Conductance properties of rough quantum wires with colored surface disorder

Effects of correlated disorder on wave localization have attracted considerable interest. Motivated by the importance of studies of quantum transport in rough nanowires, here we examine how colored surface roughness impacts the conductance of two-dimensional quantum waveguides, using direct scattering calculations based on the reaction matrix approach. The computational results are analyzed in connection with a theoretical relation between the localization length and the structure factor of correlated disorder. We also examine and discuss several cases that have not been treated theoretically or are beyond the validity regime of available theories. Results indicate that conductance properties of quantum wires are controllable via colored surface disorder.Comment: 19 pages, 7 figure

Human Reinforcement Learning: Insights from intracranial recordings and stimulation

Reinforcement learning is the process by which individuals alter their decisions to maximize positive outcomes, and minimize negative outcomes. It is a cognitive process that is widely used in our daily lives and is often disrupted during psychiatric disease. Thus, a major goal of neuroscience is to characterize the neural underpinnings of reinforcement learning. Whereas animal studies have utilized invasive physiological methods to characterize several neural mechanisms that underlie reinforcement learning, human studies have largely relied on non-invasive techniques that have reduced physiological precision. Although ethical limitations preclude the use of invasive physiological methods in healthy human populations, patient populations undergoing certain neurosurgical interventions offer a rare opportunity to directly assay neural activity from the brain during human reinforcement learning. This dissertation presents early findings from this research effort

Prices and Price Dispersion on the Web: Evidence from the Online Book Industry

Using data collected between August 1999 and January 2000 covering 399 books, including New York Times bestsellers, computer bestsellers, and random books, we examine pricing by thirty-two online bookstores. One common prediction is that the reduction in search costs on the Internet relative to the physical channel would cause both price and price dispersion to fall. Over the sample period, we find no change in either price or price dispersion. Another prediction of the search literature is that the prices and price dispersion of advertised items or items that are purchased repeatedly will be lower than for unadvertised or infrequently purchased items. Prices across categories of books appear to conform to this prediction, with New York Times bestsellers having the lowest prices as a fraction of the publisher's suggested price and random books having the highest prices. Interestingly, price dispersion does not conform with this prediction, apparently for reasons related to stores' decisions to carry particular books. One reason why we may not observe convergence in prices is because stores have succeeded in differentiating themselves even though they are selling a commodity product. We observe differentiation (or attempted differentiation) by a significant number of firms.

Diffusive Transport in Quasi-2D and Quasi-1D Electron Systems

Quantum-confined semiconductor structures are the cornerstone of modern-day electronics. Spatial confinement in these structures leads to formation of discrete low-dimensional subbands. At room temperature, carriers transfer among different states due to efficient scattering with phonons, charged impurities, surface roughness and other electrons, so transport is scattering-limited (diffusive) and well described by the Boltzmann transport equation. In this review, we present the theoretical framework used for the description and simulation of diffusive electron transport in quasi-two-dimensional and quasi-one-dimensional semiconductor structures. Transport in silicon MOSFETs and nanowires is presented in detail.Comment: Review article, to appear in Journal of Computational and Theoretical Nanoscienc

Contrasting Multiple Social Network Autocorrelations for Binary Outcomes, With Applications To Technology Adoption

The rise of socially targeted marketing suggests that decisions made by consumers can be predicted not only from their personal tastes and characteristics, but also from the decisions of people who are close to them in their networks. One obstacle to consider is that there may be several different measures for "closeness" that are appropriate, either through different types of friendships, or different functions of distance on one kind of friendship, where only a subset of these networks may actually be relevant. Another is that these decisions are often binary and more difficult to model with conventional approaches, both conceptually and computationally. To address these issues, we present a hierarchical model for individual binary outcomes that uses and extends the machinery of the auto-probit method for binary data. We demonstrate the behavior of the parameters estimated by the multiple network-regime auto-probit model (m-NAP) under various sensitivity conditions, such as the impact of the prior distribution and the nature of the structure of the network, and demonstrate on several examples of correlated binary data in networks of interest to Information Systems, including the adoption of Caller Ring-Back Tones, whose use is governed by direct connection but explained by additional network topologies