Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration

Understanding Peripheral Blood Pressure Signals: A Statistical Learning Approach

Proper estimation of body fluid status for human or animal subjects has always been a challenging problem. Accurate and timely estimate of body fluid can prevent life threatening conditions under trauma and severe dehydration. The main objective of this research is the estimation, classification and detection of dehydration in human and animal subjects using peripheral blood pressure (PBP) signals. Peripheral venous pressure (PVP) and peripheral arterial pressure (PAP) signals have been investigated in this research. Both PVP and PAP signals are PBP signals. A dataset of PVP signals was collected using standard peripheral intravenous catheters from human subjects suffering from hypertrophic pyloric stenosis. Using this dataset, we successfully classified dehydrated subjects from hydrated subjects using regularized logistic regression on frequency domain data of the PVP signals. During the data acquisition process, the PVP signals was corrupted by noise and blood clot. So, we developed an unsupervised anomaly detection algorithm for PVP signals using hidden Markov model and Kalman filter. This anomaly detection algorithm removed the human bias in data-preprocessing. Another dataset of PAP and PVP signals was collected from pigs under anesthesia using the Millar catheter. We proposed a integral pulse frequency modulation (IPFM) based signal model for both PAP and PVP signals. The proposed model-synthesized signal is highly correlated with the experimental data. The model-synthesized signals also performs similar to experimental signals under classification tasks. We also examine the model estimated parameters both qualitatively and quantitatively. This model can also quantify the effect of respiratory rate on heart rate variability. Increasing doses of anesthesia has similar effect of getting hydrated from dehydration

