arXiv subjects

Samuel W. K. Wong

Publications and source records attributed to Samuel W. K. Wong.

32 records · Page 2Linked to original sources

arXiv2021

Conformational variability of loops in the SARS-CoV-2 spike protein

The SARS-CoV-2 spike (S) protein facilitates viral infection, and has been the focus of many structure determination efforts. Its flexible loop regions are known to be involved in protein binding and may adopt multiple conformations. This paper identifies the S protein loops and studies their conformational variability based on the available Protein Data Bank (PDB) structures. While most loops had essentially one stable conformation, 17 of 44 loop regions were observed to be structurally variable with multiple substantively distinct conformations based on a cluster analysis. Loop modeling methods were then applied to the S protein loop targets, and the prediction accuracies discussed in relation to the characteristics of the conformational clusters identified. Loops with multiple conformations were found to be challenging to model based on a single structural template.

q-bio.BM↗

arXiv2021

Inference of dynamic systems from noisy and sparse data via manifold-constrained Gaussian processes

Parameter estimation for nonlinear dynamic system models, represented by ordinary differential equations (ODEs), using noisy and sparse data is a vital task in many fields. We propose a fast and accurate method, MAGI (MAnifold-constrained Gaussian process Inference), for this task. MAGI uses a Gaussian process model over time-series data, explicitly conditioned on the manifold constraint that derivatives of the Gaussian process must satisfy the ODE system. By doing so, we completely bypass the need for numerical integration and achieve substantial savings in computational time. MAGI is also suitable for inference with unobserved system components, which often occur in real experiments. MAGI is distinct from existing approaches as we provide a principled statistical construction under a Bayesian framework, which incorporates the ODE system through the manifold constraint. We demonstrate the accuracy and speed of MAGI using realistic examples based on physical experiments.

stat.ME↗

arXiv2021

Statistical challenges in the analysis of sequence and structure data for the COVID-19 spike protein

As the major target of many vaccines and neutralizing antibodies against SARS-CoV-2, the spike (S) protein is observed to mutate over time. In this paper, we present statistical approaches to tackle some challenges associated with the analysis of S-protein data. We build a Bayesian hierarchical model to study the temporal and spatial evolution of S-protein sequences, after grouping the sequences into representative clusters. We then apply sampling methods to investigate possible changes to the S-protein's 3-D structure as a result of commonly observed mutations. While the increasing spread of D614G variants has been noted in other research, our results also show that the co-occurring mutations of D614G together with S477N or A222V may spread even more rapidly, as quantified by our model estimates.

stat.AP↗

arXiv2020

Ellipse Detection and Localization with Applications to Knots in Sawn Lumber Images

While general object detection has seen tremendous progress, localization of elliptical objects has received little attention in the literature. Our motivating application is the detection of knots in sawn timber images, which is an important problem since the number and types of knots are visual characteristics that adversely affect the quality of sawn timber. We demonstrate how models can be tailored to the elliptical shape and thereby improve on general purpose detectors; more generally, elliptical defects are common in industrial production, such as enclosed air bubbles when casting glass or plastic. In this paper, we adapt the Faster R-CNN with its Region Proposal Network (RPN) to model elliptical objects with a Gaussian function, and extend the existing Gaussian Proposal Network (GPN) architecture by adding the region-of-interest pooling and regression branches, as well as using the Wasserstein distance as the loss function to predict the precise locations of elliptical objects. Our proposed method has promising results on the lumber knot dataset: knots are detected with an average intersection over union of 73.05%, compared to 63.63% for general purpose detectors. Specific to the lumber application, we also propose an algorithm to correct any misalignment in the raw timber images during scanning, and contribute the first open-source lumber knot dataset by labeling the elliptical knots in the preprocessed images.

cs.CV↗

arXiv2020

Where Does Haydn End and Mozart Begin? Composer Classification of String Quartets

