Design and Implementation of Tools for STM Studies in UHV and at the Solid-Liquid Interface

This thesis presents details of the design and development of novel tools and instruments for scanning tunneling microscopy (STM), and may be considered as a repository for several years' worth of development work. The author presents design goals and implementations for two microscopes. First, a novel Pan-type STM was built that could be operated in an ambient environment as a liquid-phase STM. Unique features of this microscope include a unibody frame, for increased microscope rigidity, a novel slider component with large Z-range, a unique wiring scheme and damping mechanism, and a removable liquid cell. The microscope exhibits a high level of mechanical isolation at the tunnel junction, and operates excellently as an ambient tool. Experiments in liquid are on-going. Simultaneously, the author worked on designs for a novel low temperature, ultra-high vacuum (LT-UHV) instrument, and these are presented as well. A novel stick-slip vertical coarse approach motor was designed and built. To gauge the performance of the motor, an in situ motion sensing apparatus was implemented, which could measure the step size of the motor to high precision. A new driving circuit for stick-slip inertial motors is also presented, that o ffers improved performance over our previous driving circuit, at a fraction of the cost. The circuit was shown to increase step size performance by 25%. Finally, a horizontal sample stage was implemented in this microscope. The build of this UHV instrument is currently being fi nalized. In conjunction with the above design projects, the author was involved in a collaborative project characterizing N-heterocyclic carbene (NHC) self-assembled monolayers (SAMs) on Au(111) films. STM was used to characterize Au substrate quality, for both commercial substrates and those manufactured via a unique atomic layer deposition (ALD) process by collaborators. Ambient and UHV STM was then also used to characterize the NHC/Au(111) films themselves, and several key properties of these films are discussed. During this study, the author discovered an unexpected surface contaminant, and details of this are also presented. Finally, two models are presented for the nature of the NHC-Au(111) surface interaction based on the observed film properties, and some preliminary theoretical work by collaborators is presented.

AGOCS – Accurate Google Cloud Simulator Framework

This paper presents the Accurate Google Cloud Simulator (AGOCS) – a novel high-fidelity Cloud workload simulator based on parsing real workload traces, which can be conveniently used on a desktop machine for day-to-day research. Our simulation is based on real-world workload traces from a Google Cluster with 12.5K nodes, over a period of a calendar month. The framework is able to reveal very precise and detailed parameters of the executed jobs, tasks and nodes as well as to provide actual resource usage statistics. The system has been implemented in Scala language with focus on parallel execution and an easy-to-extend design concept. The paper presents the detailed structural framework for AGOCS and discusses our main design decisions, whilst also suggesting alternative and possibly performance enhancing future approaches. The framework is available via the Open Source GitHub repository.

3D considerations for motion perception and visuomotor transformations

Moving through a stable, three-dimensional world is a hallmark of our motor and perceptual experience. This stability is constantly being challenged by movements of the eyes and head, inducing retinal blur and retino-spatial misalignments for which the brain must compensate. To do so, the brain must account for eye and head kinematics to transform two-dimensional retinal input into the reference frame necessary for movement or perception. The four studies in this thesis used both computational and psychophysical approaches to investigate several aspects of this reference frame transformation. In the first study, we examined the neural mechanism underlying the visuomotor transformation for smooth pursuit using a feedforward neural network model. After training, the model performed the general, three-dimensional transformation using gain modulation. This gave mechanistic significance to gain modulation observed in cortical pursuit areas while also providing several testable hypotheses for future electrophysiological work. In the second study, we asked how anticipatory pursuit, which is driven by memorized signals, accounts for eye and head geometry using a novel head-roll updating paradigm. We showed that the velocity memory driving anticipatory smooth pursuit relies on retinal signals, but is updated for the current head orientation. In the third study, we asked how forcing retinal motion to undergo a reference frame transformation influences perceptual decision making. We found that simply rolling one's head impairs perceptual decision making in a way captured by stochastic reference frame transformations. In the final study, we asked how torsional shifts of the retinal projection occurring with almost every eye movement influence orientation perception across saccades. We found a pre-saccadic, predictive remapping consistent with maintaining a purely retinal (but spatially inaccurate) orientation perception throughout the movement. Together these studies suggest that, despite their spatial inaccuracy, retinal signals play a surprisingly large role in our seamless visual experience. This work therefore represents a significant advance in our understanding of how the brain performs one of its most fundamental functions.

An adaptive motion model for person tracking with instantaneous head-pose features

This letter presents novel behaviour-based tracking of people in low-resolution using instantaneous priors mediated by head-pose. We extend the Kalman Filter to adaptively combine motion information with an instantaneous prior belief about where the person will go based on where they are currently looking. We apply this new method to pedestrian surveillance, using automatically-derived head pose estimates, although the theory is not limited to head-pose priors. We perform a statistical analysis of pedestrian gazing behaviour and demonstrate tracking performance on a set of simulated and real pedestrian observations. We show that by using instantaneous `intentional' priors our algorithm significantly outperforms a standard Kalman Filter on comprehensive test data.