Academic literature on the topic 'Continuous Time Bayesian Network'

Intrusion detection systems (IDSs) fall into two high-level categories: network-based systems (NIDS) that monitor network behaviors, and host-based systems (HIDS) that monitor system calls. In this work, we present a general technique for both systems. We use anomaly detection, which identifies patterns not conforming to a historic norm. In both types of systems, the rates of change vary dramatically over time (due to burstiness) and over components (due to service difference). To efficiently model such systems, we use continuous time Bayesian networks (CTBNs) and avoid specifying a fixed update interval common to discrete-time models. We build generative models from the normal training data, and abnormal behaviors are flagged based on their likelihood under this norm. For NIDS, we construct a hierarchical CTBN model for the network packet traces and use Rao-Blackwellized particle filtering to learn the parameters. We illustrate the power of our method through experiments on detecting real worms and identifying hosts on two publicly available network traces, the MAWI dataset and the LBNL dataset. For HIDS, we develop a novel learning method to deal with the finite resolution of system log file time stamps, without losing the benefits of our continuous time model. We demonstrate the method by detecting intrusions in the DARPA 1998 BSM dataset.

We introduce a novel event-driven continuous time Bayesian network (ECTBN) representation to model situations where a system's state variables could be influenced by occurrences of events of various types. In this way, the model parameters and graphical structure capture not only potential “causal” dynamics of system evolution but also the influence of event occurrences that may be interventions. We propose a greedy search procedure for structure learning based on the BIC score for a special class of ECTBNs, showing that it is asymptotically consistent and also effective for limited data. We demonstrate the power of the representation by applying it to model paths out of poverty for clients of CityLink Center, an integrated social service provider in Cincinnati, USA. Here the ECTBN formulation captures the effect of classes/counseling sessions on an individual's life outcome areas such as education, transportation, employment and financial education.

A continuous-time Markov process (CTMP) is a collection of variables indexed by a continuous quantity, time. It obeys the Markov property that the distribution over a future variable is independent of past variables given the state at the present time. We introduce continuous-time Markov process representations and algorithms for filtering, smoothing, expected sufficient statistics calculations, and model estimation, assuming no prior knowledge of continuous-time processes but some basic knowledge of probability and statistics. We begin by describing "flat" or unstructured Markov processes and then move to structured Markov processes (those arising from state spaces consisting of assignments to variables) including Kronecker, decision-diagram, and continuous-time Bayesian network representations. We provide the first connection between decision-diagrams and continuous-time Bayesian networks.

Streaming data are relevant to finance, computer science, and engineering, while they are becoming increasingly important to medicine and biology. Continuous time Bayesian networks are designed for analyzing efficiently multivariate streaming data, exploiting the conditional independencies in continuous time homogeneous Markov processes. Continuous time Bayesian network classifiers are a specialization of continuous time Bayesian networks designed for multivariate streaming data classification when time duration of events matters and the class occurs in the future. Continuous time Bayesian network classifiers are presented and analyzed. Structural learning is introduced for this class of models when complete data are available. A conditional log-likelihood scoring is derived to improve the marginal log- likelihood structural learning on continuous time Bayesian net- work classifiers. The expectation maximization algorithm is developed to address the unsupervised learning of continuous time Bayesian network classifiers when the class is unknown. Performances of continuous time Bayesian network classifiers in the case of classification and clustering are analyzed with the help of a rich set of numerical experiments on synthetic and real data sets. Continuous time Bayesian network classifiers learned by maximizing marginal log-likelihood and conditional log-likelihood are compared with continuous time naive Bayes and dynamic Bayesian networks. Results show that the conditional log-likelihood scoring combined with Bayesian parameter estimation outperforms marginal log-likelihood scoring and dynamic Bayesian networks in the case of supervised classification. Conditional log-likelihood scoring becomes even more effective when the amount of available data is limited. Continuous time Bayesian network classifiers outperform dynamic Bayesian networks even on data sets generated from dis- crete time models. Clustering results show that in the case of unsupervised learning the marginal log-likelihood score is the most effective way to learn continuous time Bayesian network classifiers. Continuous time models again outperform dynamic Bayesian networks even when applied on discrete time data sets. A Java software toolkit implementing the main theoretical achievements of the thesis has been designed and developed under the name of the CTBNCToolkit. It provides a free stand- alone toolkit for multivariate trajectory classification and an open source library, which can be extend in accordance with the GPL v.2.0 license. The CTBNCToolkit allows classification and clustering of multivariate trajectories using continuous time Bayesian network classifiers. Structural learning, maximizing marginal log-likelihood and conditional log-likelihood scores, is provided.

Thesis (Ph. D.)--University of California, Riverside, 2009.
Includes abstract. Title from first page of PDF file (viewed March 8, 2010). Available via ProQuest Digital Dissertations. Includes bibliographical references (p. 130-133). Also issued in print.

