PIANO AS A PARTNER IN TWENTIETH­ CENTURY ART SONG REPERTOIRE: A RECORDING PROJECT

The piano's role in art song repertoire has evolved from a modest one during its formative years to the versatility, complexity and creativity found in the twentieth-century. The art song repertoire of the twentieth century is vast and has secured the reputation for being the most diverse, innovative, illustrative, atmospheric and colorful in all of art song literature. Within this time period, there are compositions that reach back to the romantic works of nineteenth century, others which combine old and new traditions, and finally those which adopt new means and new ends. In choosing the material for this project, I have focused on compositions with uniquely challenging and unusual piano accompaniments in order to achieve a balance between well- known and rarely performed works, as well as those pieces that combine various languages and styles. Selections range from Claude Debussy, Richard Strauss, Sergey Rachmaninoff, Ralph Vaughan Williams, Roger Quilter, Francis Poulenc, Fernando Obradors, and Joaquin Rodrigo to composers such as Samuel Barber, Marc Blitzstein, Dominick Argento, William Bolcom, and John Duke, including arrangements of traditional spirituals by Harry T. Burleigh and Florence B. Price, all of which helped to establish the American Art Song. My objective is to trace the development of the twentieth-century art song from the late Romantic Period through nationalistitrends to works which show the influences of jazz and folk elements. The two CD's for this dissertation recording project are available on compact discs which can be found in the Digital Repository at the University of Maryland (DRUM). The performers were Daniel Armstrong, baritone, Giles Herman, baritone, Thomas Glenn, tenor, Valerie Yinzant, soprano, Aaron Odom, tenor, Jennifer Royal, soprano, Kenneth Harmon, tenor, Karen Sorenson, soprano and Maxim Ivanov, baritone.

Image-Guided Precision Manipulation of Cells and Nanoparticles in Microfluidics

Manipulation of single cells and particles is important to biology and nanotechnology. Our electrokinetic (EK) tweezers manipulate objects in simple microfluidic devices using gentle fluid and electric forces under vision-based feedback control. In this dissertation, I detail a user-friendly implementation of EK tweezers that allows users to select, position, and assemble cells and nanoparticles. This EK system was used to measure attachment forces between living breast cancer cells, trap single quantum dots with 45 nm accuracy, build nanophotonic circuits, and scan optical properties of nanowires. With a novel multi-layer microfluidic device, EK was also used to guide single microspheres along complex 3D trajectories. The schemes, software, and methods developed here can be used in many settings to precisely manipulate most visible objects, assemble objects into useful structures, and improve the function of lab-on-a-chip microfluidic systems.

Rural Decay Almanac

Rural Decay Almanac is an exhibition comprised of sculptural objects and video/sound documentation. The following is an explanation of inspiration and personal history, a proposed schematic/manual for the objects in the gallery, and other contemporary artists I frame myself within. The front half of The Art Gallery at the University of Maryland as well as the atrium space directly outside the gallery hosts the work: four large scale Site-Responsive sculptural objects, and one video/sound loop projection. The library of materials comes from a farm site in Ijamsville, MD which has been re-purposed into the structures. As a sister work, the process of dismantling documentation is shown alongside the objects in a sound/video installation. The gallery space is transformed into a meticulously controlled environment via hard objects, sound, light, and video.

Generating Discriminative Objective Proposals via Submodular Ranking

Object recognition has long been a core problem in computer vision. To improve object spatial support and speed up object localization for object recognition, generating high-quality category-independent object proposals as the input for object recognition system has drawn attention recently. Given an image, we generate a limited number of high-quality and category-independent object proposals in advance and used as inputs for many computer vision tasks. We present an efficient dictionary-based model for image classification task. We further extend the work to a discriminative dictionary learning method for tensor sparse coding. In the first part, a multi-scale greedy-based object proposal generation approach is presented. Based on the multi-scale nature of objects in images, our approach is built on top of a hierarchical segmentation. We first identify the representative and diverse exemplar clusters within each scale. Object proposals are obtained by selecting a subset from the multi-scale segment pool via maximizing a submodular objective function, which consists of a weighted coverage term, a single-scale diversity term and a multi-scale reward term. The weighted coverage term forces the selected set of object proposals to be representative and compact; the single-scale diversity term encourages choosing segments from different exemplar clusters so that they will cover as many object patterns as possible; the multi-scale reward term encourages the selected proposals to be discriminative and selected from multiple layers generated by the hierarchical image segmentation. The experimental results on the Berkeley Segmentation Dataset and PASCAL VOC2012 segmentation dataset demonstrate the accuracy and efficiency of our object proposal model. Additionally, we validate our object proposals in simultaneous segmentation and detection and outperform the state-of-art performance. To classify the object in the image, we design a discriminative, structural low-rank framework for image classification. We use a supervised learning method to construct a discriminative and reconstructive dictionary. By introducing an ideal regularization term, we perform low-rank matrix recovery for contaminated training data from all categories simultaneously without losing structural information. A discriminative low-rank representation for images with respect to the constructed dictionary is obtained. With semantic structure information and strong identification capability, this representation is good for classification tasks even using a simple linear multi-classifier.