Objective: The primary objective of this study was to estimate the long-term cost-effectiveness of stroke prevention in patients with nonvalvular atrial fibrillation (NVAF) in the United States using new anticoagulant therapies - dabigatran 150 mg, apixaban 5 mg, and rivaroxaban 20 mg - as well as the standard treatment, warfarin. Methods: A Markov decision-analysis model was constructed using data from clinical trials that evaluated the new oral anticoagulants relative to warfarin (apixaban 5 mg & ARISTOTLE, dabigatran 150 mg & RE-LY, and rivaroxaban 20 mg & ROCKET-AF) to compare the lifetime cost and quality-adjusted life expectancy. The Markov model target population was a hypothetical cohort of 70-year old patients with nonvalvular atrial fibrillation, an increased risk for stroke (CHADS₂ ≥ 1, or equivalent), a renal creatinine clearance (CrCl) of 50 or above, and no contraindication to anticoagulant therapy. Using pair-wise comparisons of each therapy, analyses were conducted to evaluate incremental cost-effectiveness ratios (ICERs), net monetary benefits (NMBs), lifetime costs, life-years, and quality-adjusted life-years (QALYs). Results: In the base case, warfarin had the lowest cost of $71,857 (95% confidence interval [CI]: $68,730, $77,452), followed by rivaroxaban 20 mg ($74,023; 95% CI: $70,943, $77,307), dabigatran 150 mg ($78,584; 95% CI: $75,277, $81,968), and apixaban 5 mg ($81,180; 95% CI: $78,642, $83,756). Apixaban 5 mg also yielded the highest QALY estimate, 8.63 (95% CI: 8.52, 8.72), followed by dabigatran 150 mg (8.55; 95% CI: 8.43, 8.67), rivaroxaban 20 mg (8.42; 95% CI: 8.31, 8.54), and warfarin (8.17; 95% CI: 8.1, 8.24). In a Monte Carlo probabilistic sensitivity analysis, apixaban 5 mg, dabigatran 150 mg, rivaroxaban 20 mg, and warfarin were cost effective in 45%, 37%, 19%, 0%, respectively, of the simulations using a willingness-to pay threshold of $50,000 per QALY gained. From the one-way sensitivity analyses, new anticoagulant (apixaban 5 mg, dabigatran 150 mg, rivaroxaban 20 mg) costs and probabilities associated with intracranial hemorrhage and stroke for patients receiving rivaroxaban 20 mg were identified as significant influential variables impacting model results. Conclusion: In patients with NVAF and an increased risk of stroke prophylaxis, apixaban 5 mg, dabigatran 150 mg, and rivaroxaban 20 mg may all be cost-effective alternatives to warfarin depending on pricing in the United States and neurologic events for rivaroxaban 20 mg.

In applying reinforcement learning to agents acting in the real world we are often faced with tasks that are non-Markovian in nature. Much work has been done using state estimation algorithms to try to uncover Markovian models of tasks in order to allow the learning of optimal solutions using reinforcement learning. Unfortunately these algorithms which attempt to simultaneously learn a Markov model of the world and how to act have proved very brittle. Our focus differs. In considering embodied, embedded and situated agents we have a preference for simple learning algorithms which reliably learn satisficing policies. The learning algorithms we consider do not try to uncover the underlying Markovian states, instead they aim to learn successful deterministic reactive policies such that agents actions are based directly upon the observations provided by their sensors. Existing results have shown that such reactive policies can be arbitrarily worse than a policy that has access to the underlying Markov process and in some cases no satisficing reactive policy can exist. Our first contribution is to show that providing agents with alternative actions and viewpoints on the task through the addition of active perception can provide a practical solution in such circumstances. We demonstrate empirically that: (i) adding arbitrary active perception actions to agents which can only learn deterministic reactive policies can allow the learning of satisficing policies where none were originally possible; (ii) active perception actions allow the learning of better satisficing policies than those that existed previously and (iii) our approach converges more reliably to satisficing solutions than existing state estimation algorithms such as U-Tree and the Lion Algorithm. Our other contributions focus on issues which affect the reliability with which deterministic reactive satisficing policies can be learnt in non-Markovian environments. We show that that greedy action selection may be a necessary condition for the existence of stable deterministic reactive policies on partially observable Markov decision processes (POMDPs). We also set out the concept of Consistent Exploration. This is the idea of estimating state-action values by acting as though the policy has been changed to incorporate the action being explored. We demonstrate that this concept can be used to develop better algorithms for learning reactive policies to POMDPs by presenting a new reinforcement learning algorithm; the Consistent Exploration Q(l) algorithm (CEQ(l)). We demonstrate on a significant number of problems that CEQ(l) is more reliable at learning satisficing solutions than the algorithm currently regarded as the best for learning deterministic reactive policies, that of SARSA(l).