Flexibler Birth-Death MCMC Sampler für Changepoint-Modelle

Diese Arbeit beschreibt eine flexible Architektur eines Markov Chain Monte Carlo Samplers, der Bayessche Inferenz für eine Vielzahl von Changepoint-Modellen erlaubt. Die Struktur dieser Klasse von Modellen besteht aus zwei stochastischen Prozessen. Der erste Prozess wird entweder direkt beobachtet oder indirekt durch, möglicherweise verrauschte, Beobachtungen. Der zweite Prozess ist unbeobachtet und bestimmt die Parameter des beobachteten Prozesses. Die Hauptannahme unserer Modellklasse ist, dass der versteckte Prozess stückweise konstant ist, d.h. er springt zwischen diskreten Zuständen. Als beobachteter Prozess diskutieren wir hauptsächlich den Ornstein-Uhlenbeck und Poisson Prozess. Der versteckte Prozess kann eine feste Anzahl von Zuständen haben oder eine unbekannte Anzahl. Im zweiten Fall basiert das Modell auf einem versteckten Chinese Restaurant Prozess und ermöglicht so Bayessche Inferenz über die Anzahl der Zustände des versteckten Parameterprozesses. Der Sampler wendet einen Metropolis-Hastings Random Walk auf den versteckten Prozess an indem Birth-Death Schritte vorgeschlagen werden. Die Arbeit präsentiert unterschiedliche Modifikationen des Pfades des versteckten Prozesses. Die Struktur des Samplers ist sehr flexibel und lässt sich, im Vergleich zu anderen Algorithmen, die für ein spezifisches Modell maßgeschneidert sind, einfach an verschiedene Kombinationen von beobachteten und versteckten Prozessen anpassen. Angewandt auf Genexpressionsdaten ermöglicht der Sampler Bayessche Inferenz für komplexere Modelle als vorherige Methoden. Der berechnete Bayes Faktor deutet an, dass unser Modell, welches es erlaubt die Stärke des intrinsischen Rauschens zu variieren, die Daten besser erklärt als das vorherige Modell. Der Sampler wird für Genexpressionsdaten von Hefezellen benutzt und die Ergebnisse mit denen einer variationellen Näherung verglichen. Der Posterior scheint genauer in der Vorhersage der Aktivierungszeitpunkte der Transkriptionsfaktoren zu sein als es die Näherung zeigt. Die Ergebnisse des Chinese Restaurant Prozess Samplers auf den gleichen Messungen von Hefezellen unterstützt die vorherige Annahme über die Anzahl der Transkriptionsfaktoren, die in die Kontrolle der untersuchten Gene involviert sind. Die Anpassung des Samplers an Markov modulierte Poisson Prozesse beschleunigt die Inferenz und dies wird gezeigt, indem die Zeit zur Berechnung eines unkorrelierten Samples mit einem exakten Gibbs Sampler verglichen wird. Ein Modell, welches einen beobachteten Poisson Prozess mit dem Chinese Restaurant Prozess verbindet wird anschließend benutzt um versteckte Zustände in der Rate von neuronalen Spike-Daten zu finden und sie mit dem Stimulus zu verbinden. Die Vorteile des Modells beim finden und bestimmen von neuronalen Bursts wird diskutiert und mit Modellen verglichen, die eine kontinuierliche Poisson Rate annehmen.This thesis describes a flexible architecture for a Markov chain Monte Carlo sampler which allows Bayesian posterior inference for a variety of changepoint models. The structure of this class of models consists of two stochastic processes. The first process is either observed directly or indirectly through, possibly noisy, observations. The second process is not observed and governs the parameters of the observed process. The main assumption for our class of models is that the hidden process is piecewise constant, i.e. it jumps between discrete states. As the observed process, we discuss mainly the Ornstein-Uhlenbeck and Poisson process. The hidden process can have a fixed number of states, or an unknown number of states. The latter model is based on a hidden Chinese restaurant process and allows Bayesian inference over the number of states of the hidden parameters. The sampler applies a Metropolis-Hastings random walk on the hidden jump process through proposed birth-death moves. Different kinds of proposal moves on the path of the hidden process are presented. The structure of the sampler makes it very flexible and easy to modify to other combinations of observed and hidden processes compared to other inference methods which are tailor-made for a specific model. Applied to gene expression data the sampler allows Bayesian posterior inference on a more complex model than in previous work. We compute the Bayes factor which indicates that our model, which allows the strength of the system noise to switch, is better in explaining the data. The sampler is used on gene expression data from yeast cells and the results are compared to a variational approximation. The posterior is more confident about the times of transcriptional activity than the approximation suggests. The results from the Chinese restaurant process sampler on the same yeast dataset support the initial assumption about the number of transcription factors involved in the control of the examined genes. When the sampler is used on financial data, changepoints are revealed which can be connected to historic events. This is shown both for the Ornstein-Uhlenbeck model as well as a Cox-Ingersoll-Ross model used in a different thesis. Modifying the sampler to work on Markov modulated Poisson processes allows for very fast posterior inference and this is shown when the time to get an uncorrelated sample is compared to an exact Gibbs sampler for the model. A model combining an observed Poisson process with the Chinese restaurant process is then utilized to find hidden states in the rate of neuronal spike trains and linked to the stimulus. The model's advantages in finding and estimating bursting of neurons is discussed and compared to a model which assumes a continuous Poisson rate

Untangling hotel industry’s inefficiency: An SFA approach applied to a renowned Portuguese hotel chain

The present paper explores the technical efficiency of four hotels from Teixeira Duarte Group - a renowned Portuguese hotel chain. An efficiency ranking is established from these four hotel units located in Portugal using Stochastic Frontier Analysis. This methodology allows to discriminate between measurement error and systematic inefficiencies in the estimation process enabling to investigate the main inefficiency causes. Several suggestions concerning efficiency improvement are undertaken for each hotel studied.info:eu-repo/semantics/publishedVersio

Modelling individual accessibility using Bayesian networks: A capabilities approach

