542 research outputs found

    On the Utility of Representation Learning Algorithms for Myoelectric Interfacing

    Get PDF
    Electrical activity produced by muscles during voluntary movement is a reflection of the firing patterns of relevant motor neurons and, by extension, the latent motor intent driving the movement. Once transduced via electromyography (EMG) and converted into digital form, this activity can be processed to provide an estimate of the original motor intent and is as such a feasible basis for non-invasive efferent neural interfacing. EMG-based motor intent decoding has so far received the most attention in the field of upper-limb prosthetics, where alternative means of interfacing are scarce and the utility of better control apparent. Whereas myoelectric prostheses have been available since the 1960s, available EMG control interfaces still lag behind the mechanical capabilities of the artificial limbs they are intended to steer—a gap at least partially due to limitations in current methods for translating EMG into appropriate motion commands. As the relationship between EMG signals and concurrent effector kinematics is highly non-linear and apparently stochastic, finding ways to accurately extract and combine relevant information from across electrode sites is still an active area of inquiry.This dissertation comprises an introduction and eight papers that explore issues afflicting the status quo of myoelectric decoding and possible solutions, all related through their use of learning algorithms and deep Artificial Neural Network (ANN) models. Paper I presents a Convolutional Neural Network (CNN) for multi-label movement decoding of high-density surface EMG (HD-sEMG) signals. Inspired by the successful use of CNNs in Paper I and the work of others, Paper II presents a method for automatic design of CNN architectures for use in myocontrol. Paper III introduces an ANN architecture with an appertaining training framework from which simultaneous and proportional control emerges. Paper Iv introduce a dataset of HD-sEMG signals for use with learning algorithms. Paper v applies a Recurrent Neural Network (RNN) model to decode finger forces from intramuscular EMG. Paper vI introduces a Transformer model for myoelectric interfacing that do not need additional training data to function with previously unseen users. Paper vII compares the performance of a Long Short-Term Memory (LSTM) network to that of classical pattern recognition algorithms. Lastly, paper vIII describes a framework for synthesizing EMG from multi-articulate gestures intended to reduce training burden

    Scalable and adaptive variational Bayes methods for Hawkes processes

    Full text link
    Hawkes processes are often applied to model dependence and interaction phenomena in multivariate event data sets, such as neuronal spike trains, social interactions, and financial transactions. In the nonparametric setting, learning the temporal dependence structure of Hawkes processes is generally a computationally expensive task, all the more with Bayesian estimation methods. In particular, for generalised nonlinear Hawkes processes, Monte-Carlo Markov Chain methods applied to compute the doubly intractable posterior distribution are not scalable to high-dimensional processes in practice. Recently, efficient algorithms targeting a mean-field variational approximation of the posterior distribution have been proposed. In this work, we first unify existing variational Bayes approaches under a general nonparametric inference framework, and analyse the asymptotic properties of these methods under easily verifiable conditions on the prior, the variational class, and the nonlinear model. Secondly, we propose a novel sparsity-inducing procedure, and derive an adaptive mean-field variational algorithm for the popular sigmoid Hawkes processes. Our algorithm is parallelisable and therefore computationally efficient in high-dimensional setting. Through an extensive set of numerical simulations, we also demonstrate that our procedure is able to adapt to the dimensionality of the parameter of the Hawkes process, and is partially robust to some type of model mis-specification

    Modeling and pricing cyber insurance: Idiosyncratic, systematic, and systemic risks

    Get PDF
    The paper provides a comprehensive overview of modeling and pricing cyber insurance and includes clear and easily understandable explanations of the underlying mathematical concepts. We distinguish three main types of cyber risks: idiosyncratic, systematic, and systemic cyber risks. While for idiosyncratic and systematic cyber risks, classical actuarial and financial mathematics appear to be well-suited, systemic cyber risks require more sophisticated approaches that capture both network and strategic interactions. In the context of pricing cyber insurance policies, issues of interdependence arise for both systematic and systemic cyber risks; classical actuarial valuation needs to be extended to include more complex methods, such as concepts of risk-neutral valuation and (set-valued) monetary risk measures

    Simulation methods for reliability-based design optimization and model updating of civil engineering structures and systems