Explorations into the organisation of spontaneous activity within the brain have demonstrated the existence of networks of temporally correlated activity, consisting of brain areas that share similar cognitive or sensory functions. These so-called resting state networks (RSNs) emerge spontaneously during rest and disappear in response to overt stimuli or cognitive demands. In recent years, the study of RSNs has emerged as a valuable tool for probing brain function, both in the healthy brain and in disorders such as schizophrenia, Alzheimer’s disease and Parkinson’s disease. However, analyses of these networks have so far been limited, in part due to assumptions that the patterns of neuronal activity that underlie these networks remain constant over time. Moreover, the majority of RSN studies have used functional magnetic resonance imaging (fMRI), in which slow fluctuations in the level of oxygen in the blood are used as a proxy for the activity within a given brain region. In this thesis we develop the use of magnetoencephalography (MEG) to study resting state functional connectivity. Unlike fMRI, MEG provides a direct measure of neuronal activity and can provide novel insights into the temporal dynamics that underlie resting state activity. In particular, we focus on the application of non- stationary analysis methods, which are able to capture fast temporal changes in activity. We first develop a framework for preprocessing MEG data and measuring interactions within different RSNs (Chapter 3). We then extend this framework to assess temporal variability in resting state functional connectivity by applying time- varying measures of interactions and show that within-network functional connectivity is underpinned by non-stationary temporal dynamics (Chapter 4). Finally we develop a data driven approach based on a hidden Markov model for inferring short lived connectivity states from resting state and task data (Chapter 5). By applying this approach to data from multiple subjects we reveal transient states that capture short lived patterns of neuronal activity (Chapter 6).

In a collaboration between Kolmården Zoo and Linköping University, supported by the Norrköping municipality’s fund for research and innovation, mobility measurements have been performed inside the zoo. These measurements have been done by six WiFi sniffers collecting anonymised MAC addresses from the visitors smartphones. The aim of this thesis is to analyse these data to understand visitor flows in the park and other statistics using a model based mobility analysis. The work implies that one can make a rather good prediction of the geographical visitor distribution using this equipment and statistical models. / I ett samarbete mellan Kolmården djurpark och Linköpings universitet, sponsrat av Norrköpingskommuns fond för forskning och utveckling, har rörelsemätningar gjorts inuti parken. Mätningarna har utgjorts av sex WiFi-sniffers som samlar in anonymiserade MAC-adresser från besökares smartphones. Målet med detta arbete är att analysera denna data för att förstå besökarflöden i parken och annan statistik genom att använda en modellbaserad rörelseanalys. Arbetet visar att man med denna utrsutning och statistiska metoder kan skapa en god prediktion av hur den geografiska besökardistributionen ser ut över tid.

