Fat-Tree Routing for Transit

The Transit network provides high-speed, low-latency, fault-tolerant interconnect for high-performance, multiprocessor computers. The basic connection scheme for Transit uses bidelta style, multistage networks to support up to 256 processors. Scaling to larger machines by simply extending the bidelta network topology will result in a uniform degradation of network latency between all processors. By employing a fat-tree network structure in larger systems, the network provides locality and universality properties which can help minimize the impact of scaling on network latency. This report details the topology and construction issues associated with integrating Transit routing technology into fat-tree interconnect topologies.

Surface Reflectance Recognition and Real-World Illumination Statistics

Humans distinguish materials such as metal, plastic, and paper effortlessly at a glance. Traditional computer vision systems cannot solve this problem at all. Recognizing surface reflectance properties from a single photograph is difficult because the observed image depends heavily on the amount of light incident from every direction. A mirrored sphere, for example, produces a different image in every environment. To make matters worse, two surfaces with different reflectance properties could produce identical images. The mirrored sphere simply reflects its surroundings, so in the right artificial setting, it could mimic the appearance of a matte ping-pong ball. Yet, humans possess an intuitive sense of what materials typically "look like" in the real world. This thesis develops computational algorithms with a similar ability to recognize reflectance properties from photographs under unknown, real-world illumination conditions. Real-world illumination is complex, with light typically incident on a surface from every direction. We find, however, that real-world illumination patterns are not arbitrary. They exhibit highly predictable spatial structure, which we describe largely in the wavelet domain. Although they differ in several respects from the typical photographs, illumination patterns share much of the regularity described in the natural image statistics literature. These properties of real-world illumination lead to predictable image statistics for a surface with given reflectance properties. We construct a system that classifies a surface according to its reflectance from a single photograph under unknown illuminination. Our algorithm learns relationships between surface reflectance and certain statistics computed from the observed image. Like the human visual system, we solve the otherwise underconstrained inverse problem of reflectance estimation by taking advantage of the statistical regularity of illumination. For surfaces with homogeneous reflectance properties and known geometry, our system rivals human performance.

Reinforcement Learning by Policy Search

One objective of artificial intelligence is to model the behavior of an intelligent agent interacting with its environment. The environment's transformations can be modeled as a Markov chain, whose state is partially observable to the agent and affected by its actions; such processes are known as partially observable Markov decision processes (POMDPs). While the environment's dynamics are assumed to obey certain rules, the agent does not know them and must learn. In this dissertation we focus on the agent's adaptation as captured by the reinforcement learning framework. This means learning a policy---a mapping of observations into actions---based on feedback from the environment. The learning can be viewed as browsing a set of policies while evaluating them by trial through interaction with the environment. The set of policies is constrained by the architecture of the agent's controller. POMDPs require a controller to have a memory. We investigate controllers with memory, including controllers with external memory, finite state controllers and distributed controllers for multi-agent systems. For these various controllers we work out the details of the algorithms which learn by ascending the gradient of expected cumulative reinforcement. Building on statistical learning theory and experiment design theory, a policy evaluation algorithm is developed for the case of experience re-use. We address the question of sufficient experience for uniform convergence of policy evaluation and obtain sample complexity bounds for various estimators. Finally, we demonstrate the performance of the proposed algorithms on several domains, the most complex of which is simulated adaptive packet routing in a telecommunication network.

On the Fabrication of Microparticles Using Electrohydrodynamic Atomization Method

A new approach for the control of the size of particles fabricated using the Electrohydrodynamic Atomization (EHDA) method is being developed. In short, the EHDA process produces solution droplets in a controlled manner, and as the solvent evaporates from the surface of the droplets, polymeric particles are formed. By varying the voltage applied, the size of the droplets can be changed, and consequently, the size of the particles can also be controlled. By using both a nozzle electrode and a ring electrode placed axisymmetrically and slightly above the nozzle electrode, we are able to produce a Single Taylor Cone Single Jet for a wide range of voltages, contrary to just using a single nozzle electrode where the range of permissible voltage for the creation of the Single Taylor Cone Single Jet is usually very small. Phase Doppler Particle Analyzer (PDPA) test results have shown that the droplet size increases with increasing voltage applied. This trend is predicted by the electrohydrodynamic theory of the Single Taylor Cone Single Jet based on a perfect dielectric fluid model. Particles fabricated using different voltages do not show much change in the particles size, and this may be attributed to the solvent evaporation process. Nevertheless, these preliminary results do show that this method has the potential of providing us with a way of fine controlling the particles size using relatively simple method with trends predictable by existing theories.