Doubt, for narrator and orchestra

Doubt is a single-movement composition of roughly twelve minutes for narrator and orchestra (woodwinds, horns, and trumpets in pairs, timpani, percussion, strings). The piece explores the controversial issue of capital punishment. The text was compiled from resources found on the websites of Death Penalty Information Center (http://www.deathpenaltyinfo.org) and Anti-Death Penalty Information (http://www.antideathpenalty.org), as well as excerpts from the Bible. Doubt was conceived of as a dramatic work in which a narrator recites factual information in a direct and unemotional manner and the orchestra provides a response to the mixed emotions elicited by the text. The list of dates and case summaries presented in the middle section of the piece seemed most powerful and effective when recited in a natural speaking voice, which is why I chose not to set the text as song. Also, I chose the orchestral medium rather than a chamber setting because the nature of the topic demanded a larger range of colors and combinations, as well as a louder, fuller sound. Much of the music was composed while deciding which texts to include. Thus the music influenced the choice of text as much as the text suggested the musical setting. The four formal divisions of the piece are delineated primarily by the text. The first section is an orchestral introduction representing various emotional perspectives suggested by the texts. The narrator begins the second section with a Biblical verse over sparse orchestration. The third and main section of the piece begins with a new melody in the low strings that is closely related to the harmonic organization of the piece. The narrator lists dates of convictions, executions, exonerations and facts related to doubtful cases. The third section and the narration conclude with another brief passage from the Bible. The fourth section is a dramatic orchestral coda, bringing back the opening harmonies of juxtaposed perfect fifths. The final chord is full of tension and discord, reflecting the oppositions inherent in the topic of capital punishment: life vs. death, sympathy vs. reproach, pain vs. hope, but above all, doubt about guilt vs. innocence.

Reflections on Children

In the Fall 2004, while preparing my dissertation project “Reflections on children,” I realized that whether art songs, operatic arias or scenes, the classical repertory offers a wide variety of masterpieces regarding all aspects of children’s life. My first dissertation recital is called “Journeys” and was performed on December 6th 2004 at 8:00pm in the Tawes Recital Hall. Change is the only inevitable condition in life and as such, it has been thought-provoking to many poets and composers. The narrator in Mahler’s Wayfarer Songs experiences the pain of love for the first time and emerges transformed into an adult. The narrator in Barber’s Knoxville is an adult looking back at his journey in an attempt to understand himself. The mothers in Britten’s Charm of Lullabies each make every effort to accept the changes in their own life after the birth of her baby. Finally, both Brahms’ songs are about risks and losses. “Destinies” is the title of the second dissertation recital as it attempts to capture the dark fate of Mignon and of Rückert/Mahler’s children. It was performed on April 8th 2005 in the Tawes Recital Hall. Mignon’s grief is portrayed through songs full of sadness and hopelessness in mourning of a long lost childhood. Rückert wrote 425 Kindertotenlieder in memory of his children’s deaths over a period of twenty years and Mahler orchestrated five of them. Mahler’s choices deal with light and darkness, symbolizing eternal life and hope versus despair and death, in both the literal and the literary sense. Finally, the third dissertation recital called “Blessings” was performed on May 19th 2005 in the Tawes Recital Hall. It relies on the evocation of moods and scenes created by a variety of composers and poets. Poulenc’s La Courte Paille are brief works in the style of a child’s song with unrelated and unexpected images juxtaposed, creating a sense of inner monologues. John Greer’s House of Tomorrow, Guastavino’s Cradle Songs and the rest of the composers offer wonderful pieces of immense power and expression in their directness and sensitive contour.

Security and Trust in Distributed Computation

We propose three research problems to explore the relations between trust and security in the setting of distributed computation. In the first problem, we study trust-based adversary detection in distributed consensus computation. The adversaries we consider behave arbitrarily disobeying the consensus protocol. We propose a trust-based consensus algorithm with local and global trust evaluations. The algorithm can be abstracted using a two-layer structure with the top layer running a trust-based consensus algorithm and the bottom layer as a subroutine executing a global trust update scheme. We utilize a set of pre-trusted nodes, headers, to propagate local trust opinions throughout the network. This two-layer framework is flexible in that it can be easily extensible to contain more complicated decision rules, and global trust schemes. The first problem assumes that normal nodes are homogeneous, i.e. it is guaranteed that a normal node always behaves as it is programmed. In the second and third problems however, we assume that nodes are heterogeneous, i.e, given a task, the probability that a node generates a correct answer varies from node to node. The adversaries considered in these two problems are workers from the open crowd who are either investing little efforts in the tasks assigned to them or intentionally give wrong answers to questions. In the second part of the thesis, we consider a typical crowdsourcing task that aggregates input from multiple workers as a problem in information fusion. To cope with the issue of noisy and sometimes malicious input from workers, trust is used to model workers' expertise. In a multi-domain knowledge learning task, however, using scalar-valued trust to model a worker's performance is not sufficient to reflect the worker's trustworthiness in each of the domains. To address this issue, we propose a probabilistic model to jointly infer multi-dimensional trust of workers, multi-domain properties of questions, and true labels of questions. Our model is very flexible and extensible to incorporate metadata associated with questions. To show that, we further propose two extended models, one of which handles input tasks with real-valued features and the other handles tasks with text features by incorporating topic models. Our models can effectively recover trust vectors of workers, which can be very useful in task assignment adaptive to workers' trust in the future. These results can be applied for fusion of information from multiple data sources like sensors, human input, machine learning results, or a hybrid of them. In the second subproblem, we address crowdsourcing with adversaries under logical constraints. We observe that questions are often not independent in real life applications. Instead, there are logical relations between them. Similarly, workers that provide answers are not independent of each other either. Answers given by workers with similar attributes tend to be correlated. Therefore, we propose a novel unified graphical model consisting of two layers. The top layer encodes domain knowledge which allows users to express logical relations using first-order logic rules and the bottom layer encodes a traditional crowdsourcing graphical model. Our model can be seen as a generalized probabilistic soft logic framework that encodes both logical relations and probabilistic dependencies. To solve the collective inference problem efficiently, we have devised a scalable joint inference algorithm based on the alternating direction method of multipliers. The third part of the thesis considers the problem of optimal assignment under budget constraints when workers are unreliable and sometimes malicious. In a real crowdsourcing market, each answer obtained from a worker incurs cost. The cost is associated with both the level of trustworthiness of workers and the difficulty of tasks. Typically, access to expert-level (more trustworthy) workers is more expensive than to average crowd and completion of a challenging task is more costly than a click-away question. In this problem, we address the problem of optimal assignment of heterogeneous tasks to workers of varying trust levels with budget constraints. Specifically, we design a trust-aware task allocation algorithm that takes as inputs the estimated trust of workers and pre-set budget, and outputs the optimal assignment of tasks to workers. We derive the bound of total error probability that relates to budget, trustworthiness of crowds, and costs of obtaining labels from crowds naturally. Higher budget, more trustworthy crowds, and less costly jobs result in a lower theoretical bound. Our allocation scheme does not depend on the specific design of the trust evaluation component. Therefore, it can be combined with generic trust evaluation algorithms.