A demanda por abordagens eficientes para o problema de reconhecer a estrutura de cada gene numa sequência genômica motivou a implementação de um grande número de programas preditores de genes. Fizemos uma análise dos programas de sucesso com abordagem probabilística e reconhecemos semelhanças na implementação dos mesmos. A maior parte desses programas utiliza a cadeia oculta generalizada de Markov (GHMM - generalized hiddenMarkov model) como um modelo de gene. Percebemos que muitos preditores têm a arquitetura da GHMM fixada no código-fonte, dificultando a investigação de novas abordagens. Devido a essa dificuldade e pelas semelhanças entre os programas atuais, implementamos o sistema MYOP (Make Your Own Predictor) que tem como objetivo fornecer um ambiente flexível o qual permite avaliar rapidamente cada modelo de gene. Mostramos a utilidade da ferramenta através da implementação e avaliação de 96 modelos de genes em que cada modelo é formado por um conjunto de estados e cada estado tem uma distribuição de duração e um outro modelo probabilístico. Verificamos que nem sempre um modelo probabilísticomais sofisticado fornece um preditor melhor, mostrando a relevância das experimentações e a importância de um sistema como o MYOP. / The demand for efficient approaches for the gene structure prediction has motivated the implementation of different programs. In this work, we have analyzed successful programs that apply the probabilistic approach. We have observed similarities between different implementations, the same mathematical framework called generalized hidden Markov chain (GHMM) is applied. One problem with these implementations is that they maintain fixed GHMM architectures that are hard-coded. Due to this problem and similarities between the programs, we have implemented the MYOP framework (Make Your Own Predictor) with the objective of providing a flexible environment that allows the rapid evaluation of each gene model. We have demonstrated the utility of this tool through the implementation and evaluation of 96 gene models in which each model has a set of states and each state has a duration distribution and a probabilistic model. We have shown that a sophisticated probabilisticmodel is not sufficient to obtain better predictor, showing the experimentation relevance and the importance of a system as MYOP.

For young animals, surviving the first year of nutritional independence requires rapid development of effective foraging behaviour before the onset of terminal starvation. Grey seal (Halichoerus grypus) pups are abandoned on the natal colony after a brief (15-21 days) suckling period and must learn to dive and forage without parental instruction. Regional and sex-specific differences in diet and foraging behaviour have been described for adults and juveniles, but the early-life behaviour of pups during the critical first months at sea remains poorly understood. This thesis investigates sources of intrinsic and extrinsic variation in the development of foraging behaviour and resource selection in grey seal pups. The studies presented here feature tracking and dive data collected from 52 recently-weaned pups, tagged at six different breeding colonies in two geographically-distinct regions of the United Kingdom (UK). Original aspects of this thesis include: (Chapter I) a comprehensive review of analytical methods for inferring foraging behaviour from tracking and dive data in pinnipeds; (Chapter II) description and comparison of regional and sex differences in movements and diving characteristics of recently-weaned pups during their first trips at sea; (Chapter III) implementation of a novel generalized hidden Markov modelling (HMM) technique to investigate the development of foraging movement patterns whilst accounting for sources of intrinsic (age, sex) and extrinsic (regional) variation; and (Chapter IV) the first analysis of grey seal pup foraging habitat preference, incorporating behavioural inferences from HMMs and investigating changes in preference through time.

Distance sampling and spatial capture-recapture are statistical methods to estimate the number of animals in a wild population based on encounters between these animals and scientific detectors. Both methods estimate the probability an animal is detected during a survey, but do not explicitly model animal movement. The primary challenge is that animal movement in these surveys is unobserved; one must average over all possible paths each animal could have travelled during the survey. In this thesis, a general statistical model, with distance sampling and spatial capture-recapture as special cases, is presented that explicitly incorporates animal movement. An efficient algorithm to integrate over all possible movement paths, based on quadrature and hidden Markov modelling, is given to overcome the computational obstacles. For distance sampling, simulation studies and case studies show that incorporating animal movement can reduce the bias in estimated abundance found in conventional models and expand application of distance sampling to surveys that violate the assumption of no animal movement. For spatial capture-recapture, continuous-time encounter records are used to make detailed inference on where animals spend their time during the survey. In surveys conducted in discrete occasions, maximum likelihood models that allow for mobile activity centres are presented to account for transience, dispersal, and heterogeneous space use. These methods provide an alternative when animal movement causes bias in standard methods and the opportunity to gain richer inference on how animals move, where they spend their time, and how they interact.

