Right-Wing Refugees and British Politics, 1830-1871

This thesis investigates the role of right-wing refugees in British politics during the middle years of the nineteenth century, considering the relationships which these refugees established with British politicians, and the difficulties which their multifarious activities created for the makers of British foreign policy. Whereas the contribution of left-wing refugees to British politics and diplomacy during the Victorian era has been considered at length by numerous historians, the relationships which their right-wing counterparts formed with British politicians and the diplomatic concerns which they created have found little attention. This thesis seeks to redress this imbalance by analysing an overlooked but nevertheless important series of networks and controversies in which these exiles became involved during the tumultuous middle years of the nineteenth century. The study first considers the largely diplomatic implications of the presence of the former Charles X of France and his court in Britain during 1830-32, before turning to the difficulties and opportunities which both the Carlist and Miguelite pretenders and their refugee supporters presented for British governments and politicians alike throughout the 1830s and 1840s. The next three chapters consider the apogee of refugee influence over British politics during the years 1848-50, when the victims of the European revolutions of 1848 intrigued with allies in both Britain and continental Europe alike. The final two chapters then chart the rise of the refugee Orléans branch of the French royal family into highly-regarded political actors, whilst considering the diplomatic implications of their presence in Britain. This study suggests that whereas left-wing refugees boasted a modest political legacy and provoked several international controversies, those of the right not only enflamed diplomatic dispute but often actively intervened in British high politics. It therefore posits that refugees played a far wider and more important role in nineteenth-century Britain than previously noted

Use Of Sacrificial Embankments To Minimize Bridge Damage From Scour During Extreme Flow Events

The leading cause of bridge failure has often been identified as bridge scour, which is generally defined as the erosion or removal of streambed and/or bank material around bridge foundations due to flowing water. These scour critical bridges are particularly vulnerable during extreme flood events, and pose a major risk to human life, transportation infrastructure, and economic sustainability. Climate change is increasing the intensity and persistence of large flow events throughout the world, further straining bridge infrastructure. Retrofitting the thousands of undersized and scour critical bridges to more rigorous standards is prohibitively expensive, and current countermeasures inadequately address the core problems related to bridge scour. This research tested the efficacy of using approach embankments as intentional sacrificial fuses to protect the integrity of bridges with minimal damage during large flow events by allowing the streams to access their natural floodplain and reduce channel velocities. The concept was evaluated using the Hydrologic Engineering Center\u27s River Analysis System (HEC-RAS) models. Steady flow models were developed for three specific bridges on two river reaches. Bayesian streamflow return period estimators were developed for both river reaches using available United States Geological Survey (USGS) stream gauge data to evaluate sacrificial embankments under non-stationary climatic conditions. Fuse placement was determined to be a cost effective scour mitigation strategy for bridges with suboptimal hydraulic capacity and unknown or shallow foundations. Additional benefits of fuses include reductions in upstream flood stage and velocity

Middle Managers Intention To Use Information In A Data-driven Decision Support System: A Case Study In The Aircraft Industry

As aircraft organizations, like many organizations, seek out ways to increase efficiencies in order to remain competitive within the market, higher utilization of information and personnel become essential. This increase in efficiency will require additional insight into the behavioral intention predictability of middle managers that are responsible for making the decisions that drive efficiencies. Theoretical models such as the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB), and the Technology Acceptance Model (TAM) have linked behavioral intention to predictable behavior. This research study seeks to provide a deeper understanding into the phenomenon of the behavioral intention predictability of middle managers in the aircraft industry to who use information from a data-driven decision support system. The research intends to use a case study methodology utilizing interviews and observations to collect data about attitude, subjective norms, and perceived behavioral controls. All of these play a role in the prediction of behavioral intention. The expectation of the study is to provide organizations, within the aerospace industry, with advanced understanding about behavioral intention predictability to maximize information utilization and performance from a data-driven decision support system. This research study focuses on the measurement of behavioral intention to use, not use itself

G-RepsNet: A Fast and General Construction of Equivariant Networks for Arbitrary Matrix Groups

Group equivariance is a strong inductive bias useful in a wide range of deep learning tasks. However, constructing efficient equivariant networks for general groups and domains is difficult. Recent work by Finzi et al. (2021) directly solves the equivariance constraint for arbitrary matrix groups to obtain equivariant MLPs (EMLPs). But this method does not scale well and scaling is crucial in deep learning. Here, we introduce Group Representation Networks (G-RepsNets), a lightweight equivariant network for arbitrary matrix groups with features represented using tensor polynomials. The key intuition for our design is that using tensor representations in the hidden layers of a neural network along with simple inexpensive tensor operations can lead to expressive universal equivariant networks. We find G-RepsNet to be competitive to EMLP on several tasks with group symmetries such as O(5), O(1, 3), and O(3) with scalars, vectors, and second-order tensors as data types. On image classification tasks, we find that G-RepsNet using second-order representations is competitive and often even outperforms sophisticated state-of-the-art equivariant models such as GCNNs (Cohen & Welling, 2016a) and E(2)-CNNs (Weiler & Cesa, 2019). To further illustrate the generality of our approach, we show that G-RepsNet is competitive to G-FNO (Helwig et al., 2023) and EGNN (Satorras et al., 2021) on N-body predictions and solving PDEs, respectively, while being efficient

