share the same optimal mechanism. (3) The mutual-information optimal mechanism satisfies differential privacy with a level at most a constant away from the optimal level.
The second part studies a market model of trading private data, where a data collector purchases private data from strategic data subjects (individuals) through an incentive mechanism. The value of epsilon units of privacy is measured by the minimum payment such that an individual's equilibrium strategy is to report data in an epsilon-differentially private manner. For the setting with binary private data that represents individuals' knowledge about a common underlying state, asymptotically tight lower and upper bounds on the value of privacy are established as the number of individuals becomes large, and the payment--accuracy tradeoff for learning the state is obtained. The lower bound assures the impossibility of using lower payment to buy epsilon units of privacy, and the upper bound is given by a designed reward mechanism. When the individuals' valuations of privacy are unknown to the data collector, mechanisms with possible negative payments (aiming to penalize individuals with "unacceptably" high privacy valuations) are designed to fulfill the accuracy goal and drive the total payment to zero. For the setting with binary private data following a general joint probability distribution with some symmetry, asymptotically optimal mechanisms are designed in the high data quality regime.
ContributorsWang, Weina (Author) / Ying, Lei (Thesis advisor) / Zhang, Junshan (Thesis advisor) / Scaglione, Anna (Committee member) / Zhang, Yanchao (Committee member) / Arizona State University (Publisher)
Created2016
ContributorsMartorana, Gabrielle (Performer) / Olarte, Aida (Performer) / ASU Library. Music Library (Publisher)
Created2018-03-20