    Get PDF
    This thesis presents a collection of original contributions pertaining to the subjects of reliability-based design optimization (RBDO) and model updating of civil engineering structures and systems. In this regard, probability theory concepts and tools are instrumental in the formulation of the herein reported developments. Firstly, two approaches are devised for the RBDO of structural dynamical systems under stochastic excitation. Namely, a stochastic search technique is proposed for constrained and unconstrained RBDO problems involving continuous, discrete and mixed discrete-continuous design spaces, whereas an efficient sensitivity assessment framework for linear stochastic structures is implemented to identify optimal designs and evaluate their sensitivities. Moreover, two classes of model updating problems are considered. In this context, the Bayesian interpretation of probability theory plays a key role in the proposed solution schemes. Specifically, contaminant source detection in water distribution networks is addressed by resorting to a sampling-based Bayesian model class selection framework. Furthermore, an effective strategy for Bayesian model updating with structural reliability methods is presented to treat identification problems involving structural dynamical systems, measured response data, and high-dimensional parameter spaces. The approaches proposed in this thesis integrate stochastic simulation techniques as an essential part of their formulation, which allows obtaining non-trivial information about the systems of interest as a byproduct of the solution processes. Overall, the findings presented in this thesis suggest that the reported methods can be potentially adopted as supportive tools for a number of practical decision-making processes in civil engineering.Diese Arbeit stellt eine Sammlung von Beiträgen vor, die sich mit der Reliability-based-Design-Optimization (RBDO) und dem Model updating von Strukturen und Systemen im Bauwesen befassen. In diesem Zusammenhang sind wahrscheinlichkeitstheoretische Konzepte für die Formulierung der hier vorgestellten Entwicklungen von entscheidender Bedeutung. Zunächst werden zwei Ansätze für eine RBDO von strukturdynamischen Systemen unter stochastischer Anregung entwickelt. Es wird eine stochastische Suchtechnik für beschränkte und unbeschränkte RBDO-Probleme vorgeschlagen. Diese beziehen kontinuierliche, diskrete und gemischt diskret-kontinuierliche Designräume ein. Gleichzeitig wird ein effizientes Framework zur Bewertung der Sensitivität lineare stochastische Strukturen implementiert, um optimale Designs zu identifizieren und ihre Sensitivitäten zu bewerten. Darüber hinaus werden zwei Klassen von Problem aus dem Model updating betrachtet. Der Fokus wird hierbei auf die Erkennung von Kontaminationsquellen in Wasserverteilungsnetzen mithilfe eines auf Stichproben basierenden Bayesian-Model-Class-selection-Framework gelegt. Ferner wird eine effektive Strategie zur Bearbeitung von Problemen des Bayesian-Model-updating, die strukturdynamischen Systeme, gemessene Systemantwortdaten und hochdimensionale Parameterräume umfassen, vorgestellt. Die beschriebenen Ansätze verwenden stochastische Simulationstechniken als wesentlicher Bestandteil ihrer Formulierung, wodurch nicht-triviale Informationen über betrachtete Systeme als Nebenprodukt der Lösungsprozesse gewonnen werden können. Insgesamt deuten die vorgestellten Ergebnisse dieser Arbeit darauf hin, dass die beschriebenen Methoden potenziell als unterstützende Elemente in praktischen Entscheidungsproblemen im Zusammenhang mit Strukturen und Systemen im Bauwesen eingesetzt werden können

    Statistical learning of random probability measures

    Get PDF
    The study of random probability measures is a lively research topic that has attracted interest from different fields in recent years. In this thesis, we consider random probability measures in the context of Bayesian nonparametrics, where the law of a random probability measure is used as prior distribution, and in the context of distributional data analysis, where the goal is to perform inference given avsample from the law of a random probability measure. The contributions contained in this thesis can be subdivided according to three different topics: (i) the use of almost surely discrete repulsive random measures (i.e., whose support points are well separated) for Bayesian model-based clustering, (ii) the proposal of new laws for collections of random probability measures for Bayesian density estimation of partially exchangeable data subdivided into different groups, and (iii) the study of principal component analysis and regression models for probability distributions seen as elements of the 2-Wasserstein space. Specifically, for point (i) above we propose an efficient Markov chain Monte Carlo algorithm for posterior inference, which sidesteps the need of split-merge reversible jump moves typically associated with poor performance, we propose a model for clustering high-dimensional data by introducing a novel class of anisotropic determinantal point processes, and study the distributional properties of the repulsive measures, shedding light on important theoretical results which enable more principled prior elicitation and more efficient posterior simulation algorithms. For point (ii) above, we consider several models suitable for clustering homogeneous populations, inducing spatial dependence across groups of data, extracting the characteristic traits common to all the data-groups, and propose a novel vector autoregressive model to study of growth curves of Singaporean kids. Finally, for point (iii), we propose a novel class of projected statistical methods for distributional data analysis for measures on the real line and on the unit-circle

