THE GREAT RUSSIAN PIANO TRADITION: SELECTED REPERTOIRE BY TCHAIKOVSKY, RACHMANINOFF AND PROKOFIEV

This dissertation explores representative piano music by three great Russian composers: Tchaikovsky, Rachmaninoff and Prokofiev. The areas of research include: 1) the short character piece; 2) the Russian piano transcription tradition; 3) the concerto and sonata cycle; 4) extra-musical imagery; 5) the influence of popular and dance music of the period. Perhaps the most important result of this research is learning how the art of incorporating a singing quality at the piano stands at the center of Russian pianistic heritage. The first recital features compositions by Sergei Prokofiev. The Seventh Sonata exhibits rebellious, uncompromisingly dissonant treatment of its musical content. Ten Pieces from “Cinderella” shows an ascetic approach to piano texture - a common characteristic in Prokofiev’s late works. The Third Concerto is Prokofiev’s masterpiece in the genre. One of the 20th century’s most performed concerti, it overflows with pianistic challenges. For my second dissertation recital, I have chosen Peter Ilich Tchaikovsky’s The Seasons. These short character pieces were inspired by literary sources. The text portrays Russian rural life, nature, moments of intimate reflection, and imaginary experiences and impressions. Tchaikovsky’s gift as a melodist and remarkable musical individualist is represented in his two Nocturnes as well as in the Nutcracker Suite, masterfully transcribed by Mikhail Pletnev. The final program features Sergei Rachmaninoff’s Ten Preludes, Op. 23, regarded as a culmination of the turn-of-the-century grand Russian pianistic style. The Fantasy Pieces helped establish Rachmaninoff’s reputation as a pianist-composer, a profoundly lyrical poet of the piano. The three Rachmaninoff transcriptions, the Minuet, the Hopak and the Polka de W.R. preserve the spirit of the Golden Era’s musical salon. These pieces were written to delight and dazzle audiences with their bold character, musical taste, virtuosic tricks and technical finesse. The three recitals comprising this dissertation were presented in Gildenhorn Recital at the University of Maryland School of Music on November 13, 2010, April 11, 2011 and February 27, 2012. The recitals were recorded on compact discs and are archived within the Digital Repository at the University of Maryland (DRUM).

THE GREAT RUSSIAN PIANO TRADITION: SELECTED REPERTOIRE BY TCHAIKOVSKY, RACHMANINOFF AND PROKOFIEV

This dissertation explores representative piano music by three great Russian composers: Tchaikovsky, Rachmaninoff and Prokofiev. The areas of research include: 1) the short character piece; 2) the Russian piano transcription tradition; 3) the concerto and sonata cycle; 4) extra-musical imagery; 5) the influence of popular and dance music of the period. Perhaps the most important result of this research is learning how the art of incorporating a singing quality at the piano stands at the center of Russian pianistic heritage. The first recital features compositions by Sergei Prokofiev. The Seventh Sonata exhibits rebellious, uncompromisingly dissonant treatment of its musical content. Ten Pieces from "Cinderella" shows an ascetic approach to piano texture - a common characteristic in Prokofiev's late works. The Third Concerto is Prokofiev's masterpiece in the genre. One of the 20th century's most performed concerti, it overflows with pianistic challenges. For my second dissertation recital, I have chosen Peter Ilich Tchaikovsky's The Seasons. These short character pieces were inspired by literary sources. The text portrays Russian rural life, nature, moments of intimate reflection, and imaginary experiences and impressions. Tchaikovsky's gift as a melodist and remarkable musical individualist is represented in his two Nocturnes as well as in the Nutcracker Suite, masterfully transcribed by Mikhail Pletnev. The final program features Sergei Rachmaninoff's Ten Preludes, Op. 23, regarded as a culmination of the turn-of-the-century grand Russian pianistic style. The Fantasy Pieces helped establish Rachmaninoff's reputation as a pianist-composer, a profoundly lyrical poet of the piano. The three Rachmaninoff transcriptions, the Minuet, the Hopak and the Polka de W.R. preserve the spirit of the Golden Era's musical salon. These pieces were written to delight and dazzle audiences with their bold character, musical taste, virtuosic tricks and technical finesse. The three recitals comprising this dissertation were presented in Gildenhorn Recital at the University of Maryland School of Music on November 13, 2010, April 11, 2011 and February 27, 2012. The recitals were recorded on compact discs and are archived within the Digital Repository at the University of Maryland (DRUM).