Dynamic aspects of gene regulatory networks are typically investigated by measuring system variables at multiple time points. Current state-of-the-art computational approaches for reconstructing gene networks directly build on such data, making a strong assumption that the system evolves in a synchronous fashion at fixed points in time. However, nowadays omics data are being generated with increasing time course granularity. Thus, modellers now have the possibility to represent the system as evolving in continuous time and improve the models' expressiveness. Continuous time Bayesian networks is proposed as a new approach for gene network reconstruction from time course expression data. Their performance was compared to two state-of-the-art methods: dynamic Bayesian networks and Granger causality analysis. On simulated data methods's comparison was carried out for networks of increasing dimension, for measurements taken at different time granularity densities and for measurements evenly vs. unevenly spaced over time. Continuous time Bayesian networks outperformed the other methods in terms of the accuracy of regulatory interactions learnt from data for all network dimensions. Furthermore, their performance degraded smoothly as the dimension of the network increased. Continuous time Bayesian network were significantly better than dynamic Bayesian networks for all time granularities tested and better than Granger causality for dense time series. Both continuous time Bayesian networks and Granger causality performed robustly for unevenly spaced time series, with no significant loss of performance compared to the evenly spaced case, while the same did not hold true for dynamic Bayesian networks. The comparison included the IRMA experimental datasets which confirmed the effectiveness of the proposed method. Continuous time Bayesian networks were then applied to elucidate the regulatory mechanisms controlling murine T helper 17 (Th17) cell differentiation and were found to be effective in discovering well-known regulatory mechanisms as well as new plausible biological insights. Continuous time Bayesian networks resulted to be effective on networks of both small and big dimensions and particularly feasible when the measurements are not evenly distributed over time. Reconstruction of the murine Th17 cell differentiation network using continuous time Bayesian networks revealed several autocrine loops suggesting that Th17 cells may be auto regulating their own differentiation process.

L'analisi dell'enorme quantità di dati finanziari, messi a disposizione dai mercati elettronici, richiede lo sviluppo di nuovi modelli e tecniche per estrarre efficacemente la conoscenza da utilizzare in un processo decisionale informato. Lo scopo della tesi concerne l'introduzione di modelli grafici probabilistici utilizzati per il ragionamento e l'attività decisionale in tale contesto. Nella prima parte della tesi viene presentato un framework che utilizza le reti Bayesiane per effettuare l'analisi e l'ottimizzazione di portafoglio in maniera olistica. In particolare, esso sfrutta, da un lato, la capacità delle reti Bayesiane di rappresentare distribuzioni di probabilità in modo compatto ed efficiente per modellare il portafoglio e, dall'altro, la loro capacità di fare inferenza per ottimizzare il portafoglio secondo diversi scenari economici. In molti casi, si ha la necessità di ragionare in merito a scenari di mercato nel tempo, ossia si vuole rispondere a domande che coinvolgono distribuzioni di probabilità che evolvono nel tempo. Le reti Bayesiane a tempo continuo possono essere utilizzate in questo contesto. Nella seconda parte della tesi viene mostrato il loro utilizzo per affrontare problemi finanziari reali e vengono descritte due importanti estensioni. La prima estensione riguarda il problema di classificazione, in particolare vengono introdotti un algoritmo per apprendere tali classificatori da Big Data e il loro utilizzo nel contesto di previsione dei cambi valutari ad alta frequenza. La seconda estensione concerne l'apprendimento delle reti Bayesiane a tempo continuo in domini non stazionari, in cui vengono modellate esplicitamente le dipendenze statistiche presenti nelle serie temporali multivariate consentendo loro di cambiare nel corso del tempo. Nella terza parte della tesi viene descritto l'uso delle reti Bayesiane a tempo continuo nell'ambito dei processi decisionali di Markov, i quali consentono di modellare processi decisionali sequenziali in condizioni di incertezza. In particolare, viene introdotto un metodo per il controllo di sistemi dinamici a tempo continuo che sfrutta le proprietà additive e contestuali per scalare efficacemente su grandi spazi degli stati. Infine, vengono mostrate le prestazioni di tale metodo in un contesto significativo di trading.
The analysis of the huge amount of financial data, made available by electronic markets, calls for new models and techniques to effectively extract knowledge to be exploited in an informed decision-making process. The aim of this thesis is to introduce probabilistic graphical models that can be used to reason and to perform actions in such a context. In the first part of this thesis, we present a framework which exploits Bayesian networks to perform portfolio analysis and optimization in a holistic way. It leverages on the compact and efficient representation of high dimensional probability distributions offered by Bayesian networks and their ability to perform evidential reasoning in order to optimize the portfolio according to different economic scenarios. In many cases, we would like to reason about the market change, i.e. we would like to express queries as probability distributions over time. Continuous time Bayesian networks can be used to address this issue. In the second part of the thesis, we show how it is possible to use this model to tackle real financial problems and we describe two notable extensions. The first one concerns classification, where we introduce an algorithm for learning these classifiers from Big Data, and we describe their straightforward application to the foreign exchange prediction problem in the high frequency domain. The second one is related to non-stationary domains, where we explicitly model the presence of statistical dependencies in multivariate time-series while allowing them to change over time. In the third part of the thesis, we describe the use of continuous time Bayesian networks within the Markov decision process framework, which provides a model for sequential decision-making under uncertainty. We introduce a method to control continuous time dynamic systems, based on this framework, that relies on additive and context-specific features to scale up to large state spaces. Finally, we show the performances of our method in a simplified, but meaningful trading domain.