For centuries, the history and music of Joseph Franz Haydn and Wolfgang Amadeus Mozart have been compared by scholars. Recently, the growing field of music information retrieval (MIR) has offered quantitative analyses to complement traditional qualitative analyses of these composers. In this MIR study, we classify the composer of Haydn and Mozart string quartets based on the content of their scores. Our contribution is an interpretable statistical and machine learning approach that provides high classification accuracies and musical relevance. We develop novel global features that are automatically computed from symbolic data and informed by musicological Haydn-Mozart comparative studies, particularly relating to the sonata form. Several of these proposed features are found to be important for distinguishing between Haydn and Mozart string quartets. Our Bayesian logistic regression model attains leave-one-out classification accuracies over 84%, higher than prior works and providing interpretations that could aid in assessing musicological claims. Overall, our work can help expand the longstanding dialogue surrounding Haydn and Mozart and exemplify the benefit of interpretable machine learning in MIR, with potential applications to music generation and classification of other classical composers.

stat.AP↗

arXiv2020

Assessing the impacts of mutations to the structure of COVID-19 spike protein via sequential Monte Carlo

Proteins play a key role in facilitating the infectiousness of the 2019 novel coronavirus. A specific spike protein enables this virus to bind to human cells, and a thorough understanding of its 3-dimensional structure is therefore critical for developing effective therapeutic interventions. However, its structure may continue to evolve over time as a result of mutations. In this paper, we use a data science perspective to study the potential structural impacts due to ongoing mutations in its amino acid sequence. To do so, we identify a key segment of the protein and apply a sequential Monte Carlo sampling method to detect possible changes to the space of low-energy conformations for different amino acid sequences. Such computational approaches can further our understanding of this protein structure and complement laboratory efforts.

q-bio.BM↗

arXiv2020

On the circular correlation coefficients for bivariate von Mises distributions on a torus

This paper studies circular correlations for the bivariate von Mises sine and cosine distributions. These are two simple and appealing models for bivariate angular data with five parameters each that have interpretations comparable to those in the ordinary bivariate normal model. However, the variability and association of the angle pairs cannot be easily deduced from the model parameters unlike the bivariate normal. Thus to compute such summary measures, tools from circular statistics are needed. We derive analytic expressions and study the properties of the Jammalamadaka-Sarma and Fisher-Lee circular correlation coefficients for the von Mises sine and cosine models. Likelihood-based inference of these coefficients from sample data is then presented. The correlation coefficients are illustrated with numerical and visual examples, and the maximum likelihood estimators are assessed on simulated and real data, with comparisons to their non-parametric counterparts. Implementations of these computations for practical use are provided in our R package BAMBI.

math.ST↗

arXiv2020

Calibrating wood products for load duration and rate: A statistical look at three damage models

Lumber and wood-based products are versatile construction materials that are susceptible to weakening as a result of applied stresses. To assess the effects of load duration and rate, experiments have been carried out by applying preset load profiles to sample specimens. This paper studies these effects via a damage modeling approach, by considering three models in the literature: the Gerhards and Foschi accumulated damage models, and a degradation model based on the gamma process. We present a statistical framework for fitting these models to failure time data generated by a combination of ramp and constant load settings, and show how estimation uncertainty can be quantified. The models and methods are illustrated and compared via a novel analysis of a Hemlock lumber dataset. Practical usage of the fitted damage models is demonstrated with an application to long-term reliability prediction under stochastic future loadings.

stat.AP↗

arXiv2019

A Comparison Study on Nonlinear Dimension Reduction Methods with Kernel Variations: Visualization, Optimization and Classification

Because of high dimensionality, correlation among covariates, and noise contained in data, dimension reduction (DR) techniques are often employed to the application of machine learning algorithms. Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and their kernel variants (KPCA, KLDA) are among the most popular DR methods. Recently, Supervised Kernel Principal Component Analysis (SKPCA) has been shown as another successful alternative. In this paper, brief reviews of these popular techniques are presented first. We then conduct a comparative performance study based on three simulated datasets, after which the performance of the techniques are evaluated through application to a pattern recognition problem in face image analysis. The gender classification problem is considered on MORPH-II and FG-NET, two popular longitudinal face aging databases. Several feature extraction methods are used, including biologically-inspired features (BIF), local binary patterns (LBP), histogram of oriented gradients (HOG), and the Active Appearance Model (AAM). After applications of DR methods, a linear support vector machine (SVM) is deployed with gender classification accuracy rates exceeding 95% on MORPH-II, competitive with benchmark results. A parallel computational approach is also proposed, attaining faster processing speeds and similar recognition rates on MORPH-II. Our computational approach can be applied to practical gender classification systems and generalized to other face analysis tasks, such as race classification and age prediction.