The ability of an individual to reach and engage with basic services such as healthcare, education and activities such as employment is a fundamental aspect of their wellbeing. Within transport studies, accessibility is considered to be a valuable concept that can be used to generate insights on issues related to social exclusion due to limited access to transport options. Recently, researchers have attempted to link accessibility with popular theories of social justice such as Amartya Sen's Capabilities Approach (CA). Such studies have set the theoretical foundations on the way accessibility can be expressed through the CA, however, attempts to operationalise this approach remain fragmented and predominantly qualitative in nature. The data landscape however, has changed over the last decade providing an unprecedented quantity of transport related data at an individual level. Mobility data from dfferent sources have the potential to contribute to the understanding of individual accessibility and its relation to phenomena such as social exclusion. At the same time, the unlabelled nature of such data present a considerable challenge, as a non-trivial step of inference is required if one is to deduce the transportation modes used and activities reached. This thesis develops a novel framework for accessibility modelling using the CA as theoretical foundation. Within the scope of this thesis, this is used to assess the levels of equality experienced by individuals belonging to different population groups and its link to transport related social exclusion. In the proposed approach, activities reached and transportation modes used are considered manifestations of individual hidden capabilities. A modelling framework using dynamic Bayesian networks is developed to quantify and assess the relationships and dynamics of the different components in fluencing the capabilities sets. The developed approach can also provide inferential capabilities for activity type and transportation mode detection, making it suitable for use with unlabelled mobility data such as Automatic Fare Collection Systems (AFC), mobile phone and social media. The usefulness of the proposed framework is demonstrated through three case studies. In the first case study, mobile phone data were used to explore the interaction of individuals with different public transportation modes. It was found that assumptions about individual mobility preferences derived from travel surveys may not always hold, providing evidence for the significance of personal characteristics to the choices of transportation modes. In the second case, the proposed framework is used for activity type inference, testing the limits of accuracy that can be achieved from unlabelled social media data. A combination of the previous case studies, the third case further defines a generative model which is used to develop the proposed capabilities approach to accessibility model. Using data from London's Automatic Fare Collection Systems (AFC) system, the elements of the capabilities set are explicitly de ned and linked with an individual's personal characteristics, external variables and functionings. The results are used to explore the link between social exclusion and transport disadvantage, revealing distinct patterns that can be attributed to different accessibility levels

Advances in Public Transport Platform for the Development of Sustainability Cities

Modern societies demand high and varied mobility, which in turn requires a complex transport system adapted to social needs that guarantees the movement of people and goods in an economically efficient and safe way, but all are subject to a new environmental rationality and the new logic of the paradigm of sustainability. From this perspective, an efficient and flexible transport system that provides intelligent and sustainable mobility patterns is essential to our economy and our quality of life. The current transport system poses growing and significant challenges for the environment, human health, and sustainability, while current mobility schemes have focused much more on the private vehicle that has conditioned both the lifestyles of citizens and cities, as well as urban and territorial sustainability. Transport has a very considerable weight in the framework of sustainable development due to environmental pressures, associated social and economic effects, and interrelations with other sectors. The continuous growth that this sector has experienced over the last few years and its foreseeable increase, even considering the change in trends due to the current situation of generalized crisis, make the challenge of sustainable transport a strategic priority at local, national, European, and global levels. This Special Issue will pay attention to all those research approaches focused on the relationship between evolution in the area of transport with a high incidence in the environment from the perspective of efficiency

Modeling Mutual Influence in Multi-Agent Reinforcement Learning

In multi-agent systems (MAS), agents rarely act in isolation but tend to achieve their goals through interactions with other agents. To be able to achieve their ultimate goals, individual agents should actively evaluate the impacts on themselves of other agents' behaviors before they decide which actions to take. The impacts are reciprocal, and it is of great interest to model the mutual influence of agent's impacts with one another when they are observing the environment or taking actions in the environment. In this thesis, assuming that the agents are aware of each other's existence and their potential impact on themselves, I develop novel multi-agent reinforcement learning (MARL) methods that can measure the mutual influence between agents to shape learning. The first part of this thesis outlines the framework of recursive reasoning in deep multi-agent reinforcement learning. I hypothesize that it is beneficial for each agent to consider how other agents react to their behavior. I start from Probabilistic Recursive Reasoning (PR2) using level-1 reasoning and adopt variational Bayes methods to approximate the opponents' conditional policies. Each agent shapes the individual Q-value by marginalizing the conditional policies in the joint Q-value and finding the best response to improving their policies. I further extend PR2 to Generalized Recursive Reasoning (GR2) with different hierarchical levels of rationality. GR2 enables agents to possess various levels of thinking ability, thereby allowing higher-level agents to best respond to less sophisticated learners. The first part of the thesis shows that eliminating the joint Q-value to an individual Q-value via explicitly recursive reasoning would benefit the learning. In the second part of the thesis, in reverse, I measure the mutual influence by approximating the joint Q-value based on the individual Q-values. I establish Q-DPP, an extension of the Determinantal Point Process (DPP) with partition constraints, and apply it to multi-agent learning as a function approximator for the centralized value function. An attractive property of using Q-DPP is that when it reaches the optimum value, it can offer a natural factorization of the centralized value function, representing both quality (maximizing reward) and diversity (different behaviors). In the third part of the thesis, I depart from the action-level mutual influence and build a policy-space meta-game to analyze agents' relationship between adaptive policies. I present a Multi-Agent Trust Region Learning (MATRL) algorithm that augments single-agent trust region policy optimization with a weak stable fixed point approximated by the policy-space meta-game. The algorithm aims to find a game-theoretic mechanism to adjust the policy optimization steps that force the learning of all agents toward the stable point