Reasoning about evolution of system in time is both an important and challenging task. We are interested in probability distributions over time of events where often observations are irregularly spaced over time. Probabilistic models have been widely used to accomplish this task but they have some limits. Indeed, Hidden Markov Models and Dynamic Bayesian Networks in general require the specification of a time granularity between consecutive observations. This requirement leads to computationally inefficient learning and inference procedures when the adopted time granularity is finer than the time spent between consecutive observations, and to possible losses of information in the opposite case. The framework of Continuous Time Bayesian Networks (CTBN) overcomes this limit, allowing the representation of temporal dynamics over a structured state space. In this dissertation an overview of the semantic and inference aspects of the framework of the CTBNs is proposed. The limits of exact inference are overcome using approximate inference, in particular the cluster-graph message passing algorithm and the Gibbs Sampling has been investigated. The CTBN has been applied to a real case study of diagnosis of cardiogenic heart failure, developed in collaboration with domain experts. Moving from the task of simply reasoning under uncertainty, to the task of deciding how to act in the world, a part of the dissertation is devoted to graphical models that allow the inclusion of decisions. We describe Influence Diagrams, which extend Bayesian Networks by introducing decisions and utilities. We then discuss an approach for approximate representation of optimal strategies in influence diagrams. The contributions of the dissertation are the following: design and development of a CTBN software package implementing two of the most important inference algorithms (Expectation Propagation and Gibbs Sampling), development of a realistic diagnosis scenario of cardiogenic heart failure (to the best of our knowledge it is the first clinical application of this type), the approach of information enhancement to reduce the domain of the policy in large influence diagrams together with an important contribution concerning the identification of informational links to add in the graph.

Continuous time Markov chains (CTMCs) are a flexible class of stochastic models that have been employed in a wide range of applications from timing of computer protocols, through analysis of reliability in engineering, to models of biochemical networks in molecular biology. These models are defined as a state system with continuous time transitions between the states. Extensive work has been historically performed to enable convenient and flexible definition, simulation, and analysis of continuous time Markov chains. This thesis considers the problem of Bayesian parameter inference on these models and investigates computational methodologies to enable such inference. Bayesian inference over continuous time Markov chains is particularly challenging as the likelihood cannot be evaluated in a closed form. To overcome the statistical problems associated with evaluation of the likelihood, advanced algorithms based on Monte Carlo have been used to enable Bayesian inference without explicit evaluation of the likelihoods. An additional class of approximation methods has been suggested to handle such inference problems, known as approximate Bayesian computation. Novel Markov chain Monte Carlo (MCMC) approaches were recently proposed to allow exact inference. The contribution of this thesis is in discussion of the techniques and challenges in implementing these inference methods and performing an extensive comparison of these approaches on two case studies in systems biology. We investigate how the algorithms can be designed and tuned to work on CTMC models, and to achieve an accurate estimate of the posteriors with reasonable computational cost. Through this comparison, we investigate how to avoid some practical issues with accuracy and computational cost, for example by selecting an optimal proposal distribution and introducing a resampling step within the sequential Monte-Carlo method. Within the implementation of the ABC methods we investigate using an adaptive tolerance schedule to maximise the efficiency of the algorithm and in order to reduce the computational cost.

This thesis concerns the statistical modelling of animal movement paths given observed GPS locations. With observations being in discrete time, mechanistic models of movement are often formulated as such. This popularity remains despite an inability to compare analyses through scale invariance and common problems handling irregularly timed observations. A natural solution is to formulate in continuous time, yet uptake of this has been slow, often excused by a difficulty in interpreting the ‘instantaneous’ parameters associated with a continuous-time model. The aim here was to bolster usage by developing a continuous-time model with interpretable parameters, similar to those of popular discrete-time models that use turning angles and step lengths to describe the movement process. Movement is defined by a continuous-time, joint bearing and speed process, the parameters of which are dependent on a continuous-time behavioural switching process, thus creating a flexible class of movement models. Further, we allow for the observed locations derived from this process to have unknown error. Markov chain Monte Carlo inference is presented for parameters given irregular, noisy observations. The approach involves augmenting the observed locations with a reconstruction of the underlying continuous-time process. Example implementations showcasing this method are given featuring simulated and real datasets. Data from elk (Cervus elaphus), which have previously been modelled in discrete time, demonstrate the interpretable nature of the model, finding clear differences in behaviour over time and insights into short-term behaviour that could not have been obtained in discrete time. Observations from reindeer (Rangifer tarandus) reveal the effect observation error has on the identification of large turning angles—a feature often inferred in discrete-time modelling. Scalability to realistically large datasets is shown for lesser black-backed gull (Larus fuscus) data.

