13,354 research outputs found
A study of uncertainty quantification in overparametrized high-dimensional models
Uncertainty quantification is a central challenge in reliable and trustworthy
machine learning. Naive measures such as last-layer scores are well-known to
yield overconfident estimates in the context of overparametrized neural
networks. Several methods, ranging from temperature scaling to different
Bayesian treatments of neural networks, have been proposed to mitigate
overconfidence, most often supported by the numerical observation that they
yield better calibrated uncertainty measures. In this work, we provide a sharp
comparison between popular uncertainty measures for binary classification in a
mathematically tractable model for overparametrized neural networks: the random
features model. We discuss a trade-off between classification accuracy and
calibration, unveiling a double descent like behavior in the calibration curve
of optimally regularized estimators as a function of overparametrization. This
is in contrast with the empirical Bayes method, which we show to be well
calibrated in our setting despite the higher generalization error and
overparametrization
Augmented classification for electrical coil winding defects
A green revolution has accelerated over the recent decades with a look to replace existing transportation power solutions through the adoption of greener electrical alternatives. In parallel the digitisation of manufacturing has enabled progress in the tracking and traceability of processes and improvements in fault detection and classification. This paper explores electrical machine manufacture and the challenges faced in identifying failures modes during this life cycle through the demonstration of state-of-the-art machine vision methods for the classification of electrical coil winding defects. We demonstrate how recent generative adversarial networks can be used to augment training of these models to further improve their accuracy for this challenging task. Our approach utilises pre-processing and dimensionality reduction to boost performance of the model from a standard convolutional neural network (CNN) leading to a significant increase in accuracy
Learning disentangled speech representations
A variety of informational factors are contained within the speech signal and a single short recording of speech reveals much more than the spoken words. The best method to extract and represent informational factors from the speech signal ultimately depends on which informational factors are desired and how they will be used. In addition, sometimes methods will capture more than one informational factor at the same time such as speaker identity, spoken content, and speaker prosody.
The goal of this dissertation is to explore different ways to deconstruct the speech signal into abstract representations that can be learned and later reused in various speech technology tasks. This task of deconstructing, also known as disentanglement, is a form of distributed representation learning. As a general approach to disentanglement, there are some guiding principles that elaborate what a learned representation should contain as well as how it should function. In particular, learned representations should contain all of the requisite information in a more compact manner, be interpretable, remove nuisance factors of irrelevant information, be useful in downstream tasks, and independent of the task at hand. The learned representations should also be able to answer counter-factual questions.
In some cases, learned speech representations can be re-assembled in different ways according to the requirements of downstream applications. For example, in a voice conversion task, the speech content is retained while the speaker identity is changed. And in a content-privacy task, some targeted content may be concealed without affecting how surrounding words sound. While there is no single-best method to disentangle all types of factors, some end-to-end approaches demonstrate a promising degree of generalization to diverse speech tasks.
This thesis explores a variety of use-cases for disentangled representations including phone recognition, speaker diarization, linguistic code-switching, voice conversion, and content-based privacy masking. Speech representations can also be utilised for automatically assessing the quality and authenticity of speech, such as automatic MOS ratings or detecting deep fakes. The meaning of the term "disentanglement" is not well defined in previous work, and it has acquired several meanings depending on the domain (e.g. image vs. speech). Sometimes the term "disentanglement" is used interchangeably with the term "factorization". This thesis proposes that disentanglement of speech is distinct, and offers a viewpoint of disentanglement that can be considered both theoretically and practically
Annals [...].
Pedometrics: innovation in tropics; Legacy data: how turn it useful?; Advances in soil sensing; Pedometric guidelines to systematic soil surveys.Evento online. Coordenado por: Waldir de Carvalho Junior, Helena Saraiva Koenow Pinheiro, Ricardo SimĂŁo Diniz Dalmolin
Quantifying the Indirect Effect of Wolves on Aspen in Northern Yellowstone National Park: Evidence for a Trophic Cascade?
