Miracle-dz/UniFAR-FTUs huggingface dataset:
Gender-specific symptomatology in depression: implications for assessment and treatment

University of Wollongong Copyright Warning You may print or download ONE copy of this document for the purpose of your own research or study. The University does not authorise you to copy, communicate or otherwise make available electronically to any other person any copyright material contained on this site. You are reminded of the following: This work is copyright. Apart from any use permitted under the Copyright Act 1968, no part of this work may be reproduced by any process, nor may any other exclusive right be exercised, without the permission of the author. Copyright owners are entitled to take legal action against persons who infringe their copyright. A reproduction of material that is protected by copyright may be a copyright infringement. A court may impose penalties and award damages in relation to offences and infringements relating to copyright material. Higher penalties may apply, and higher damages may be awarded, for offences and infringements involving the conversion of material into digital or electronic form. Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.
Combined interferometric and absorption-spectroscopic technique for determining molecular line strengths: Applications to CO2

A diode-laser-based technique has been developed for absolute line strength measurements with high precision and accuracy, combining a spectroscopic determination of the integrated absorbance with an optical measurement of the absorption path length by means of a Michelson interferometer. This method has been applied to several {sup 12}C{sup 16}O{sub 2} vibrorotational transitions belonging to the {nu}{sub 1}+2{nu}{sub 2}{sup 0}+{nu}{sub 3} combination band, in the spectral region around 2 {mu}m. The 1{sigma} overall accuracy in line strength determination ranges between 0.4% and 0.7%, while the experimental reproducibility is estimated to be around 0.1%. To our knowledge, these levels are the highest ever reached before. The pressure-broadening coefficients for the same transitions are also reported.
Corneal biomechanical properties and the development of myopia

This thesis is protected by copyright, with all rights reserved. By reading and using the thesis, the reader understands and agrees to the following terms: 1. The reader will abide by the rules and legal ordinances governing copyright regarding the use of the thesis. 2. The reader will use the thesis for the purpose of research or private study only and not for distribution or further reproduction or any other purpose. 3. The reader agrees to indemnify and hold the University harmless from and against any loss, damage, cost, liability or expenses arising from copyright infringement or unauthorized usage.
BioNet: Infusing Biomarker Prior into Global-to-Local Network for Choroid Segmentation in Optical Coherence Tomography Images

Choroid is the vascular layer of the eye, which is directly related to the incidence and severity of many ocular diseases. Optical Coherence Tomography (OCT) is capable of imaging both the cross-sectional view of retina and choroid, but the segmentation of the choroid region is challenging because of the fuzzy choroid-sclera interface (CSI). In this paper, we propose a biomarker infused global-to-local network (BioNet) for choroid segmentation, which segments the choroid with higher credibility and robustness. Firstly, our method trains a biomarker prediction network to learn the features of the biomarker. Then a global multi-layers segmentation module is applied to segment the OCT image into 12 layers. Finally, the global multi-layered result and the original OCT image are fed into a local choroid segmentation module to segment the choroid region with the biomarker infused as regularizer. We conducted comparison experiments with the state-of-the-art methods on a dataset (named AROD). The experimental results demonstrate the superiority of our method with $90.77\%$ Dice-index and 6.23 pixels Average-unsigned-surface-detection-error, etc.
Event Outcome Prediction using Sentiment Analysis and Crowd Wisdom in Microblog Feeds

Sentiment Analysis of microblog feeds has attracted considerable interest in recent times. Most of the current work focuses on tweet sentiment classification. But not much work has been done to explore how reliable the opinions of the mass (crowd wisdom) in social network microblogs such as twitter are in predicting outcomes of certain events such as election debates. In this work, we investigate whether crowd wisdom is useful in predicting such outcomes and whether their opinions are influenced by the experts in the field. We work in the domain of multi-label classification to perform sentiment classification of tweets and obtain the opinion of the crowd. This learnt sentiment is then used to predict outcomes of events such as: US Presidential Debate winners, Grammy Award winners, Super Bowl Winners. We find that in most of the cases, the wisdom of the crowd does indeed match with that of the experts, and in cases where they don't (particularly in the case of debates), we see that the crowd's opinion is actually influenced by that of the experts.
Line-based Camera Pose Estimation in Point Cloud of Structured Environments