Doctor of Philosophy
Department of Computing and Information Sciences
William Henry Hsu
Topic models are probabilistic models for discovering topical themes in collections of documents. In real world applications, these models provide us with the means of organizing what would otherwise be unstructured collections. They can help us cluster a huge collection into different topics or find a subset of the collection that resembles the topical theme found in an article at hand. The first wave of topic models developed were able to discover the prevailing topics in a big collection of documents spanning a period of time. It was later realized that these time-invariant models were not capable of modeling 1) the time varying number of topics they discover and 2) the time changing structure of these topics. Few models were developed to address this two deficiencies. The online-hierarchical Dirichlet process models the documents with a time varying number of topics. It varies the structure of the topics over time as well. However, it relies on document order, not timestamps to evolve the model over time. The continuous-time dynamic topic model evolves topic structure in continuous-time. However, it uses a fixed number of topics over time. In this dissertation, I present a model, the continuous-time infinite dynamic topic model, that combines the advantages of these two models 1) the online-hierarchical Dirichlet process, and 2) the continuous-time dynamic topic model. More specifically, the model I present is a probabilistic topic model that does the following: 1) it changes the number of topics over continuous time, and 2) it changes the topic structure over continuous-time. I compared the model I developed with the two other models with different setting values. The results obtained were favorable to my model and showed the need for having a model that has a continuous-time varying number of topics and topic structure.