Rapid simulation of spatial epidemics : a spectral method

Spatial structure and hence the spatial position of host populations plays a vital role in the spread of infection. In the majority of situations, it is only possible to predict the spatial spread of infection using simulation models, which can be computationally demanding especially for large population sizes. Here we develop an approximation method that vastly reduces this computational burden. We assume that the transmission rates between individuals or sub-populations are determined by a spatial transmission kernel. This kernel is assumed to be isotropic, such that the transmission rate is simply a function of the distance between susceptible and infectious individuals; as such this provides the ideal mechanism for modelling localised transmission in a spatial environment. We show that the spatial force of infection acting on all susceptibles can be represented as a spatial convolution between the transmission kernel and a spatially extended ‘image’ of the infection state. This representation allows the rapid calculation of stochastic rates of infection using fast-Fourier transform (FFT) routines, which greatly improves the computational efficiency of spatial simulations. We demonstrate the efficiency and accuracy of this fast spectral rate recalculation (FSR) method with two examples: an idealised scenario simulating an SIR-type epidemic outbreak amongst N habitats distributed across a two-dimensional plane; the spread of infection between US cattle farms, illustrating that the FSR method makes continental-scale outbreak forecasting feasible with desktop processing power. The latter model demonstrates which areas of the US are at consistently high risk for cattle-infections, although predictions of epidemic size are highly dependent on assumptions about the tail of the transmission kernel

Efficient Isotropic BRDF Measurement

In this paper we present novel reflectance measurement procedures that require fewer total measurements than standard uniform sampling approaches. First, we acquire densely sampled reflectance data for a large collection of different materials. Using these densely sampled measurements we analyze the general surface reflectance function to determine the local signal variation at each point in the function's domain. We then use wavelet analysis to derive a common basis for all of the acquired reflectance functions as well as a corresponding non-uniform sampling pattern that corresponds to all non-zero wavelet coefficients. Second, we show that the reflectance of an arbitrary material can be represented as a linear combination of the surface reflectance functions. Furthermore, our analysis provides a reduced set of sampling points that permits us to robustly estimate the coefficients of this linear combination. These procedures dramatically shorten the acquisition time for isotropic reflectance measurements. We present a detailed description and analysis of our measurement approaches and sampling strategies.Engineering and Applied Science

Intelligent Systems Development in a Non Engineering Curriculum

Much of computer system development today is programming in the large - systems of millions of lines of code distributed across servers and the web. At the same time, microcontrollers have also become pervasive in everyday products, economical to manufacture, and represent a different level of learning about system development. Real world systems at this level require integrated development of custom hardware and software. How can academic institutions give students a view of this other extreme - programming on small microcontrollers with specialized hardware? Full scale system development including custom hardware and software is expensive, beyond the range of any but the larger engineering oriented universities, and hard to fit into a typical length course. The course described here is a solution using microcontroller programming in high level language, small hardware components, and the Arduino open source microcontroller. The results of the hands-on course show that student programmers with limited hardware knowledge are able to build custom devices, handle the complexity of basic hardware design, and learn to appreciate the differences between large and small scale programming

Face Transfer with Multilinear Models

Face Transfer is a method for mapping videorecorded performances of one individual to facial animations of another. It extracts visemes (speech-related mouth articulations), expressions, and three-dimensional (3D) pose from monocular video or film footage. These parameters are then used to generate and drive a detailed 3D textured face mesh for a target identity, which can be seamlessly rendered back into target footage. The underlying face model automatically adjusts for how the target performs facial expressions and visemes. The performance data can be easily edited to change the visemes, expressions, pose, or even the identity of the target---the attributes are separably controllable. This supports a wide variety of video rewrite and puppetry applications.Face Transfer is based on a multilinear model of 3D face meshes that separably parameterizes the space of geometric variations due to different attributes (e.g., identity, expression, and viseme). Separability means that each of these attributes can be independently varied. A multilinear model can be estimated from a Cartesian product of examples (identities x expressions x visemes) with techniques from statistical analysis, but only after careful preprocessing of the geometric data set to secure one-to-one correspondence, to minimize cross-coupling artifacts, and to fill in any missing examples. Face Transfer offers new solutions to these problems and links the estimated model with a face-tracking algorithm to extract pose, expression, and viseme parameters.Engineering and Applied Science