Real-time analytics on large dynamic graphs

In today's fast-paced and interconnected digital world, the data generated by an increasing number of applications is being modeled as dynamic graphs. The graph structure encodes relationships among data items, while the structural changes to the graphs as well as the continuous stream of information produced by the entities in these graphs make them dynamic in nature. Examples include social networks where users post status updates, images, videos, etc.; phone call networks where nodes may send text messages or place phone calls; road traffic networks where the traffic behavior of the road segments changes constantly, and so on. There is a tremendous value in storing, managing, and analyzing such dynamic graphs and deriving meaningful insights in real-time. However, a majority of the work in graph analytics assumes a static setting, and there is a lack of systematic study of the various dynamic scenarios, the complexity they impose on the analysis tasks, and the challenges in building efficient systems that can support such tasks at a large scale. In this dissertation, I design a unified streaming graph data management framework, and develop prototype systems to support increasingly complex tasks on dynamic graphs. In the first part, I focus on the management and querying of distributed graph data. I develop a hybrid replication policy that monitors the read-write frequencies of the nodes to decide dynamically what data to replicate, and whether to do eager or lazy replication in order to minimize network communication and support low-latency querying. In the second part, I study parallel execution of continuous neighborhood-driven aggregates, where each node aggregates the information generated in its neighborhoods. I build my system around the notion of an aggregation overlay graph, a pre-compiled data structure that enables sharing of partial aggregates across different queries, and also allows partial pre-computation of the aggregates to minimize the query latencies and increase throughput. Finally, I extend the framework to support continuous detection and analysis of activity-based subgraphs, where subgraphs could be specified using both graph structure as well as activity conditions on the nodes. The query specification tasks in my system are expressed using a set of active structural primitives, which allows the query evaluator to use a set of novel optimization techniques, thereby achieving high throughput. Overall, in this dissertation, I define and investigate a set of novel tasks on dynamic graphs, design scalable optimization techniques, build prototype systems, and show the effectiveness of the proposed techniques through extensive evaluation using large-scale real and synthetic datasets.

STATISTICAL METHODS FOR ADVANCED ECONOMETRIC MODELS WITH APPLICATIONS TO VEHICLE HOLDING AND SPEED QUANTILES DISTRIBUTION

This dissertation proposes statistical methods to formulate, estimate and apply complex transportation models. Two main problems are part of the analyses conducted and presented in this dissertation. The first method solves an econometric problem and is concerned with the joint estimation of models that contain both discrete and continuous decision variables. The use of ordered models along with a regression is proposed and their effectiveness is evaluated with respect to unordered models. Procedure to calculate and optimize the log-likelihood functions of both discrete-continuous approaches are derived, and difficulties associated with the estimation of unordered models explained. Numerical approximation methods based on the Genz algortithm are implemented in order to solve the multidimensional integral associated with the unordered modeling structure. The problems deriving from the lack of smoothness of the probit model around the maximum of the log-likelihood function, which makes the optimization and the calculation of standard deviations very difficult, are carefully analyzed. A methodology to perform out-of-sample validation in the context of a joint model is proposed. Comprehensive numerical experiments have been conducted on both simulated and real data. In particular, the discrete-continuous models are estimated and applied to vehicle ownership and use models on data extracted from the 2009 National Household Travel Survey. The second part of this work offers a comprehensive statistical analysis of free-flow speed distribution; the method is applied to data collected on a sample of roads in Italy. A linear mixed model that includes speed quantiles in its predictors is estimated. Results show that there is no road effect in the analysis of free-flow speeds, which is particularly important for model transferability. A very general framework to predict random effects with few observations and incomplete access to model covariates is formulated and applied to predict the distribution of free-flow speed quantiles. The speed distribution of most road sections is successfully predicted; jack-knife estimates are calculated and used to explain why some sections are poorly predicted. Eventually, this work contributes to the literature in transportation modeling by proposing econometric model formulations for discrete-continuous variables, more efficient methods for the calculation of multivariate normal probabilities, and random effects models for free-flow speed estimation that takes into account the survey design. All methods are rigorously validated on both real and simulated data.