Minimally invasive continuous glucose monitoring (CGM) sensors are wearable medical devices that provide frequent (e.g., 1-5 min sampling rate) real-time measurements of glucose concentration for several consecutive days. This can be of great help in the daily management of diabetes. Most of the CGM systems commercially available today have a wire-based electrochemical sensor, usually placed in the subcutaneous tissue, which measures a "raw" electrical current signal via a glucose-oxidase electrochemical reaction. Observations of the raw electrical signal are frequently revealed by the sensor on a fine, uniformly spaced, time grid. These samples of electrical nature are in real-time converted to interstitial glucose (IG) concentration levels through a calibration process by fitting a few blood glucose (BG) concentration measurements, sparsely collected by the patient through fingerprick. Usually, for coping with such a process, CGM sensor manufacturers employ linear calibration models to approximate, albeit in limited time-intervals, the nonlinear relationship between electrical signal and glucose concentration. Thus, on the one hand, frequent calibrations (e.g., two per day) are required to guarantee a good sensor accuracy. On the other, each calibration requires patients to add uncomfortable extra actions to the many already needed in the routine of diabetes management. The aim of this thesis is to develop new calibration algorithms for minimally invasive CGM sensors able to ensure good sensor accuracy with the minimum number of calibrations. In particular, we propose i) to replace the time-invariant gain and offset conventionally used by the linear calibration models with more sophisticated time-varying functions valid for multiple-day periods, with unknown model parameters for which an a priori statistical description is available from independent training sets; ii) to numerically estimate the calibration model parameters by means of a Bayesian estimation procedure that exploits the a priori information on model parameters in addition to some BG samples sparsely collected by the patient. The thesis is organized in 6 chapters. In Chapter 1, after a background introduction on CGM sensor technologies, the calibration problem is illustrated. Then, some state-of-art calibration techniques are briefly discussed with their open problems, which result in the aims of the thesis illustrated at the end of the chapter. In Chapter 2, the datasets used for the implementation of the calibration techniques are described, together with the performance metrics and the statistical analysis tools which will be employed to assess the quality of the results. In Chapter 3, we illustrate a recently proposed calibration algorithm (Vet- toretti et al., IEEE Trans Biomed Eng 2016), which represents the starting point of the study proposed in this thesis. In particular, we demonstrate that, thanks to the development of a time-varying day-specific Bayesian prior, the algorithm can become able to reduce the calibration frequency from two to one per day. However, the linear calibration model used by the algorithm has domain of validity limited to certain time intervals, not allowing to further reduce calibrations to less then one per day and calling for the development of a new calibration model valid for multiple-day periods like that developed in the remainder of this thesis. In Chapter 4, a novel Bayesian calibration algorithm working in a multi-day framework (referred to as Bayesian multi-day, BMD, calibration algorithm) is presented. It is based on a multiple-day model of sensor time-variability with second order statistical priors on its unknown parameters. In each patient-sensor realization, the numerical values of the calibration model parameters are determined by a Bayesian estimation procedure exploiting the BG samples sparsely collected by the patient. In addition, the distortion introduced by the BG-to-IG kinetics is compensated during parameter identification via non-parametric deconvolution. The BMD calibration algorithm is applied to two datasets acquired with the "present-generation" Dexcom (Dexcom Inc., San Diego, CA) G4 Platinum (DG4P) CGM sensor and a "next-generation" Dexcom CGM sensor prototype (NGD). In the DG4P dataset, results show that, despite the reduction of calibration frequency (on average from 2 per day to 0.25 per day), the BMD calibration algorithm significantly improves sensor accuracy compared to the manufacturer calibration algorithm. In the NGD dataset, performance is even better than that of present generation, allowing to further reduce calibrations toward zero. In Chapter 5, we analyze the potential margins for improvement of the BMD calibration algorithm and propose a further extension of the method. In particular, to cope with the inter-sensor and inter-subject variability, we propose a multi-model approach and a Bayesian model selection framework (referred to as multi-model Bayesian framework, MMBF) in which the most likely calibration model is chosen among a finite set of candidates. A preliminary assessment of the MMBF is conducted on synthetic data generated by a well-established type 1 diabetes simulation model. Results show a statistically significant accuracy improvement compared to the use of a unique calibration model. Finally, the major findings of the work carried out in this thesis, possible applications and margins for improvement are summarized in Chapter 6.
I sensori minimamente invasivi per il monitoraggio in continua della glicemia, indicati con l’acronimo CGM (continuous glucose monitoring), sono dei dispositivi medici indossabili capaci di misurare la glicemia in tempo reale, ogni 1-5 minuti, per più giorni consecutivi. Questo tipo di misura fornisce un profilo di glicemia quasi continuo che risulta essere un’informazione molto utile per la gestione quotidiana della terapia del diabete. La maggior parte dei dispositivi CGM ad oggi disponibili nel mercato dispongono di un sensore di tipo elettrochimico, solitamente inserito nel tessuto sottocutaneo, che misura una corrente elettrica generata dalla reazione chimica di glucosio-ossidasi. Le misure di corrente elettrica sono fornite dal sensore con campionamento uniforme ad elevata frequenza temporale e vengono convertite in tempo reale in valori di glicemia interstiziale attraverso un processo di calibrazione. La procedura di calibrazione prevede l’acquisizione da parte del paziente di qualche misura di glicemia plasmatica di riferimento tramite dispositivi pungidito. Solitamente, le aziende produttrici di sensori CGM implementano un processo di calibrazione basato su un modello di tipo lineare che approssima, sebbene in intervalli di tempo di durata limitata, la più complessa relazione tra corrente elettrica e glicemia. Di conseguenza, si rendono necessarie frequenti calibrazioni (per esempio, due al giorno) per aggiornare i parametri del modello di calibrazione e garantire una buona accuratezza di misura. Tuttavia, ogni calibrazione prevede l’acquisizione da parte del paziente di misure di glicemia tramite dispositivi pungidito. Questo aumenta la già numerosa lista di azioni che i pazienti devono svolgere quotidianamente per gestire la loro terapia. Lo scopo di questa tesi è quello di sviluppare un nuovo algoritmo di calibrazione per sensori CGM minimamente invasivi capace di garantire una buona accuratezza di misura con il minimo numero di calibrazioni. Nello specifico, si propone i) di sostituire il guadagno ed offset tempo-invarianti solitamente utilizzati nei modelli di calibrazione di tipo lineare con delle funzioni tempo-varianti, capaci di descrivere il comportamento del sensore per intervalli di tempo di più giorni, e per cui sia disponibile dell’informazione a priori riguardante i parametri incogniti; ii) di stimare il valore numerico dei parametri del modello di calibrazione con metodo Bayesiano, sfruttando l’informazione a priori sui parametri di calibrazione in aggiunta ad alcune misure di glicemia plasmatica di riferimento. La tesi è organizzata in 6 capitoli. Nel Capitolo 1, dopo un’introduzione sulle tecnologie dei sensori CGM, viene illustrato il problema della calibrazione. In seguito, vengono discusse alcune tecniche di calibrazione che rappresentano lo stato dell’arte ed i loro problemi aperti, che risultano negli scopi della tesi descritti alla fine del capitolo. Nel Capitolo 2 vengono descritti i dataset utilizzati per l’implementazione delle tecniche di calibrazione. Inoltre, vengono illustrate le metriche di accuratezza e le tecniche di analisi statistica utilizzate per analizzare la qualità dei risultati. Nel Capitolo 3 viene illustrato un algoritmo di calibrazione recentemente proposto in letteratura (Vettoretti et al., IEEE, Trans Biomed Eng 2016). Questo algoritmo rappresenta il punto di partenza dello studio svolto in questa tesi. Più precisamente, viene dimostrato che, grazie all’utilizzo di un prior Bayesiano specifico per ogni giorno di utilizzo, l’algoritmo diventa efficace nel ridurre le calibrazioni da due a una al giorno senza perdita di accuratezza. Tuttavia, il modello lineare di calibrazione utilizzato dall’algoritmo ha dominio di validità limitato a brevi intervalli di tempo tra due calibrazioni successive, rendendo impossibile l’ulteriore riduzione delle calibrazioni a meno di una al giorno senza perdita di accuratezza. Questo determina la necessità di sviluppare un nuovo modello di calibrazione valido per intervalli di tempo più estesi, fino a più giorni consecutivi, come quello sviluppato nel resto di questa tesi. Nel Capitolo 4 viene presentato un nuovo algoritmo di calibrazione di tipo Bayesiano (Bayesian multi-day, BMD). L’algoritmo si basa su un modello della tempo-varianza delle caratteristiche del sensore nei suoi giorni di utilizzo e sulla disponibilità di informazione statistica a priori sui suoi parametri incogniti. Per ogni coppia paziente-sensore, il valore numerico dei parametri del modello è determinato tramite stima Bayesiana sfruttando alcune misure plasmatiche di riferimento acquisite dal paziente con dispositivi pungidito. Inoltre, durante la stima dei parametri, la dinamica introdotta dalla cinetica plasma-interstizio viene compensata tramite deconvoluzione nonparametrica. L’algoritmo di calibrazione BMD viene applicato a due differenti set di dati acquisiti con il sensore commerciale Dexcom (Dexocm Inc., San Diego, CA) G4 Platinum (DG4P) e con un prototipo di sensore Dexcom di nuova generazione (NGD). Nei dati acquisiti con il sensore DG4P, i risultati dimostrano che, nonostante le calibrazioni vengano ridotte (in media da 2 al giorno a 0.25 al giorno), l’ algoritmo BMD migliora significativamente l’accuratezza del sensore rispetto all’algoritmo di calibrazione utilizzato dall’azienda produttrice del sensore. Nei dati acquisiti con il sensore NGD, i risultati sono ancora migliori, permettendo di ridurre ulteriormente le calibrazioni fino a zero. Nel Capitolo 5 vengono analizzati i potenziali margini di miglioramento dell’algoritmo di calibrazione BMD discusso nel capitolo precedente e viene proposta un’ulteriore estensione dello stesso. In particolare, per meglio gestire la variabilità tra sensori e tra soggetti, viene proposto un approccio di calibrazione multi-modello e un metodo Bayesiano di selezione del modello (Multi-model Bayesian framework, MMBF) in cui il modello di calibrazione più probabile a posteriori viene scelto tra un set di possibili candidati. Tale approccio multi-modello viene analizzato in via preliminare su un set di dati simulati generati da un simulatore del paziente diabetico di tipo 1 ben noto in letteratura. I risultati dimostrano che l’accuratezza del sensore migliora in modo significativo con MMBF rispetto ad utilizzare un unico modello di calibrazione. Infine, nel Capitolo 6 vengono riassunti i principali risultati ottenuti in questa tesi, le possibili applicazioni, e i margini di miglioramento per gli sviluppi futuri.