Yellowstone National Park is renowned for its incredible wildlife, and perhaps the most famous of these species is the gray wolf, which was reintroduced to the Park in the mid-1990s. After reintroduction, it was highly publicized by scientists, journalists, and environmentalists that the wolf both decreased elk density and changed elk behavior in a way that reduced elk effects on plants, a process known as a âtrophic cascade.â Aspen, which is eaten by elk in winter, is one species at the forefront of Yellowstone trophic cascade research because it has been in decline across the Park for over a century. However, due to the challenges of measuring trophic cascades, there is continued uncertainty regarding the effects of wolves on aspen in northern Yellowstone. Thus, the purpose of my dissertation was to provide a comprehensive test of a trophic cascade in this system. Specifically, I used 20 years of data on aspen, elk, and wolves in Yellowstone to: 1) clarify annual trends in browsing and height of young aspen (a proxy for regeneration) after wolf reintroduction, 2) assess the influence of wolves scaring elk on aspen (âtrait-mediated indirect effectsâ), and 3) evaluate the effect of wolves killing elk on aspen (âdensity-mediated indirect effectsâ).
My research suggests that wolves indirectly contributed to increased aspen over story recruitment following their reintroduction by helping to reduce the elk population size, but elk response to the risk of wolf predation did not reduce elk foraging in a way that measurably increased aspen recruitment. Additionally, hunter harvest of elk north of the park was twice as important as wolf predation in causing increased aspen recruitment. However, despite wolves and hunters limiting elk abundance, it is still uncommon for young aspen to grow past peak browsing height (120-cm), indicating that many stands remain vulnerable to elk herbivory nearly 30 years after wolf reintroduction. These results highlight that the strength and mechanism of predator effects on plant communities are context-specific. Thus, using predator reintroduction as a tool for ecosystem restoration without considering the many factors that shape trophic cascades may result in different management and conservation outcomes than intended
The Role of Transient Vibration of the Skull on Concussion
Concussion is a traumatic brain injury usually caused by a direct or indirect blow to the head that affects brain function. The maximum mechanical impedance of the brain tissue occurs at 450±50 Hz and may be affected by the skull resonant frequencies. After an impact to the head, vibration resonance of the skull damages the underlying cortex. The skull deforms and vibrates, like a bell for 3 to 5 milliseconds, bruising the cortex. Furthermore, the deceleration forces the frontal and temporal cortex against the skull, eliminating a layer of cerebrospinal fluid. When the skull vibrates, the force spreads directly to the cortex, with no layer of cerebrospinal fluid to reflect the wave or cushion its force. To date, there is few researches investigating the effect of transient vibration of the skull. Therefore, the overall goal of the proposed research is to gain better understanding of the role of transient vibration of the skull on concussion. This goal will be achieved by addressing three research objectives. First, a MRI skull and brain segmentation automatic technique is developed. Due to bonesâ weak magnetic resonance signal, MRI scans struggle with differentiating bone tissue from other structures. One of the most important components for a successful segmentation is high-quality ground truth labels. Therefore, we introduce a deep learning framework for skull segmentation purpose where the ground truth labels are created from CT imaging using the standard tessellation language (STL). Furthermore, the brain region will be important for a future work, thus, we explore a new initialization concept of the convolutional neural network (CNN) by orthogonal moments to improve brain segmentation in MRI. Second, the creation of a novel 2D and 3D Automatic Method to Align the Facial Skeleton is introduced. An important aspect for further impact analysis is the ability to precisely simulate the same point of impact on multiple bone models. To perform this task, the skull must be precisely aligned in all anatomical planes. Therefore, we introduce a 2D/3D technique to align the facial skeleton that was initially developed for automatically calculating the craniofacial symmetry midline. In the 2D version, the entire concept of using cephalometric landmarks and manual image grid alignment to construct the training dataset was introduced. Then, this concept was extended to a 3D version where coronal and transverse planes are aligned using CNN approach. As the alignment in the sagittal plane is still undefined, a new alignment based on these techniques will be created to align the sagittal plane using Frankfort plane as a framework. Finally, the resonant frequencies of multiple skulls are assessed to determine how the skull resonant frequency vibrations propagate into the brain tissue. After applying material properties and mesh to the skull, modal analysis is performed to assess the skull natural frequencies. Finally, theories will be raised regarding the relation between the skull geometry, such as shape and thickness, and vibration with brain tissue injury, which may result in concussive injury
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Mixture Models in Machine Learning
Modeling with mixtures is a powerful method in the statistical toolkit that can be used for representing the presence of sub-populations within an overall population. In many applications ranging from financial models to genetics, a mixture model is used to fit the data. The primary difficulty in learning mixture models is that the observed data set does not identify the sub-population to which an individual observation belongs. Despite being studied for more than a century, the theoretical guarantees of mixture models remain unknown for several important settings.