Accurate registration of 2D imagery with point clouds is a key technology for image-LiDAR point cloud fusion, camera to laser scanner calibration and camera localization. Despite continuous improvements, automatic registration of 2D and 3D data without using additional textured information still faces great challenges. In this paper, we propose a new 2D-3D registration method to estimate 2D-3D line feature correspondences and the camera pose in untextured point clouds of structured environments. Specifically, we first use geometric constraints between vanishing points and 3D parallel lines to compute all feasible camera rotations. Then, we utilize a hypothesis testing strategy to estimate the 2D-3D line correspondences and the translation vector. By checking the consistency with computed correspondences, the best rotation matrix can be found. Finally, the camera pose is further refined using non-linear optimization with all the 2D-3D line correspondences. The experimental results demonstrate the effectiveness of the proposed method on the synthetic and real dataset (outdoors and indoors) with repeated structures and rapid depth changes.
Severity detection tool for patients with infectious disease

Hand foot and mouth disease (HFMD) and tetanus are serious infectious diseases in low- and middle-income countries. Tetanus, in particular, has a high mortality rate and its treatment is resource-demanding. Furthermore, HFMD often affects a large number of infants and young children. As a result, its treatment consumes enormous healthcare resources, especially when outbreaks occur. Autonomic nervous system dysfunction (ANSD) is the main cause of death for both HFMD and tetanus patients. However, early detection of ANSD is a difficult and challenging problem. The authors aim to provide a proof-of-principle to detect the ANSD level automatically by applying machine learning techniques to physiological patient data, such as electrocardiogram waveforms, which can be collected using low-cost wearable sensors. Efficient features are extracted that encode variations in the waveforms in the time and frequency domains. The proposed approach is validated on multiple datasets of HFMD and tetanus patients in Vietnam. Results show that encouraging performance is achieved. Moreover, the proposed features are simple, more generalisable and outperformed the standard heart rate variability analysis. The proposed approach would facilitate both the diagnosis and treatment of infectious diseases in low- and middle-income countries, and thereby improve patient care.
Sub-sampling and other considerations for efficient risk estimation in large portfolios

Computing risk measures of a financial portfolio comprising thousands of options is a challenging problem because (a) it involves a nested expectation requiring multiple evaluations of the loss of the financial portfolio for different risk scenarios and (b) evaluating the loss of the portfolio is expensive and the cost increases with its size. In this work, we look at applying Multilevel Monte Carlo (MLMC) with adaptive inner sampling to this problem and discuss several practical considerations. In particular, we discuss a sub-sampling strategy that results in a method whose computational complexity does not increase with the size of the portfolio. We also discuss several control variates that significantly improve the efficiency of MLMC in our setting.
Fenton-Wilkinson Order Statistics and German Tanks: A Case Study of an Orienteering Relay Race

Ordinal regression falls between discretevalued classification and continuous-valued regression. Ordinal target variables can be associated with ranked random variables. These random variables are known as order statistics and they are closely related to ordinal regression. However, the challenge of using order statistics for ordinal regression prediction is finding a suitable parent distribution. In this work, we provide a case study of a real-world orienteering relay race by viewing it as a random process. For this process, we show that accurate order statistical ordinal regression predictions of final team rankings, or places, can be obtained by assuming a lognormal distribution of individual leg times. Moreover, we apply Fenton-Wilkinson approximations to intermediate changeover times alongside an estimator for the total number of teams as in the notorious German tank problem. The purpose of this work is, in part, to spark interest in studying the applicability of order statistics in ordinal regression problems.
Entropy Regularization with Discounted Future State Distribution in Policy Gradient Methods

The policy gradient theorem is defined based on an objective with respect to the initial distribution over states. In the discounted case, this results in policies that are optimal for one distribution over initial states, but may not be uniformly optimal for others, no matter where the agent starts from. Furthermore, to obtain unbiased gradient estimates, the starting point of the policy gradient estimator requires sampling states from a normalized discounted weighting of states. However, the difficulty of estimating the normalized discounted weighting of states, or the stationary state distribution, is quite well-known. Additionally, the large sample complexity of policy gradient methods is often attributed to insufficient exploration, and to remedy this, it is often assumed that the restart distribution provides sufficient exploration in these algorithms. In this work, we propose exploration in policy gradient methods based on maximizing entropy of the discounted future state distribution. The key contribution of our work includes providing a practically feasible algorithm to estimate the normalized discounted weighting of states, i.e, the \textit{discounted future state distribution}. We propose that exploration can be achieved by entropy regularization with the discounted state distribution in policy gradients, where a metric for maximal coverage of the state space can be based on the entropy of the induced state distribution. The proposed approach can be considered as a three time-scale algorithm and under some mild technical conditions, we prove its convergence to a locally optimal policy. Experimentally, we demonstrate usefulness of regularization with the discounted future state distribution in terms of increased state space coverage and faster learning on a range of complex tasks.
Sensitivity to grayscale textures is adapted to natural scene statistics