Atomistic modelling of precipitation in Ni-base superalloys

The presence of the ordered $\gamma^{\prime}$ phase ($\text{Ni}_{3}\text{Al}$) in Ni-base superalloys is fundamental to the performance of engineering components such as turbine disks and blades which operate at high temperatures and loads. Hence for these alloys it is important to optimize their microstructure and phase composition. This is typically done by varying their chemistry and heat treatment to achieve an appropriate balance between $\gamma^{\prime}$ content and other constituents such as carbides, borides, oxides and topologically close packed phases. In this work we have set out to investigate the onset of $\gamma^{\prime}$ ordering in Ni-Al single crystals and in Ni-Al bicrystals containing coincidence site lattice grain boundaries (GBs) and we do this at high temperatures, which are representative of typical heat treatment schedules including quenching and annealing. For this we use the atomistic simulation methods of molecular dynamics (MD) and density functional theory (DFT). In the first part of this work we develop robust Bayesian classifiers to identify the $\gamma^{\prime}$ phase in large scale simulation boxes at high temperatures around 1500 K. We observe significant \gamma^{\prime} ordering in the simulations in the form of clusters of $\gamma^{\prime}$-like ordered atoms embedded in a $\gamma$ host solid solution and this happens within 100 ns. Single crystals are found to exhibit the expected homogeneous ordering with slight indications of chemical composition change and a positive correlation between the Al concentration and the concentration of $\gamma^{\prime}$ phase. In general, the ordering is found to take place faster in systems with GBs and preferentially adjacent to the GBs. The sole exception to this is the $\Sigma3 \left(111\right)$ tilt GB, which is a coherent twin. An analysis of the ensemble and time lag average displacements of the GBs reveals mostly `anomalous diffusion' behaviour. Increasing the Al content from pure Ni to Ni 20 at.% Al was found to either consistently increase or decrease the mobility of the GB as seen from the changing slope of the time lag displacement average. The movement of the GB can then be characterized as either `super' or `sub-diffusive' and is interpreted in terms of diffusion induced grain boundary migration, which is posited as a possible precursor to the appearance of serrated edge grain boundaries. In the second part of this work we develop a method for the training of empirical interatomic potentials to capture more elements in the alloy system. We focus on the embedded atom method (EAM) and use the Ni-Al system as a test case. Recently, empirical potentials have been developed based on results from DFT which utilize energies and forces, but neglect the electron densities, which are also available. Noting the importance of electron densities, we propose a route to include them into the training of EAM-type potentials via Bayesian linear regression. Electron density models obtained for structures with a range of bonding types are shown to accurately reproduce the electron densities from DFT. Also, the resulting empirical potentials accurately reproduce DFT energies and forces of all the phases considered within the Ni-Al system. Properties not included in the training process, such as stacking fault energies, are sometimes not reproduced with the desired accuracy and the reasons for this are discussed. General regression issues, known to the machine learning community, are identified as the main difficulty facing further development of empirical potentials using this approach.EPSRC, Rolls-Royc

Applications of Monte Carlo Methods in Biology, Medicine and Other Fields of Science

This volume is an eclectic mix of applications of Monte Carlo methods in many fields of research should not be surprising, because of the ubiquitous use of these methods in many fields of human endeavor. In an attempt to focus attention on a manageable set of applications, the main thrust of this book is to emphasize applications of Monte Carlo simulation methods in biology and medicine