The Cameraman

In this novel segment titled The Cameraman, a fictionalized version of silent film icon Buster Keaton grapples with his newfound role at big picture studio MGM, which has stripped him of his creative influence. For the first time in his career, Buster must operate with little authority in what was the beginning of the Hollywood studio system. This excerpt’s narrative present takes place in New York City in 1928 during the filming of The Cameraman, with flashbacks to Los Angeles in 1927, prior to the filming. The close third-person narrator examines what it means for Buster to be famous, to be an artist, and what happens when public and private identities are conflated.

Falling Out of the Sky

“Falling Out of the Sky” is a collection of poems, both formal and free verse, that explores an intimate familial landscape. In particular, these poems raise the question of what it means to be human through examinations of family mythology and its changes as bodies and memories become unreliable with time.

Sick Boy

This poetry collection explores the concepts of addiction and redemption. It does so through a series of vignette-style poems set in the Baltimore and DC area at the height of the heroin epidemic in the United States. Split into three parts, the first addresses the narrator’s initial drug use, the second follows the narrator at the strongest and least hopeful point of his addiction, and the third examines, through various scenes, the narrator’s attempts to find a life free from the confines of addiction. Although dealing with subject matter derived from dark and unfortunate circumstances, the narrator’s heroin addiction serves merely as a catalyst for the various situations that force the narrator to develop emotionally and grow even when trapped in the seemingly inescapable confines of addiction.

The Sugar Factory

Luke Banas is a young video artist who lives illegally in the disused Domino sugar refinery in Williamsburg, Brooklyn. While his art is an attempt to fully record and share his own life story, developers want to tear down the building where he works; a building that’s a monument to his hip neighborhood’s industrial past. The novel’s narrator, Lila Fairfax, is a journalist writing her first feature article about Luke and the fate of the factory. Observant and astute, she soon realizes that, despite his obsessive self-revelation, Luke is hiding a secret. Lila’s rational, detached approach to life is disrupted as, in the course of her reporting, she falls in love with Luke and as a result, learns far more than she anticipated. Though primarily a love story, The Sugar Factory is also an investigation of art, and art’s interaction with commerce, history, and new technology.

Self-Organizing Map Neural Architectures Based on Limit Cycle Attractors

Recent efforts to develop large-scale neural architectures have paid relatively little attention to the use of self-organizing maps (SOMs). Part of the reason is that most conventional SOMs use a static encoding representation: Each input is typically represented by the fixed activation of a single node in the map layer. This not only carries information in an inefficient and unreliable way that impedes building robust multi-SOM neural architectures, but it is also inconsistent with rhythmic oscillations in biological neural networks. Here I develop and study an alternative encoding scheme that instead uses limit cycle attractors of multi-focal activity patterns to represent input patterns/sequences. Such a fundamental change in representation raises several questions: Can this be done effectively and reliably? If so, will map formation still occur? What properties would limit cycle SOMs exhibit? Could multiple such SOMs interact effectively? Could robust architectures based on such SOMs be built for practical applications? The principal results of examining these questions are as follows. First, conditions are established for limit cycle attractors to emerge in a SOM through self-organization when encoding both static and temporal sequence inputs. It is found that under appropriate conditions a set of learned limit cycles are stable, unique, and preserve input relationships. In spite of the continually changing activity in a limit cycle SOM, map formation continues to occur reliably. Next, associations between limit cycles in different SOMs are learned. It is shown that limit cycles in one SOM can be successfully retrieved by another SOM’s limit cycle activity. Control timings can be set quite arbitrarily during both training and activation. Importantly, the learned associations generalize to new inputs that have never been seen during training. Finally, a complete neural architecture based on multiple limit cycle SOMs is presented for robotic arm control. This architecture combines open-loop and closed-loop methods to achieve high accuracy and fast movements through smooth trajectories. The architecture is robust in that disrupting or damaging the system in a variety of ways does not completely destroy the system. I conclude that limit cycle SOMs have great potentials for use in constructing robust neural architectures.