TIMESCAPES: an architectural process of memory and adaptation

What if the architectural process of making could incorporate time? All designers who impact the physical environment- consciously and unconsciously are gatekeepers of the past, commentators of the present, and speculators of the future. This project proposes the creation of architecture and adaptive public space that looks to historical memories, foster present day cultural formation, and new alternative visions for the city of the future. The thesis asks what it means to design for stasis and change in a variety of scales- urban, architectural, and detail and arrives at a speculated new neighborhood, institutional buildings, and landscape. Central to this project is the idea of the architect as archeologist, anthropologist, and artist. The project focuses on a rapidly changing part of the city of Fort Worth, Texas and assigns a multipurpose institutional buildings and public space as a method of investigation. The thesis hopes to further architectural discourse about into the role of architecture in the preservation of memory, adaptive potential of public spaces, and the role of time in architecture.

Analysis of the effect of yoga on selective attention and mental concentration in young adults

Gemstone Team Om

Of Mice and Men: An Ergonomic and Market Assessment of Current Computer Mice

Gemstone Team MICE (Modifying and Improving Computer Ergonomics)

RICH AND EFFICIENT VISUAL DATA REPRESENTATION

Increasing the size of training data in many computer vision tasks has shown to be very effective. Using large scale image datasets (e.g. ImageNet) with simple learning techniques (e.g. linear classifiers) one can achieve state-of-the-art performance in object recognition compared to sophisticated learning techniques on smaller image sets. Semantic search on visual data has become very popular. There are billions of images on the internet and the number is increasing every day. Dealing with large scale image sets is intense per se. They take a significant amount of memory that makes it impossible to process the images with complex algorithms on single CPU machines. Finding an efficient image representation can be a key to attack this problem. A representation being efficient is not enough for image understanding. It should be comprehensive and rich in carrying semantic information. In this proposal we develop an approach to computing binary codes that provide a rich and efficient image representation. We demonstrate several tasks in which binary features can be very effective. We show how binary features can speed up large scale image classification. We present learning techniques to learn the binary features from supervised image set (With different types of semantic supervision; class labels, textual descriptions). We propose several problems that are very important in finding and using efficient image representation.

Action and perception: Neural indices of learning in infants

Early human development offers a unique perspective in investigating the potential cognitive and social implications of action and perception. Specifically, during infancy, action production and action perception undergo foundational developments. One essential component to examine developments in action processing is the analysis of others’ actions as meaningful and goal-directed. Little research, however, has examined the underlying neural systems that may be associated with emerging action and perception abilities, and infants’ learning of goal-directed actions. The current study examines the mu rhythm—a brain oscillation found in the electroencephalogram (EEG)—that has been associated with action and perception. Specifically, the present work investigates whether the mu signal is related to 9-month-olds’ learning of a novel goal-directed means-end task. The findings of this study demonstrate a relation between variations in mu rhythm activity and infants’ ability to learn a novel goal-directed means-end action task (compared to a visual pattern learning task used as a comparison task). Additionally, we examined the relations between standardized assessments of early motor competence, infants’ ability to learn a novel goal-directed task, and mu rhythm activity. We found that: 1a) mu rhythm activity during observation of a grasp uniquely predicted infants’ learning on the cane training task, 1b) mu rhythm activity during observation and execution of a grasp did not uniquely predict infants’ learning on the visual pattern learning task (comparison learning task), 2) infants’ motor competence did not predict infants’ learning on the cane training task, 3) mu rhythm activity during observation and execution was not related to infants’ measure of motor competence, and 4) mu rhythm activity did not predict infants’ learning on the cane task above and beyond infants’ motor competence. The results from this study demonstrate that mu rhythm activity is a sensitive measure to detect individual differences in infants’ action and perception abilities, specifically their learning of a novel goal-directed action.

A navigation and object location device for the blind

Gemstone Team Vision