Spinal cord injury therapy through active learning

Therapy employing epidural electrostimulation holds great potential for improving therapy for patients with spinal cord injury (SCI) (Harkema et al., 2011). Further promising results from combined therapies using electrostimulation have also been recently obtained (e.g., van den Brand et al., 2012). The devices being developed to deliver the stimulation are highly flexible, capable of delivering any individual stimulus among a combinatorially large set of stimuli (Gad et al., 2013). While this extreme flexibility is very useful for ensuring that the device can deliver an appropriate stimulus, the challenge of choosing good stimuli is quite substantial, even for expert human experimenters. To develop a fully implantable, autonomous device which can provide useful therapy, it is necessary to design an algorithmic method for choosing the stimulus parameters. Such a method can be used in a clinical setting, by caregivers who are not experts in the neurostimulator's use, and to allow the system to adapt autonomously between visits to the clinic. To create such an algorithm, this dissertation pursues the general class of active learning algorithms that includes Gaussian Process Upper Confidence Bound (GP-UCB, Srinivas et al., 2010), developing the Gaussian Process Batch Upper Confidence Bound (GP-BUCB, Desautels et al., 2012) and Gaussian Process Adaptive Upper Confidence Bound (GP-AUCB) algorithms. This dissertation develops new theoretical bounds for the performance of these and similar algorithms, empirically assesses these algorithms against a number of competitors in simulation, and applies a variant of the GP-BUCB algorithm in closed-loop to control SCI therapy via epidural electrostimulation in four live rats. The algorithm was tasked with maximizing the amplitude of evoked potentials in the rats' left tibialis anterior muscle. These experiments show that the algorithm is capable of directing these experiments sensibly, finding effective stimuli in all four animals. Further, in direct competition with an expert human experimenter, the algorithm produced superior performance in terms of average reward and comparable or superior performance in terms of maximum reward. These results indicate that variants of GP-BUCB may be suitable for autonomously directing SCI therapy.

Voting games with incomplete information

We examine voting situations in which individuals have incomplete information over each others' true preferences. In many respects, this work is motivated by a desire to provide a more complete understanding of so-called probabilistic voting.

Chapter 2 examines the similarities and differences between the incentives faced by politicians who seek to maximize expected vote share, expected plurality, or probability of victory in single member: single vote, simple plurality electoral systems. We find that, in general, the candidates' optimal policies in such an electoral system vary greatly depending on their objective function. We provide several examples, as well as a genericity result which states that almost all such electoral systems (with respect to the distributions of voter behavior) will exhibit different incentives for candidates who seek to maximize expected vote share and those who seek to maximize probability of victory.

In Chapter 3, we adopt a random utility maximizing framework in which individuals' preferences are subject to action-specific exogenous shocks. We show that Nash equilibria exist in voting games possessing such an information structure and in which voters and candidates are each aware that every voter's preferences are subject to such shocks. A special case of our framework is that in which voters are playing a Quantal Response Equilibrium (McKelvey and Palfrey (1995), (1998)). We then examine candidate competition in such games and show that, for sufficiently large electorates, regardless of the dimensionality of the policy space or the number of candidates, there exists a strict equilibrium at the social welfare optimum (i.e., the point which maximizes the sum of voters' utility functions). In two candidate contests we find that this equilibrium is unique.

Finally, in Chapter 4, we attempt the first steps towards a theory of equilibrium in games possessing both continuous action spaces and action-specific preference shocks. Our notion of equilibrium, Variational Response Equilibrium, is shown to exist in all games with continuous payoff functions. We discuss the similarities and differences between this notion of equilibrium and the notion of Quantal Response Equilibrium and offer possible extensions of our framework.

Nanophotonic Light Trapping In Thin Solar Cells

Over the last several decades there have been significant advances in the study and understanding of light behavior in nanoscale geometries. Entire fields such as those based on photonic crystals, plasmonics and metamaterials have been developed, accelerating the growth of knowledge related to nanoscale light manipulation. Coupled with recent interest in cheap, reliable renewable energy, a new field has blossomed, that of nanophotonic solar cells.

In this thesis, we examine important properties of thin-film solar cells from a nanophotonics perspective. We identify key differences between nanophotonic devices and traditional, thick solar cells. We propose a new way of understanding and describing limits to light trapping and show that certain nanophotonic solar cell designs can have light trapping limits above the so called ray-optic or ergodic limit. We propose that a necessary requisite to exceed the traditional light trapping limit is that the active region of the solar cell must possess a local density of optical states (LDOS) higher than that of the corresponding, bulk material. Additionally, we show that in addition to having an increased density of states, the absorber must have an appropriate incoupling mechanism to transfer light from free space into the optical modes of the device. We outline a portfolio of new solar cell designs that have potential to exceed the traditional light trapping limit and numerically validate our predictions for select cases.

We emphasize the importance of thinking about light trapping in terms of maximizing the optical modes of the device and efficiently coupling light into them from free space. To further explore these two concepts, we optimize patterns of superlattices of air holes in thin slabs of Si and show that by adding a roughened incoupling layer the total absorbed current can be increased synergistically. We suggest that the addition of a random scattering surface to a periodic patterning can increase incoupling by lifting the constraint of selective mode occupation associated with periodic systems.

Lastly, through experiment and simulation, we investigate a potential high efficiency solar cell architecture that can be improved with the nanophotonic light trapping concepts described in this thesis. Optically thin GaAs solar cells are prepared by the epitaxial liftoff process by removal from their growth substrate and addition of a metallic back reflector. A process of depositing large area nano patterns on the surface of the cells is developed using nano imprint lithography and implemented on the thin GaAs cells.