While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.

This article deals with curve registration, which refers to methods for aligning prominent features in a set of curves by transforming their abscissa variables. It first illustrates the concepts of amplitude and phase variation schematically and with real data before defining the time-warping functions and their functional inverse. It then describes the decomposition of total mean squared variation into separate amplitude and phase components, along with an R2 measure of the proportion of functional variation due to phase in a sample of curves. It also considers landmark registration, novel ways of defining curve features, continuous registration, and methods based on structured models for amplitude and phase variation combined with more statistically oriented fitting methods such as maximum likelihood or Bayesian estimation. The article concludes with a brief survey of software resources for registration.

To meet the expectations raised by the terms Industrie 4.0, Industrial Internet and Internet of Things, real innovations are necessary, which can be brought about by information processing systems working autonomously. Owing to their growing complexity and their embedding in complex environments, their design becomes increasingly critical. Thus, the topics addressed in this book span from verification and validation of safety-related control software and suitable hardware designed for verifiability to be deployed in embedded systems over approaches to suppress electromagnetic interferences to strategies for network routing based on centrality measures and continuous re-authentication in peer-to-peer networks. Methods of neural and evolutionary computing are employed to aid diagnosing retinopathy of prematurity, to invert matrices and to solve non-deterministic polynomial-time hard problems. In natural language processing, interface problems between humans and machines are solved with g...

AbstractMost of the sampled data in complex industrial processes are sequential in time. Therefore, the traditional BN learning mechanisms have limitations on the value of probability and cannot be applied to the time series. The model established in Chap. 10.1007/978-981-16-8044-1_13 is a graphical model similar to a Bayesian network, but its parameter learning method can only handle the discrete variables. This chapter aims at the probabilistic graphical model directly for the continuous process variables, which avoids the assumption of discrete or Gaussian distributions.