The efficient coding hypothesis posits that sensory circuits and animal behavior should be adapted to the statistical structure of natural signals. Here, we show that the perception of visual textures is adapted to the spatial distribution of light signals at a striking level of detail. We first identify a parametrized 66-dimensional space of grayscale textures defined by local spatial correlations between discrete light intensities. We then devise a method of measuring the contribution of each of these textures to the spatial structure of scenes. Efficient coding predicts that the perceptual salience of a complex sensory signal should be related to its variability in natural settings. Based on an analysis of contrast-equalized natural scenes, this theory predicts that textures involving two-point correlations will be most salient, and it further predicts the relative salience among second-order correlations of different types. We test our predictions by asking observers to locate a briefly-flashed texture strip in a background of white noise. The behavior of individual subjects is highly consistent: correlations beyond the second order are hard to distinguish from random noise, and detection thresholds within this salient second-order subspace are quantitatively predicted across 26 two-dimensional texture planes. We additionally find non-trivial symmetries of natural images that leave psychophysical thresholds unchanged, further supporting our hypothesis. These findings significantly extend previous results obtained for binary images, and they provide deeper insights into the relationship between natural signal statistics and human perception.
A 3D wavelet-based incompressible Navier-Stokes solver for fully adaptive computations in time-varying geometries

We present a wavelet-based adaptive method for computing 3D flows in complex, time-dependent geometries, implemented on massively parallel computers. The incompressible fluid is modeled with an artificial compressibility approach in order to avoid the solution of elliptical problems. No-slip and in/outflow boundary conditions are imposed using volume penalization. The governing equations are discretized on a locally uniform Cartesian grid with centered finite differences, and integrated in time with a Runge--Kutta scheme, both of 4th order. The domain is partitioned into cubic blocks with equidistant grids and, for each block, biorthogonal interpolating wavelets are used as refinement indicators and prediction operators. Thresholding of wavelet coefficients allows to introduce dynamically evolving grids and the adaption strategy tracks the solution in both space and scale. Blocks are distributed among MPI processes and the global topology of the grid is encoded in a tree-like data structure. Analyzing the different physical and numerical parameters allows balancing their individual error contributions and thus ensures optimal convergence while minimizing computational effort. Different validation tests score accuracy and performance of our new open source code, \texttt{WABBIT} (Wavelet Adaptive Block-Based solver for Interactions with Turbulence), on massively parallel computers using fully adaptive grids. Flow simulations of flapping insects demonstrate its applicability to complex, bio-inspired problems.
Searching for water ice in the coma of interstellar object 2I/Borisov

Aims. Interstellar objects passing through our Solar System offer a rare opportunity to probe the physical and chemical processes involved in solid body and planet formation in extrasolar systems. The main objective of our study is to search for diagnostic absorption features of water ice in the near-infrared (NIR) spectrum of the second interstellar object 2I/2019 Q4 (Borisov) and compare its ice features to those of the Solar System icy objects. Methods. We observed 2I in the NIR on three separate occasions. The first observation was made on 2019 September 19 UT using the SpeX spectrograph at the 3m IRTF and again on September 24 UT with the GNIRS spectrograph at the 8m GEMINI telescope; the last observation was made on October 09 UT with IRTF. Results. The spectra obtained from all three nights appear featureless. No absorption features associated with water ice are detected. Spectral modeling suggests that water grains, if present, comprise no more than 10% of the coma cross section. The comet consistently exhibits a red D-type like spectrum with a spectral slope of about 6% per 1000 Å, which is similar to that of 1I/’Oumuamua and is comparable to Solar System comets.
On the Resolution of the Sensitivity Conjecture

The Sensitivity Conjecture is a long-standing problem in theoretical computer science that seeks to fit the sensitivity of a Boolean function into a unified framework formed by the other complexity measures of Boolean functions, such as block sensitivity and certificate complexity. After more than thirty years of attacks on this Conjecture, Hao Huang (2019) gave a very succinct proof of the Conjecture. In this survey, we explore the ideas that inspired the proof of this Conjecture by an exposition of four papers that had the most impact on the Conjecture. We also discuss progress on further research directions that the Conjecture leads us to.
Solving the Reaction-Diffusion equation based on analytical methods and deep learning algorithm; the Case study of sulfate attack to concrete