stat.ML↗

arXiv2019

BAMBI: An R package for Fitting Bivariate Angular Mixture Models

Statistical analyses of directional or angular data have applications in a variety of fields, such as geology, meteorology and bioinformatics. There is substantial literature on descriptive and inferential techniques for univariate angular data, with the bivariate (or more generally, multivariate) cases receiving more attention in recent years. More specifically, the bivariate wrapped normal, von Mises sine and von Mises cosine distributions, and mixtures thereof, have been proposed for practical use. However, there is a lack of software implementing these distributions and the associated inferential techniques. In this article, we introduce BAMBI, an R package for analyzing bivariate (and univariate) angular data. We implement random data generation, density evaluation, and computation of theoretical summary measures (variances and correlation coefficients) for the three aforementioned bivariate angular distributions, as well as two univariate angular distributions: the univariate wrapped normal and the univariate von Mises distribution. The major contribution of BAMBI to statistical computing is in providing Bayesian methods for modeling angular data using finite mixtures of these distributions. We also provide functions for visual and numerical diagnostics and Bayesian inference for the fitted models. In this article, we first provide a brief review of the distributions and techniques used in BAMBI, then describe the capabilities of the package, and finally conclude with demonstrations of mixture model fitting using BAMBI on the two real datasets included in the package, one univariate and one bivariate.

stat.CO↗

arXiv2017

Sequential Decision Model for Inference and Prediction on Non-Uniform Hypergraphs with Application to Knot Matching from Computational Forestry

In this paper, we consider the knot matching problem arising in computational forestry. The knot matching problem is an important problem that needs to be solved to advance the state of the art in automatic strength prediction of lumber. We show that this problem can be formulated as a quadripartite matching problem and develop a sequential decision model that admits efficient parameter estimation along with a sequential Monte Carlo sampler on graph matching that can be utilized for rapid sampling of graph matching. We demonstrate the effectiveness of our methods on 30 manually annotated boards and present findings from various simulation studies to provide further evidence supporting the efficacy of our methods.

stat.AP↗

arXiv2017

The duration of load effect in lumber as stochastic degradation

This paper proposes a gamma process for modelling the damage that accumulates over time in the lumber used in structural engineering applications when stress is applied. The model separates the stochastic processes representing features internal to the piece of lumber on the one hand, from those representing external forces due to applied dead and live loads. The model applies those external forces through a time-varying population level function designed for time-varying loads. The application of this type of model, which is standard in reliability analysis, is novel in this context, which has been dominated by accumulated damage models (ADMs) over more than half a century. The proposed model is compared with one of the traditional ADMs. Our statistical results based on a Bayesian analysis of experimental data highlight the limitations of using accelerated testing data to assess long-term reliability, as seen in the wide posterior intervals. This suggests the need for more comprehensive testing in future applications, or to encode appropriate expert knowledge in the priors used for Bayesian analysis.

stat.AP↗

arXiv2017

Dimensional and statistical foundations for accumulated damage models

This paper develops a framework for creating damage accumulation models for engineered wood products by invoking the classical theory of non--dimensionalization. The result is a general class of such models. Both the US and Canadian damage accumulation models are revisited. It is shown how the former may be generalized within that framework while deficiencies are discovered in the latter and overcome. Use of modern Bayesian statistical methods for estimating the parameters in these models is proposed along with an illustrative application of these methods to a ramp load dataset.

stat.AP↗

arXiv2017

Bayesian analysis of accumulated damage models in lumber reliability

Wood products that are subjected to sustained stress over a period of long duration may weaken, and this effect must be considered in models for the long-term reliability of lumber. The damage accumulation approach has been widely used for this purpose to set engineering standards. In this article, we revisit an accumulated damage model and propose a Bayesian framework for analysis. For parameter estimation and uncertainty quantification, we adopt approximation Bayesian computation (ABC) techniques to handle the complexities of the model. We demonstrate the effectiveness of our approach using both simulated and real data, and apply our fitted model to analyze long-term lumber reliability under a stochastic live loading scenario.

stat.AP↗