In this thesis, we look at three groups of problems. The first part is aimed at estimating the parameters of a mixture of simple distributions. We ask the following question: How many samples are necessary and sufficient to learn the latent parameters? We propose several approaches for this problem that include complex analytic tools to connect statistical distances between pairs of mixtures with the characteristic function. We show sufficient sample complexity guarantees for mixtures of popular distributions (including Gaussian, Poisson and Geometric). For many distributions, our results provide the first sample complexity guarantees for parameter estimation in the corresponding mixture. Using these techniques, we also provide improved lower bounds on the Total Variation distance between Gaussian mixtures with two components and demonstrate new results in some sequence reconstruction problems.
In the second part, we study Mixtures of Sparse Linear Regressions where the goal is to learn the best set of linear relationships between the scalar responses (i.e., labels) and the explanatory variables (i.e., features). We focus on a scenario where a learner is able to choose the features to get the labels. To tackle the high dimensionality of data, we further assume that the linear maps are also sparse , i.e., have only few prominent features among many. For this setting, we devise algorithms with sub-linear (as a function of the dimension) sample complexity guarantees that are also robust to noise.
In the final part, we study Mixtures of Sparse Linear Classifiers in the same setting as above. Given a set of features and the binary labels, the objective of this task is to find a set of hyperplanes in the space of features such that for any (feature, label) pair, there exists a hyperplane in the set that justifies the mapping. We devise efficient algorithms with sub-linear sample complexity guarantees for learning the unknown hyperplanes under similar sparsity assumptions as above. To that end, we propose several novel techniques that include tensor decomposition methods and combinatorial designs
Unraveling the effect of sex on human genetic architecture
Sex is arguably the most important differentiating characteristic in most mammalian
species, separating populations into different groups, with varying behaviors, morphologies,
and physiologies based on their complement of sex chromosomes, amongst other factors. In
humans, despite males and females sharing nearly identical genomes, there are differences
between the sexes in complex traits and in the risk of a wide array of diseases. Sex provides
the genome with a distinct hormonal milieu, differential gene expression, and environmental
pressures arising from gender societal roles. This thus poses the possibility of observing
gene by sex (GxS) interactions between the sexes that may contribute to some of the
phenotypic differences observed. In recent years, there has been growing evidence of GxS,
with common genetic variation presenting different effects on males and females. These
studies have however been limited in regards to the number of traits studied and/or
statistical power. Understanding sex differences in genetic architecture is of great
importance as this could lead to improved understanding of potential differences in
underlying biological pathways and disease etiology between the sexes and in turn help
inform personalised treatments and precision medicine.
In this thesis we provide insights into both the scope and mechanism of GxS across the
genome of circa 450,000 individuals of European ancestry and 530 complex traits in the UK
Biobank. We found small yet widespread differences in genetic architecture across traits
through the calculation of sex-specific heritability, genetic correlations, and sex-stratified
genome-wide association studies (GWAS). We further investigated whether sex-agnostic
(non-stratified) efforts could potentially be missing information of interest, including sex-specific trait-relevant loci and increased phenotype prediction accuracies. Finally, we
studied the potential functional role of sex differences in genetic architecture through sex
biased expression quantitative trait loci (eQTL) and gene-level analyses.
Overall, this study marks a broad examination of the genetics of sex differences. Our findings
parallel previous reports, suggesting the presence of sexual genetic heterogeneity across
complex traits of generally modest magnitude. Furthermore, our results suggest the need to
consider sex-stratified analyses in future studies in order to shed light into possible sex-specific molecular mechanisms
Structure and adsorption properties of gas-ionic liquid interfaces
Supported ionic liquids are a diverse class of materials that have been considered
as a promising approach to design new surface properties within solids for gas
adsorption and separation applications. In these materials, the surface morphology and
composition of a porous solid are modified by depositing ionic liquid. The resulting
materials exhibit a unique combination of structural and gas adsorption properties
arising from both components, the support, and the liquid. Naturally, theoretical and
experimental studies devoted to understanding the underlying principles of exhibited
interfacial properties have been an intense area of research. However, a complete
understanding of the interplay between interfacial gas-liquid and liquid-solid
interactions as well as molecular details of these processes remains elusive.
The proposed problem is challenging and in this thesis, it is approached from
two different perspectives applying computational and experimental techniques. In
particular, molecular dynamics simulations are used to model gas adsorption in films
of ionic liquids on a molecular level. A detailed description of the modeled systems is
possible if the interfacial and bulk properties of ionic liquid films are separated. In this
study, we use a unique method that recognizes the interfacial and bulk structures of
ionic liquids and distinguishes gas adsorption from gas solubility. By combining
classical nitrogen sorption experiments with a mean-field theory, we study how liquid-solid interactions influence the adsorption of ionic liquids on the surface of the porous
support.