The reaction-diffusion equation is one of the cornerstones equations in applied science and engineering. In the present study, a deep neural network has been trained in order to predict the solution of the equation with different coefficients using the numerical solution of this equation and the utility of deep learning. Analytical solution of the Reaction-Diffusion equation also has been conducted by taking advantage of the Danckwerts method. The accuracy of deep learning results was compared with the analytical solutions. In order to decrease the learning time and to find out similar equations solutions, such as pure diffusion and pure reaction, dimensional analysis technique has been performed. It was demonstrated that deep learning can accurately estimate the Partial Differential Equations solutionin the case of the reaction-diffusion equation with a constant coefficient.
Cavity-Enhanced Photon Emission from a Single Germanium-Vacancy Center in a Diamond Membrane

The nitrogen-vacancy center in diamond has been explored extensively as a light-matter interface for quantum information applications, however it is limited by low coherent photon emission and spectral instability. Here, we present a promising interface based on an alternate defect with superior optical properties (the germanium-vacancy) coupled to a finesse $\approx11{,}000$ fiber cavity, resulting in a $31^{+11}_{-15}$-fold increase in the spectral density of emission. This work sets the stage for cryogenic experiments, where we predict a measurable increase in the spontaneous emission rate.
Beyond Node Embedding: A Direct Unsupervised Edge Representation Framework for Homogeneous Networks

Network representation learning has traditionally been used to find lower dimensional vector representations of the nodes in a network. However, there are very important edge driven mining tasks of interest to the classical network analysis community, which have mostly been unexplored in the network embedding space. For applications such as link prediction in homogeneous networks, vector representation (i.e., embedding) of an edge is derived heuristically just by using simple aggregations of the embeddings of the end vertices of the edge. Clearly, this method of deriving edge embedding is suboptimal and there is a need for a dedicated unsupervised approach for embedding edges by leveraging edge properties of the network. Towards this end, we propose a novel concept of converting a network to its weighted line graph which is ideally suited to find the embedding of edges of the original network. We further derive a novel algorithm to embed the line graph, by introducing the concept of collective homophily. To the best of our knowledge, this is the first direct unsupervised approach for edge embedding in homogeneous information networks, without relying on the node embeddings. We validate the edge embeddings on three downstream edge mining tasks. Our proposed optimization framework for edge embedding also generates a set of node embeddings, which are not just the aggregation of edges. Further experimental analysis shows the connection of our framework to the concept of node centrality.
Training Deep SLAM on Single Frames

Learning-based visual odometry and SLAM methods demonstrate a steady improvement over past years. However, collecting ground truth poses to train these methods is difficult and expensive. This could be resolved by training in an unsupervised mode, but there is still a large gap between performance of unsupervised and supervised methods. In this work, we focus on generating synthetic data for deep learning-based visual odometry and SLAM methods that take optical flow as an input. We produce training data in a form of optical flow that corresponds to arbitrary camera movement between a real frame and a virtual frame. For synthesizing data we use depth maps either produced by a depth sensor or estimated from stereo pair. We train visual odometry model on synthetic data and do not use ground truth poses hence this model can be considered unsupervised. Also it can be classified as monocular as we do not use depth maps on inference. We also propose a simple way to convert any visual odometry model into a SLAM method based on frame matching and graph optimization. We demonstrate that both the synthetically-trained visual odometry model and the proposed SLAM method build upon this model yields state-of-the-art results among unsupervised methods on KITTI dataset and shows promising results on a challenging EuRoC dataset.
Multiphase magnetism in Yb2Ti2O7

Significance Quantum materials have properties that defy conventional theories of solids. Explaining these unusual properties is a frontier in physics, which promises both technological applications and fundamentally new states of matter. Yb2Ti2O7 is a center of attention in this work. While it becomes ferromagnetic at very low temperature, its excitation spectrum resembles that of a quantum spin liquid. We show using neutron scattering on high-quality crystals that the unusual spectra may arise as a superposition of ferromagnetic and antiferromagnetic spin waves. This indicates that these disparate forms of magnetism actually coexist in Yb2Ti2O7, which is consistent with near-perfect degeneracy. The unconventional properties of Yb2Ti2O7 thus appear to arise from the atomic-scale interplay between two conventional types of order. We use neutron scattering to show that ferromagnetism and antiferromagnetism coexist in the low T state of the pyrochlore quantum magnet Yb2Ti2O7. While magnetic Bragg peaks evidence long-range static ferromagnetic order, inelastic scattering shows that short-range correlated antiferromagnetism is also present. Small-angle neutron scattering provides direct evidence for mesoscale magnetic structure that we associate with metastable antiferromagnetism. Classical Monte Carlo simulations based on exchange interactions inferred from ⟨111⟩-oriented high-field spin wave measurements confirm that antiferromagnetism is metastable within the otherwise ferromagnetic ground state. The apparent lack of coherent spin wave excitations and strong sensitivity to quenched disorder characterizing Yb2Ti2O7 is a consequence of this multiphase magnetism.