Non-stationary continuous time Bayesian networks are introduced. They allow the parents set of each node in a continuous time Bayesian network to change over time. Structural learning of nonstationary continuous time Bayesian networks is developed under different knowledge settings. A macroeconomic dataset is used to assess the effectiveness of learning non-stationary continuous time Bayesian networks from real-world data.

Review question / Objective: At this time, a comprehensive systematic review and network meta-analysis (NMA) was conducted to: (1) fill the research gap by giving rankings on treatment efficacy; (2) provide statistical evidence of not head-to-head comparisons; (3) seek out the best and up-to-date therapeutic strategy reported in latest RCTs; (4) address potential adverse events (AEs) of available treatments. Condition being studied: The incidence of glioblastoma (GBM) increases with age, until now, there has been less evidence on the optimal treatments for elderly GBM since only general GBM populations were included in clinical trials. Given the poor survival of elderly GBM, we collected randomized controlled trials about newly diagnosed GBM (ndGBM) and recurrent GBM, and conducted a Bayesian network meta-analysis on ndGBM regarding overall survival (OS) and progression-free survival (PFS). We revealed TTF + TMZ and TMZ + HFRT were likely to be best treatments for OS; BEV + HFRT and TMZ + HFRT were likely to be best options for PFS. Current study is the most comprehensive and powered network analysis on elderly GBM until now, it also provides more insights for elderly GBM management.

Review question / Objective: This review aims at conducting a network meta-analysis to assess the potential therapeutic effectiveness and safety of acupuncture therapy for the treatment of MGH. Condition being studied: MGH is a benign breast disease caused by excessive growth of mammary duct epithelial cells and interstitial fibers. Its prevalence rate among women of childbearing age is about 13.5-42%, accounting for 99.3% of the total number of patients with breast related diseases, and its possibility of developing breast cancer can reach 5-10%. Breast hyperplasia can cause clinical symptoms such as breast pain, breast lump, nipple pigmentation and mood fluctuation, which brings severe physical and mental burden to patients. Modern medicine believes that the pathogenesis of MGH is related to sexual hormone disorder secondary to hypothalamus pituitary ovary axis dysfunction.At present, the treatment options of MGH are limited and not completely effective. The commonly used drugs in clinical practice, such as tamoxifen, danazol and goserelin, are expensive, which may lead to breast pain, swelling and increase of interstitial fibrous nodules, and the long-term use of MGH has huge side effects. The clinical guidelines recommend that the use time should be 2 to 6 months. Therefore, it is necessary to seek a treatment method of MGH that is effective, stable and safe.

The Northern Great Plains Inventory and Monitoring Network (NGPN) began monitoring water quality in the Knife River at Knife River Indian Villages National Historic Site (KNRI) in 2013, with the assistance of the U.S. Geological Survey (USGS). This report summarizes the data collected during the 2019 ice-free season (April 18 through October 31) for streamflow, water temperature, dissolved oxygen, specific conductance, and pH. This was the third season of continuous monitoring. 2019 began as moderately dry year until discharge on the Knife River peaked at 1,900 cubic feet per second in September following unusually heavy precipitation. There was considerable seasonal variation in all water quality measures. A summary of our results can be found in Descriptive Statistics Summary tables for the ice-free season (Table 2) and for each month (Table 3). Notably, water temperature exceeded state standards (Table 1) in summer months although these exceedances made up less than 1% of all records. Additionally, dissolved oxygen was observed below state standards twice on the same day in June, but Knife River still met the dissolved oxygen standard due to the brief nature of this deficiency. NGPN’s collaboration with USGS supported real-time and archived access to this data through the USGS National Water Information System Website KNIFE RIVER NR STANTON, ND - USGS Water Data for the Nation, where it remains available to the public

