We develop a novel advanced Particle Markov chain Monte Carlo algorithm that is capable of sampling from the posterior distribution of non-linear state space models for both the unobserved latent states and the unknown model parameters. We apply this novel methodology to five population growth models, including models with strong and weak Allee effects, and test if it can efficiently sample from the complex likelihood surface that is often associated with these models. Utilising real and also synthetically generated data sets we examine the extent to which observation noise and process error may frustrate efforts to choose between these models. Our novel algorithm involves an Adaptive Metropolis proposal combined with an SIR Particle MCMC algorithm (AdPMCMC). We show that the AdPMCMC algorithm samples complex, high-dimensional spaces efficiently, and is therefore superior to standard Gibbs or Metropolis Hastings algorithms that are known to converge very slowly when applied to the non-linear state space ecological models considered in this paper. Additionally, we show how the AdPMCMC algorithm can be used to recursively estimate the Bayesian Cram\'er-Rao Lower Bound of Tichavsk\'y (1998). We derive expressions for these Cram\'er-Rao Bounds and estimate them for the models considered. Our results demonstrate a number of important features of common population growth models, most notably their multi-modal posterior surfaces and dependence between the static and dynamic parameters. We conclude by sampling from the posterior distribution of each of the models, and use Bayes factors to highlight how observation noise significantly diminishes our ability to select among some of the models, particularly those that are designed to reproduce an Allee effect.

The Johnson-Lindenstrauss Lemma allows for the projection of $n$ points in $p-$dimensional Euclidean space onto a $k-$dimensional Euclidean space, with $k \ge \frac{24\ln \emph{n}}{3\epsilon^2-2\epsilon^3}$, so that the pairwise distances are preserved within a factor of $1\pm\epsilon$. Here, working directly with the distributions of the random distances rather than resorting to the moment generating function technique, an improvement on the lower bound for $k$ is obtained. The additional reduction in dimension when compared to bounds found in the literature, is at least $13\%$, and, in some cases, up to $30\%$ additional reduction is achieved. Using the moment generating function technique, we further provide a lower bound for $k$ using pairwise $L_2$ distances in the space of points to be projected and pairwise $L_1$ distances in the space of the projected points. Comparison with the results obtained in the literature shows that the bound presented here provides an additional $36-40\%$ reduction.

We mark up a corpus of LaTeX lecture notes semantically and expose them as Linked Data in XHTML+MathML+RDFa. Our application makes the resulting documents interactively browsable for students. Our ontology helps to answer queries from students and lecturers, and paves the path towards an integration of our corpus with external sites.

This thesis investigates the use of problem-specific knowledge to enhance a genetic algorithm approach to multiple-choice optimisation problems.It shows that such information can significantly enhance performance, but that the choice of information and the way it is included are important factors for success.Two multiple-choice problems are considered.The first is constructing a feasible nurse roster that considers as many requests as possible.In the second problem, shops are allocated to locations in a mall subject to constraints and maximising the overall income.Genetic algorithms are chosen for their well-known robustness and ability to solve large and complex discrete optimisation problems.However, a survey of the literature reveals room for further research into generic ways to include constraints into a genetic algorithm framework.Hence, the main theme of this work is to balance feasibility and cost of solutions.In particular, co-operative co-evolution with hierarchical sub-populations, problem structure exploiting repair schemes and indirect genetic algorithms with self-adjusting decoder functions are identified as promising approaches.The research starts by applying standard genetic algorithms to the problems and explaining the failure of such approaches due to epistasis.To overcome this, problem-specific information is added in a variety of ways, some of which are designed to increase the number of feasible solutions found whilst others are intended to improve the quality of such solutions.As well as a theoretical discussion as to the underlying reasons for using each operator,extensive computational experiments are carried out on a variety of data.These show that the indirect approach relies less on problem structure and hence is easier to implement and superior in solution quality.

This paper introduces the IRIS mobile robot project. IRIS is a largely student designed and implemented mobile robot platform created to provide a mechanism for classroom explorations of topics in artificial intelligence, cognitive science, and robotics. It has been designed to be used by students from middle school through college.

A key challenge for policymakers in many developing countries is to decide which intervention or collection of interventions works best to improve learning outcomes in their schools. Our aim is to develop a causal model that explains student learning outcomes in terms of observable characteristics as well as conditions and processes difficult to observe directly. We start with a theoretical model based on the results of previous research, direct experience and experts’ knowledge in the field. This model is then refined through application of supervised learning methods to available data sets. Once calibrated with local data in a country, the model estimates the probability that a given intervention would affect learning outcomes.

But whereas in the general Rwandan populace males tend Despite the increasing ubiquity of mobile phones in the developing to be much better educated (76.3% of males are literate, but world, remarkably little is known about the structure only 64.7% of females), among mobile phone users it is the and demographics of the mobile phone market. While a women who achieve higher levels of education: the median few qualitative studies have detailed social norms of phone woman completes secondary school, while the median man use in specific communities (Donner 2007; Burrell 2009), does not (t 4.79). Table 1 shows a few statistics on asset and a handful of quantitative researchers have begun to analyze ownership, with associated sampling error.

Teams of University have mentored middle and high school students primary and secondary school students participate in one of from Durham Public Schools as part of the Duke three competitions: rescue, dance, or soccer. Rescue teams RoboCupJunior program, a project-oriented, team-based build and program a robot capable of navigating a course academic enrichment program. The objective of the while identifying and rescuing victims by following lines program is to foster interest and competence in computing, and responding to color cues. Dance teams choreograph a science, and mathematics, while simultaneously dance routine using robots they build and decorate, developing problem-solving skills, enabling creative costumes they prepare, and music they select. Soccer teams thinking and design, and providing a domain for build and program two robots, which then face off against application of scientific concepts. Robotics is a popular other teams' robots in a soccer match. Winners of regional domain for attracting students to computing and competitions are eligible to attend the annual RoboCup engineering (Sklar, Parsons 2002) and can be used in World Finals, held at various locations around the globe.

Cellular phones can be used as mobile sensors, continuously logging users’ behavior including movement, communication and proximity to others. While it is well understood that data generated from mobile phones includes a record of phone calls, there are also more sophisticated data types, such as Bluetooth or cell tower proximity logging, which reveal movement patterns and day-to-day human interactions. We explore the possibility of using mobile phone data to compare movement and communication patterns across cultures. The goal of this proof-of-concept study is to quantify behavior in order to compare different populations. We compare our ability to predict future calling behavior and movement patterns from the cellular phone data of subjects in two distinct groups: a set of university students at MIT in the United States and the University of Nairobi in Kenya. In addition, we show how Bluetooth data may be used to estimate the diffusion of an airborne pathogen outbreak in the different populations.

Evaluating new educational programs and tools, especially those targeted at difficult-to-assess learning goals can be quite challenging due to the small number of participants typically engaged with pilot programs. The focus of the evaluation, then, should be on collecting rich data from each participant about their experience in the workshop and their progress towards meeting the workshop’s learning goals. We present a novel evaluation technique, the debugging task, that seeks to assess at post-workshop a participant’s independent ability to use the tools, skills, and materials of the workshop. The technique is presented in the context of Robot Diaries, a program to develop a robotics design activity centered on crafts materials and expressiveness, and targeted to middle school girls. The paper discusses the rationale for the debugging task, its implementation, and the results and analyses of girls completing the task.