542 research outputs found
On the Utility of Representation Learning Algorithms for Myoelectric Interfacing
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
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
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
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
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
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
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
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
Recommended from our members
Statistical Methods for Structured Data: Analyses of Discrete Time Series and Networks
This dissertation addresses three problems of applied statistics involving discrete time series and network data. The three problems are (1) finding and analyzing community structure in directed networks, (2) capturing changes in dynamic count-valued time series of COVID-19 daily deaths, and (3) inferring the edges of an implicit network given noisy observations of a multivariate point process on its nodes. We use tools of spectral clustering, state-space models, Bayesian hierarchical modeling and variational inference to address these problems. Each chapter presents and discusses statistical methods for the given problem. We apply the methods to simulated and real data to both validate them and demonstrate their limitations.
In chapter 1 we consider a directed spectral method for community detection that utilizes a graph Laplacian defined for non-symmetric adjacency matrices. We give the theoretical motivation behind this directed graph Laplacian, and demonstrate its connection to an objective function that reflects a notion of how communities of nodes in directed networks should behave. Applying the method to directed networks, we compare the results to an approach using a symmetrized version of the adjacency matrices. A simulation study with a directed stochastic block model shows that directed spectral clustering can succeed where the symmetrized approach fails. And we find interesting and informative differences between the two approaches in the application to Congressional cosponsorship data.
n chapter 2 we propose a generalized non-linear state-space model for count-valued time series of COVID-19 fatalities. To capture the dynamic changes in daily COVID-19 death counts, we specify a latent state process that involves second order differencing and an AR(1)-ARCH(1) model. These modeling choices are motivated by the application and validated by model assessment. We consider and fit a progression of Bayesian hierarchical models under this general framework. Using COVID-19 daily death counts from New York City's five boroughs, we evaluate and compare the considered models through predictive model assessment. Our findings justify the elements included in the proposed model. The proposed model is further applied to time series of COVID-19 deaths from the four most populous counties in Texas. These model fits illuminate dynamics associated with multiple dynamic phases and show the applicability of the framework to localities beyond New York City.
In Chapter 3 we consider the task of inferring the connections between noisy observations of events. In our model-based approach, we consider a generative process incorporating latent dynamics that are directed by past events and the unobserved network structure. This process is based on a leaky integrate-and-fire (LIF) model from neuroscience for aggregating input and triggering events (spikes) in neural populations. Given observation data we estimate the model parameters with a novel variational Bayesian approach, specifying a highly structured and parsimonious approximation for the conditional posterior distribution of the process's latent dynamics. This approach allows for fully interpretable inference of both the model parameters of interest and the variational parameters. Moreover, it is computationally efficient in scenarios when the observed event times are not too sparse.
We apply our methods in a simulation study and to recorded neural activity in the dorsomedial frontal cortex (DMFC) of a rhesus macaque. We assess our results based on ground truth, model diagnostics, and spike prediction for held-out nodes
- …