The article investigates functional techniques of extralinguistic expression in multimedia texts; the effectiveness of figurative expressions as a reaction to modern events in Ukraine and their influence on the formation of public opinion is shown. Publications of journalists, broadcasts of media resonators, experts, public figures, politicians, readers are analyzed. The language of the media plays a key role in shaping the worldview of the young political elite in the first place. The essence of each statement is a focused thought that reacts to events in the world or in one’s own country. The most popular platform for mass information and social interaction is, first of all, network journalism, which is characterized by mobility and unlimited time and space. Authors have complete freedom to express their views in direct language, including their own word formation. Phonetic, lexical, phraseological and stylistic means of speech create expression of the text. A figurative word, a good aphorism or proverb, a paraphrased expression, etc. enhance the effectiveness of a multimedia text. This is especially important for headlines that simultaneously inform and influence the views of millions of readers. Given the wide range of issues raised by the Internet as a medium, research in this area is interdisciplinary. The science of information, combining language and social communication, is at the forefront of global interactions. The Internet is an effective source of knowledge and a forum for free thought. Nonlinear texts (hypertexts) – «branching texts or texts that perform actions on request», multimedia texts change the principles of information collection, storage and dissemination, involving billions of readers in the discussion of global issues. Mastering the word is not an easy task if the author of the publication is not well-read, is not deep in the topic, does not know the psychology of the audience for which he writes. Therefore, the study of media broadcasting is an important component of the professional training of future journalists. The functions of the language of the media require the authors to make the right statements and convincing arguments in the text. Journalism education is not only knowledge of imperative and dispositive norms, but also apodictic ones. In practice, this means that there are rules in media creativity that are based on logical necessity. Apodicticity is the first sign of impressive language on the platform of print or electronic media. Social expression is a combination of creative abilities and linguistic competencies that a journalist realizes in his activity. Creative self-expression is realized in a set of many important factors in the media: the choice of topic, convincing arguments, logical presentation of ideas and deep philological education. Linguistic art, in contrast to painting, music, sculpture, accumulates all visual, auditory, tactile and empathic sensations in a universal sign – the word. The choice of the word for the reproduction of sensory and semantic meanings, its competent use in the appropriate context distinguishes the journalist-intellectual from other participants in forums, round tables, analytical or entertainment programs. Expressive speech in the media is a product of the intellect (ability to think) of all those who write on socio-political or economic topics. In the same plane with him – intelligence (awareness, prudence), the first sign of which (according to Ivan Ogienko) is a good knowledge of the language. Intellectual language is an important means of organizing a journalistic text. It, on the one hand, logically conveys the author’s thoughts, and on the other – encourages the reader to reflect and comprehend what is read. The richness of language is accumulated through continuous self-education and interesting communication. Studies of social expression as an important factor influencing the formation of public consciousness should open up new facets of rational and emotional media broadcasting; to trace physical and psychological reactions to communicative mimicry in the media. Speech mimicry as one of the methods of disguise is increasingly becoming a dangerous factor in manipulating the media. Mimicry is an unprincipled adaptation to the surrounding social conditions; one of the most famous examples of an animal characterized by mimicry (change of protective color and shape) is a chameleon. In a figurative sense, chameleons are called adaptive journalists. Observations show that mimicry in politics is to some extent a kind of game that, like every game, is always conditional and artificial.

In the face of the multiple shocks currently experienced by the domestic economy (resulting from the drop in oil prices and the appearance of a global pandemic), the Colombian financial system is in a position of sound solvency and adequate liquidity. At the same time, credit quality has been recovering and the exposure of credit institutions to firms with currency mismatches has declined relative to previous episodes of sudden drops in oil prices. These trends are reflected in the recent fading of red and blue tonalities in the performance and credit risk segments of the risk heatmaps in Graphs A and B.1 Naturally, the sudden, unanticipated change in macroeconomic conditions has caused the appearance of vulnerabilities for short-term financial stability. These vulnerabilities require close and continuous monitoring on the part of economic authorities. The main vulnerability is the response of credit and credit risk to a potential, temporarily extreme macroeconomic situation in the context of: (i) recently increased exposure of some banks to household sector, and (ii) reductions in net interest income that have led to a decline in the profitability of the banking business in the recent past. Furthermore, as a consequence of greater uncertainty and risk aversion, occasional problems may arise in the distribution of liquidity between agents and financial markets. With regards to local markets, spikes have been registered in the volatility of public and private fixed income securities in recent weeks that are consistent with the behavior of the international markets and have had a significant impact on the liquidity of those instruments (red portions in the most recent past of some market risk items on the map in Graph A). In order to adopt a forward-looking approach to those vulnerabilities, this Report presents a stress test that evaluates the resilience of credit institutions in the event of a hypothetical scenario thatseeks to simulate an extreme version of current macroeconomic conditions. The scenario assumes a hypothetical negative growth that is temporarily strong but recovers going into the middle of the coming year and has extreme effects on credit quality. The results suggest that credit institutions have the ability to withstand a significant deterioration in economic conditions in the short term. Even though there could be a strong impact on credit, liquidity, and profitability under the scenario being considered, aggregate capital ratios would probably remain at above their regulatory limits over the horizon of a year. In this context, the recent measures taken by both Banco de la República and the Office of the Financial Superintendent of Colombia that are intended to help preserve the financial stability of the Colombian economy become highly relevant. In compliance with its constitutional objectives and in coordination with the financial system’s security network, Banco de la República will continue to closely monitor the outlook for financial stability at this juncture and will make the decisions that are necessary to ensure the proper functioning of the economy, facilitate the flow of sufficient credit and liquidity resources, and further the smooth functioning of the payment system. Juan José Echavarría Governor