<div> <div> <div> <p>Nowadays, the pace of coordinated cyber security crimes has become drastically more rapid, and network attacks have become more advanced and diversified. The explosive growth of network security threats poses serious challenges for building secure Cyber-based Systems (CBS). Existing studies have addressed a breadth of challenges related to detecting network attacks. However, there is still a lack of studies on the detection of sophisticated Multi-stage Attacks (MSAs). </p> <p>The objective of this dissertation is to address the challenges of modeling and detecting sophisticated network attacks, such as multiple interleaved MSAs. We present the interleaving concept and investigate how interleaving multiple MSAs can deceive intrusion detection systems. Using one of the important statistical machine learning (ML) techniques, Hidden Markov Models (HMM), we develop three architectures that take into account the stealth nature of the interleaving attacks, and that can detect and track the progress of these attacks. These architectures deploy a set of HMM templates of known attacks and exhibit varying performance and complexity. </p> <p>For performance evaluation, various metrics are proposed which include (1) attack risk probability, (2) detection error rate, and (3) the number of correctly detected stages. Extensive simulation experiments are conducted to demonstrate the efficacy of the proposed architecture in the presence of multiple multi-stage attack scenarios, and in the presence of false alerts with various rates. </p> </div> </div> </div>

Thesis (M.Sc. (Computer Science) --University of Limpopo, 2013 / This research study focuses on building a general-purpose working Xitsonga speech synthesis system that is as far as can be possible reasonably intelligible, natural sounding, and flexible. The system built has to be able to model some of the desirable speaker characteristics and speaking styles. This research project forms part of the broader national speech technology project that aims at developing spoken language systems for human-machine interaction using the eleven official languages of South Africa (SA). Speech synthesis is the reverse of automatic speech recognition (which receives speech as input and converts it to text) in that it receives text as input and produces synthesized speech as output. It is generally accepted that most people find listening to spoken utterances better that reading the equivalent of such utterances. The Xitsonga speech synthesis system has been developed using a hidden Markov model (HMM) speech synthesis method. The HMM-based speech synthesis (HTS) system synthesizes speech that is intelligible, and natural sounding. This method can synthesize speech on a footprint of only a few megabytes of training speech data. The HTS toolkit is applied as a patch to the HTK toolkit which is a hidden Markov model toolkit primarily designed for use in speech recognition to build and manipulate hidden Markov models.

This dissertation addresses the development of a telemanipulation system using intelligent mapping from a haptic user interface to a remote manipulator to assist in maximizing the manipulation capabilities of persons with disabilities. This mapping, referred to as assistance function, is determined on the basis of environmental model or real-time sensory data to guide the motion of a telerobotic manipulator while performing a given task. Human input is enhanced rather than superseded by the computer. This is particularly useful when the user has restricted range of movements due to certain disabilities such as muscular dystrophy, a stroke, or any form of pathological tremor. In telemanipulation system, assistance of variable position/velocity mapping or virtual fixture can improve manipulation capability and dexterity. Conventionally, these assistances are based on the environment information, without knowing user's motion intention. In this dissertation, user's motion intention is combined with real-time environment information for applying appropriate assistance. If the current task is following a path, a virtual fixture orthogonal to the path is applied. Similarly, if the task is to align the end-effector with a target, an attractive force field is generated. In order to successfully recognize user's motion intention, a Hidden Markov Model (HMM) is developed. This dissertation describes the HMM based skill learning and its application in a motion therapy system in which motion along a labyrinth is controlled using a haptic interface. Two persons with disabilities on upper limb are trained using this virtual therapist. The performance measures before and after the therapy training, including the smoothness of the trajectory, distance ratio, time taken, tremor and impact forces are presented. The results demonstrate that the forms of assistance provided reduced the execution times and increased the performance of the chosen tasks for the disabled individuals. In addition, these results suggest that the introduction of the haptic rendering capabilities, including the force feedback, offers special benefit to motion-impaired users by augmenting their performance on job related tasks.