Measuring environmental perceptions and sense of place in Franklin County, Florida

Franklin County, Florida is widely perceived as one of the last unspoiled areas on the Gulf Coast. This study examines the historical geography, economic activities and socio-cultural aspects of the area which contribute to the construction of sense of place values among residents and visitors. The commodification of nature emerged as a consistent theme of this research from the timber and seafood industries to real estate development and tourism. This study uses both qualitative and quantitative techniques to analyze environmental perceptions and sense of place intensity. Results provided an opportunity to compare the two methods and to identify key factors contributing to the construction of sense of place. Informants perceived that the areas unique way of life linked to the natural environment played an important role in sense of place and personal identity construction.Department of GeographyThesis (M.S.

Bayesian inference for queueing networks and modeling of internet services

Modern Internet services, such as those at Google, Yahoo!, and Amazon, handle billions of requests per day on clusters of thousands of computers. Because these services operate under strict performance requirements, a statistical understanding of their performance is of great practical interest. Such services are modeled by networks of queues, where each queue models one of the computers in the system. A key challenge is that the data are incomplete, because recording detailed information about every request to a heavily used system can require unacceptable overhead. In this paper we develop a Bayesian perspective on queueing models in which the arrival and departure times that are not observed are treated as latent variables. Underlying this viewpoint is the observation that a queueing model defines a deterministic transformation between the data and a set of independent variables called the service times. With this viewpoint in hand, we sample from the posterior distribution over missing data and model parameters using Markov chain Monte Carlo. We evaluate our framework on data from a benchmark Web application. We also present a simple technique for selection among nested queueing models. We are unaware of any previous work that considers inference in networks of queues in the presence of missing data.Comment: Published in at http://dx.doi.org/10.1214/10-AOAS392 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Probabilistic Inference in Queueing Networks

Although queueing models have long been used to model the performance of computer systems, they are out of favor with practitioners, because they have a reputation for requiring unrealistic distributional assumptions. In fact, these distributional assumptions are used mainly to facilitate analytic approximations such as asymptotics and large-deviations bounds. In this paper, we analyze queueing networks from the probabilistic modeling perspective, applying inference methods from graphical models that afford significantly more modeling flexibility. In particular, we present a Gibbs sampler and stochastic EM algorithm for networks of M/M/1 FIFO queues. As an application of this technique, we localize performance problems in distributed systems from incomplete system trace data. On both synthetic networks and an actual distributed Web application, the model accurately recovers the system’s service time using 1 % of the available trace data.

Inference and Learning in Networks of Queues

Probabilistic models of the performance of computer systems are useful both for predicting system performance in new conditions, and for diagnosing past performance problems. The most popular performance models are networks of queues. However, no current methods exist for parameter estimation or inference in networks of queues with missing data. In this paper, we present a novel viewpoint that combines queueing networks and graphical models, allowing Markov chain Monte Carlo to be applied. We demonstrate the effectiveness of our sampler on real-world data from a benchmark Web application.

Determination of high temperature strains using a PC based vision system

With the widespread availability of video digitizers and cheap personal computers, the use of computer vision as an experimental tool is becoming common place. These systems are being used to make a wide variety of measurements that range from simple surface characterization to velocity profiles. The Sub-Pixel Digital Image Correlation technique has been developed to measure full field displacement and gradients of the surface of an object subjected to a driving force. The technique has shown its utility by measuring the deformation and movement of objects that range from simple translation to fluid velocity profiles to crack tip deformation of solid rocket fuel. This technique has recently been improved and used to measure the surface displacement field of an object at high temperature. The development of a PC based Sub-Pixel Digital Image Correlation system has yielded an accurate and easy to use system for measuring surface displacements and gradients. Experiments have been performed to show the system is viable for measuring thermal strain

Temporal-Difference Learning to Assist Human Decision Making during the Control of an Artificial Limb

In this work we explore the use of reinforcement learning (RL) to help with human decision making, combining state-of-the-art RL algorithms with an application to prosthetics. Managing human-machine interaction is a problem of considerable scope, and the simplification of human-robot interfaces is especially important in the domains of biomedical technology and rehabilitation medicine. For example, amputees who control artificial limbs are often required to quickly switch between a number of control actions or modes of operation in order to operate their devices. We suggest that by learning to anticipate (predict) a user's behaviour, artificial limbs could take on an active role in a human's control decisions so as to reduce the burden on their users. Recently, we showed that RL in the form of general value functions (GVFs) could be used to accurately detect a user's control intent prior to their explicit control choices. In the present work, we explore the use of temporal-difference learning and GVFs to predict when users will switch their control influence between the different motor functions of a robot arm. Experiments were performed using a multi-function robot arm that was controlled by muscle signals from a user's body (similar to conventional artificial limb control). Our approach was able to acquire and maintain forecasts about a user's switching decisions in real time. It also provides an intuitive and reward-free way for users to correct or reinforce the decisions made by the machine learning system. We expect that when a system is certain enough about its predictions, it can begin to take over switching decisions from the user to streamline control and potentially decrease the time and effort needed to complete tasks. This preliminary study therefore suggests a way to naturally integrate human- and machine-based decision making systems.Comment: 5 pages, 4 figures, This version to appear at The 1st Multidisciplinary Conference on Reinforcement Learning and Decision Making, Princeton, NJ, USA, Oct. 25-27, 201

Changing stroke mortality trends in middle-aged people: an age-period-cohort analysis of routine mortality data in persons aged 40 to 69 in England

Background: In the UK, overall stroke mortality has declined. A similar trend has been seen in coronary heart disease, although recent reports suggest this decline might be levelling off in middle-aged adults. Aim: To investigate recent trends in stroke mortality among those aged 40–69 years in England. Methods: The authors used routine annual aggregated stroke death and population data for England for the years 1979–2005 to investigate time trends in gender-specific mortalities for adults aged 40 to 69 years. The authors applied log-linear modelling to isolate effects attributable to age, linear ‘drift’ over time, time period and birth cohort. Results; Between 1979 and 2005, age-standardised stroke mortality aged 40 to 69 years dropped from 93 to 30 per 100 000 in men and from 62 to 18 per 100 000 in women. Mortality was higher in older age groups, but the difference between the older and younger age groups appears to have decreased over time for both sexes. Modelling of the data suggests an average annual reduction in stroke deaths of 4.0% in men and 4.3% in women, although this decrease has been particularly marked in the last few years. However, we also observed a relative rate increase in mortality among those born since the mid-1940s compared with earlier cohorts; this appears to have been sustained in men, which explains the levelling off in the rate of mortality decline observed in recent years in the younger middle-aged. Conclusions: If observed trends in middle-aged adults continue, overall stroke mortalities may start to increase again

High Strain Rate Response of Adhesively Bonded Fiber-Reinforced Composite Joints A Computational Study to Guide Experimental Design

Adhesively bonded carbon fiber-reinforced epoxy composite laminates are widely used in aerospace applications. During a high energy impact event, these laminates are often subjected to high strain rate loading. However, the influence of high strain rate loading on the response of these composite joints is not well understood. Computational finite element (FE) modeling and simulations are conducted to guide the design of high strain rate experiments. Two different experimental designs based on split Hopkinson bar were numerically modeled to simulate Mode I and Mode II types loading in the composite. In addition, the computational approach adopted in this study helps in understanding the high strain rate response of adhesively bonded composite joints subjected to nominally Mode I and Mode II loading. The modeling approach consists of a ply-level 3D FE model, a progressive damage constitutive model for the composite material behavior and a cohesive tie-break contact element for interlaminar delamination