Complex model calibration through emulation, a worked example for a stochastic epidemic model

Uncertainty quantification is a formal paradigm of statistical estimation that aims to account for all uncertainties inherent in the modelling process of real-world complex systems. The methods are directly applicable to stochastic models in epidemiology, however they have thus far not been widely used in this context. In this paper, we provide a tutorial on uncertainty quantification of stochastic epidemic models, aiming to facilitate the use of the uncertainty quantification paradigm for practitioners with other complex stochastic simulators of applied systems. We provide a formal workflow including the important decisions and considerations that need to be taken, and illustrate the methods over a simple stochastic epidemic model of UK SARS-CoV-2 transmission and patient outcome. We also present new approaches to visualisation of outputs from sensitivity analyses and uncertainty quantification more generally in high input and/or output dimensions

Inverse-power-law behavior of cellular motility reveals stromal–epithelial cell interactions in 3D co-culture by OCT fluctuation spectroscopy

The progression of breast cancer is known to be affected by stromal cells within the local microenvironment. Here we study the effect of stromal fibroblasts on the in-place motions (motility) of mammary epithelial cells within organoids in 3D co-culture, inferred from the speckle fluctuation spectrum using optical coherence tomography (OCT). In contrast to Brownian motion, mammary cell motions exhibit an inverse power-law fluctuation spectrum. We introduce two complementary metrics for quantifying fluctuation spectra: the power-law exponent and a novel definition of the motility amplitude, both of which are signal- and position-independent. We find that the power-law exponent and motility amplitude are positively (p<0.001) and negatively (p<0.01) correlated with the density of stromal cells in 3D co-culture, respectively. We also show how the hyperspectral data can be visualized using these metrics to observe heterogeneity within organoids. This constitutes a simple and powerful tool for detecting and imaging cellular functional changes with OCT

Abstract visualization of large-scale time-varying data

The explosion of large-scale time-varying datasets has created critical challenges for scientists to study and digest. One core problem for visualization is to develop effective approaches that can be used to study various data features and temporal relationships among large-scale time-varying datasets. In this dissertation, we first present two abstract visualization approaches to visualizing and analyzing time-varying datasets. The first approach visualizes time-varying datasets with succinct lines to represent temporal relationships of the datasets. A time line visualizes time steps as points and temporal sequence as a line. They are generated by sampling the distributions of virtual words across time to study temporal features. The key idea of time line is to encode various data properties with virtual words. We apply virtual words to characterize feature points and use their distribution statistics to measure temporal relationships. The second approach is ensemble visualization, which provides a highly abstract platform for visualizing an ensemble of datasets. Both approaches can be used for exploration, analysis, and demonstration purposes. The second component of this dissertation is an animated visualization approach to study dramatic temporal changes. Animation has been widely used to show trends, dynamic features and transitions in scientific simulations, while animated visualization is new. We present an automatic animation generation approach that simulates the composition and transition of storytelling techniques and synthesizes animations to describe various event features. We also extend the concept of animated visualization to non-traditional time-varying datasets--network protocols--for visualizing key information in abstract sequences. We have evaluated the effectiveness of our animated visualization with a formal user study and demonstrated the advantages of animated visualization for studying time-varying datasets

Jetting Through The Primordial Universe

Collisions of heavy ion nuclei at relativistic speeds (close to the speed of light) creates a high temperature and very dense form of matter, now known to consist of de-confined quarks and gluons, named the quark gluon plasma (QGP). In this thesis, Run1 experimental data from pp and heavy ion collisions at the CERN LHC is analyzed with the CMS detector. The pp jet cross section is compared with next to leading order theoretical calculations supplemented with non perturbative corrections for three different jet radii highlighting better comparisons for larger radii jets. Measurement of the jet yield followed by the nuclear modification factors in proton-lead at 5.02 TeV and lead-lead collisions at 2.76 TeV are presented. A new data driven technique is introduced to estimate and correct for the fake jet contribution in PbPb for low transverse momenta jets. The nuclear modification factors studied in this thesis show jet quenching to be attributed to final state effects, have a strong correlation to the event centrality, a weak inverse correlation to the jet transverse momenta and an apparent independence on the jet radii in the kinematic range studied. These measurements are compared with leading theoretical model calculations and other experimental results at the LHC leading to unanimous agreement on the qualitative nature of jet quenching. This thesis also features novel updates to the Monte Carlo heavy ion event generator JEWEL (Jet Evolution With Energy Loss) including the boson-jet production channels and also background subtraction techniques to reduce the effect of the thermal background. Keeping track of these jet-medium recoils in JEWEL due to the background subtraction techniques significantly improves its descriptions of several jet structure and sub-structure measurements at the LHC. [Shortened abstract]Comment: PhD Thesis: Defended on June 6th 2017 at Rutgers University, New Brunswick. Advised by Prof. Sevil Salur. Includes material from 1707.01539, 1609.05383, 1608.03099, 1601.02001 and 1510.0337