Gaussian process surrogates for failure detection: a Bayesian experimental design approach

An important task of uncertainty quantification is to identify {the probability of} undesired events, in particular, system failures, caused by various sources of uncertainties. In this work we consider the construction of Gaussian {process} surrogates for failure detection and failure probability estimation. In particular, we consider the situation that the underlying computer models are extremely expensive, and in this setting, determining the sampling points in the state space is of essential importance. We formulate the problem as an optimal experimental design for Bayesian inferences of the limit state (i.e., the failure boundary) and propose an efficient numerical scheme to solve the resulting optimization problem. In particular, the proposed limit-state inference method is capable of determining multiple sampling points at a time, and thus it is well suited for problems where multiple computer simulations can be performed in parallel. The accuracy and performance of the proposed method is demonstrated by both academic and practical examples

Stochastic Modeling of Central Apnea Events in Preterm Infants

A near-ubiquitous pathology in very low birth weight infants is neonatal apnea, breathing pauses with slowing of the heart and falling blood oxygen. Events of substantial duration occasionally occur after an infant is discharged from the neonatal intensive care unit (NICU). It is not known whether apneas result from a predictable process or from a stochastic process, but the observation that they occur in seemingly random clusters justifies the use of stochastic models. We use a hidden-Markov model to analyze the distribution of durations of apneas and the distribution of times between apneas. The model suggests the presence of four breathing states, ranging from very stable (with an average lifetime of 12 h) to very unstable (with an average lifetime of 10 s). Although the states themselves are not visible, the mathematical analysis gives estimates of the transition rates among these states. We have obtained these transition rates, and shown how they change with post-menstrual age; as expected, the residence time in the more stable breathing states increases with age. We also extrapolated the model to predict the frequency of very prolonged apnea during the first year of life. This paradigm-stochastic modeling of cardiorespiratory control in neonatal infants to estimate risk for severe clinical events-may be a first step toward personalized risk assessment for life threatening apnea events after NICU discharge

Advances in Raman and Surface-Enhanced Raman Spectroscopy: Instrumentation, Plasmonic Engineering and Biomolecular Sensing

Raman spectroscopy is a powerful technique for label-free molecular sensing and imaging in various fields. High molecular specificity, non-invasive sampling approach and the need for little or no sample preparation make Raman spectroscopy uniquely advantageous compared to other analytical techniques. However, Raman spectroscopy suffers from the intrinsic limitation of weak signal intensity. Therefore, time-sensitive studies such as diagnosis and clinical applications require improving the throughput of Raman instrumentation. Alternatively, surface-enhanced Raman scattering (SERS) improves the sensitivity by 10^6 to 10^14 times, making the weak Raman intensity no longer a limitation. Nevertheless, it is still a big challenge to engineer plasmonic substrates with high SERS enhancement, good uniformity and reproducibility. This thesis presents advances in: (1) Raman instrumentation towards high-throughput, environmental, biological and biomedical analysis; (2) SERS substrates with high enhancement factor (EF), uniformity and reproducibility; (3) biosensing applications including imaging of cell population and detection of biomolecules towards high time efficiency and sensitivity. In Raman instrumentation, we have built a high-throughput line-scan Raman microscope system and a novel parallel Raman microscope based on multiple-point active-illumination and wide-field hyperspectral data collection. Using the line-scan Raman microscope, we have performed chemical imaging of intact biological cells at the cell population level. We have also demonstrated more flexibility and throughput from the active-illumination Raman microscope in rapid chemical identification and screening of micro and nanoparticles and bacterial spores. Both Raman microscopes have been used to evaluate the large-area SERS uniformity of DC-sputtered gold nanoislands, a low-cost means to fabricate plasmonic substrates. In plasmonic engineering, we have introduced patterned nanoporous gold nanoparticles that feature 3-dimensional mesoporous network with pore size on the order of 10 nm throughput the sub-wavelength nanoparticles. We showed that the plasmonic resonance can be tuned by geometrical engineering of either the external nanoparticle size and shape or the nanoporous network. As an example, we have developed disk-shaped entities, also known as nanoporous gold disks (NPGD) with highly uniform and reproducible SERS EF exceeding 10^8. Label-free, multiplexed molecular sensing and imaging has been demonstrated on NPGD substrates. Using the line-scan Raman microscope and the NPGD substrates, we have successfully developed a label-free DNA hybridization sensor at the single-molecule level in microfluidics. We have observed discrete, individual DNA hybridization events by in situ monitoring the hybridization process using SERS. The advances and promising results presented in this thesis demonstrate potential impact in Raman/SERS imaging and sensing in environmental, biological and biomedical applications.Electrical and Computer Engineering, Department o

New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field

New perspectives and methods for stream learning in the presence of concept drift.

153 p.Applications that generate data in the form of fast streams from non-stationary environments, that is,those where the underlying phenomena change over time, are becoming increasingly prevalent. In thiskind of environments the probability density function of the data-generating process may change overtime, producing a drift. This causes that predictive models trained over these stream data become obsoleteand do not adapt suitably to the new distribution. Specially in online learning scenarios, there is apressing need for new algorithms that adapt to this change as fast as possible, while maintaining goodperformance scores. Examples of these applications include making inferences or predictions based onfinancial data, energy demand and climate data analysis, web usage or sensor network monitoring, andmalware/spam detection, among many others.Online learning and concept drift are two of the most hot topics in the recent literature due to theirrelevance for the so-called Big Data paradigm, where nowadays we can find an increasing number ofapplications based on training data continuously available, named as data streams. Thus, learning in nonstationaryenvironments requires adaptive or evolving approaches that can monitor and track theunderlying changes, and adapt a model to accommodate those changes accordingly. In this effort, Iprovide in this thesis a comprehensive state-of-the-art approaches as well as I identify the most relevantopen challenges in the literature, while focusing on addressing three of them by providing innovativeperspectives and methods.This thesis provides with a complete overview of several related fields, and tackles several openchallenges that have been identified in the very recent state of the art. Concretely, it presents aninnovative way to generate artificial diversity in ensembles, a set of necessary adaptations andimprovements for spiking neural networks in order to be used in online learning scenarios, and finally, adrift detector based on this former algorithm. All of these approaches together constitute an innovativework aimed at presenting new perspectives and methods for the field