    COMPUTATIONAL MODELING OF GENE REGULATION, GAMETE FORMATION, AND EMBRYO IMPLANTATION

    Get PDF
    DNA located in genes is transcribed into RNA which is translated into protein. The regulation of transcription and translation is carried out by several factors including a gene’s primary sequence, cis-regulatory elements (CREs) in non-coding DNA regions, epigenetic marks on the histones which compact DNA, and trans-binding factors (or proteins). The differential expression of a gene is crucial for establishing lineage-specific cell identity and phenotypic variability. Mutation or dysregulation may lead to natural variation within a population or aberrant gene expression and disease; trait-associated variation is known to be enriched in putative CREs, supporting their role in the origins of disease. Understanding the mechanisms by which CREs interact with one another and their cellular environment to regulate transcription may inform knowledge of biological pathways and provide a crucial foundation for developing new treatments. Further, because all DNA is passed to an offspring from their parents, it is important to understand not just the outcomes on expression due to coding and non-coding variation, but also how genetic material is passed to future generations. These dissertation chapters apply modeling approaches to large amounts of genetic and gene expression data in order to 1) better understand how the sequence and epigenetic makeup of CREs impact gene expression within hematopoiesis; 2) scan for selfish genetic elements which are preferentially passed to offspring within human sperm samples; and 3) predict implantation success for euploid embryos given gene expression profiles. Our models within Chapters 2-4 describe the impact of CREs within the blood cell lineage, connecting CREs to putative target genes, and establishing that the hematopoietic CREs were enriched for blood-trait associated genetic variation. Within Chapter 5, we find no compelling evidence of selfish genetic elements within a large sample of human sperm. Finally, within Chapter 6, we identify some genes which seem to impact the success of IVF embryo implantation by acting through regulation of translation

    Sparse Spectral Bayesian Permanental Process with Generalized Kernel

    Get PDF
    We introduce a novel scheme for Bayesian inference on permanental processes which models the Poisson intensity as the square of a Gaussian process. Combining generalized kernels and a Fourier features-based representation of the Gaussian process with a Laplace approximation to the posterior, we achieve a fast and efficient inference that does not require numerical integration over the input space, allows kernel design and scales linearly with the number of events. Our method builds and improves upon the state-of-the-art Laplace Bayesian point process benchmark of Walder and Bishop (2017), demonstrated on both synthetic, real-world temporal and large spatial data sets

    Generative Model based Training of Deep Neural Networks for Event Detection in Microscopy Data

    Get PDF
    Several imaging techniques employed in the life sciences heavily rely on machine learning methods to make sense of the data that they produce. These include calcium imaging and multi-electrode recordings of neural activity, single molecule localization microscopy, spatially-resolved transcriptomics and particle tracking, among others. All of them only produce indirect readouts of the spatiotemporal events they aim to record. The objective when analysing data from these methods is the identification of patterns that indicate the location of the sought-after events, e.g. spikes in neural recordings or fluorescent particles in microscopy data. Existing approaches for this task invert a forward model, i.e. a mathematical description of the process that generates the observed patterns for a given set of underlying events, using established methods like MCMC or variational inference. Perhaps surprisingly, for a long time deep learning saw little use in this domain, even though it became the dominant approach in the field of pattern recognition over the previous decade. The principal reason is that in the absence of labeled data needed for supervised optimization it remains unclear how neural networks can be trained to solve these tasks. To unlock the potential of deep learning, this thesis proposes different methods for training neural networks using forward models and without relying on labeled data. The thesis rests on two publications: In the first publication we introduce an algorithm for spike extraction from calcium imaging time traces. Building on the variational autoencoder framework, we simultaneously train a neural network that performs spike inference and optimize the parameters of the forward model. This approach combines several advantages that were previously incongruous: it is fast at test-time, can be applied to different non-linear forward models and produces samples from the posterior distribution over spike trains. The second publication deals with the localization of fluorescent particles in single molecule localization microscopy. We show that an accurate forward model can be used to generate simulations that act as a surrogate for labeled training data. Careful design of the output representation and loss function result in a method with outstanding precision across experimental designs and imaging conditions. Overall this thesis highlights how neural networks can be applied for precise, fast and flexible model inversion on this class of problems and how this opens up new avenues to achieve performance beyond what was previously possible
    • …
    corecore