The developed approach was applied to a range of ionic liquids that feature
different interaction behavior with gas and porous support. Using molecular
simulations with interfacial analysis, it was discovered that gas adsorption capacity
can be directly related to gas solubility data, allowing the development of a predictive
model for the gas adsorption performance of ionic liquid films. Furthermore, it was
found that this CO2 adsorption on the surface of ionic liquid films is determined by the
specific arrangement of cations and anions on the surface. A particularly important
result is that, for the first time, a quantitative relation between these structural and
adsorption properties of different ionic liquid films has been established. This link
between two types of properties determines design principles for supported ionic
liquids.
However, the proposed predictive model and design principles rely on the
assumption that the ionic liquid is uniformly distributed on the surface of the porous
support. To test how ionic liquids behave under confinement, nitrogen physisorption
experiments were conducted for microâ and mesopore analysis of supported ionic
liquid materials. In conjunction with mean-field density functional theory applied to
the lattice gas and pore models, we revealed different scenarios for the pore-filling
mechanism depending on the strength of the liquid-solid interactions.
In this thesis, a combination of computational and experimental studies provides
a framework for the characterization of complex interfacial gas-liquid and liquid-solid
processes. It is shown that interfacial analysis is a powerful tool for studying
molecular-level interactions between different phases. Finally, nitrogen sorption
experiments were effectively used to obtain information on the structure of supported
ionic liquids
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Brain signal recognition using deep learning
This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel UniversityBrain Computer Interface (BCI) has the potential to offer a new generation of applications independent of
muscular activity and controlled by the human brain. Brain imaging technologies are used to transfer the
cognitive tasks into control commands for a BCI system. The electroencephalography (EEG) technology
serves as the best available non-invasive solution for extracting signals from the brain. On the other hand,
speech is the primary means of communication, but for patients suffering from locked-in syndrome, there
is no easy way to communicate. Therefore, an ideal communication system for locked-in patients is a
thought-to-speech BCI system.
This research aims to investigate methods for the recognition of imagined speech from EEG signals
using deep learning techniques. In order to design an optimal imagined speech recognition BCI, variety
of issues have been solved. These include 1) proposing new feature extraction and classification
framework for recognition of imagined speech from EEG signals, 2) grammatical class recognition of
imagined words from EEG signals, 3) discriminating different cognitive tasks associated with speech in
the brain such as overt speech, covert speech, and visual imagery. In this work machine learning, deep
learning methods were used to analyze EEG signals.
For recognition of imagined speech from EEG signals, a new EEG database was collected while the
participants mentally spoke (imagined speech) the presented words. Along with imagined speech, EEG
data was recorded for visual imagery (imagining a scene or an image) and overt speech (verbal speech).
Spectro-temporal and spatio-temporal domain features were investigated for the classification of imagined
words from EEG signals. Further, a deep learning framework using the convolutional network
and attention mechanism was implemented for learning features in the spatial, temporal, and spectral
domains. The method achieved a recognition rate of 76.6% for three binary word pairs. These experiments
show that deep learning algorithms are ideal for imagined speech recognition from EEG signals
due to their ability to interpret features from non-linear and non-stationary signals. Grammatical classes
of imagined words from EEG signals were also recognized using a multi-channel convolution network
framework. This method was extended to a multi-level recognition system for multi-class classification
of imagined words which achieved an accuracy of 52.9% for 10 words, which is much better in
comparison to previous work.
In order to investigate the difference between imagined speech with verbal speech and visual imagery
from EEG signals, we used multivariate pattern analysis (MVPA). MVPA provided the time segments
when the neural oscillation for the different cognitive tasks was linearly separable. Further, frequencies
that result in most discrimination between the different cognitive tasks were also explored. A framework
was proposed to discriminate two cognitive tasks based on the spatio-temporal patterns in EEG signals.
The proposed method used the K-means clustering algorithm to find the best electrode combination and
convolutional-attention network for feature extraction and classification. The proposed method achieved
a high recognition rate of 82.9% and 77.7%.
The results in this research suggest that a communication based BCI system can be designed using
deep learning methods. Further, this work add knowledge to the existing work in the field of communication
based BCI system
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