Modern Statistical/Machine Learning Techniques for Bio/Neuro-imaging Applications

Developments in modern bio-imaging techniques have allowed the routine collection of a vast amount of data from various techniques. The challenges lie in how to build accurate and efficient models to draw conclusions from the data and facilitate scientific discoveries. Fortunately, recent advances in statistics, machine learning, and deep learning provide valuable tools. This thesis describes some of our efforts to build scalable Bayesian models for four bio-imaging applications: (1) Stochastic Optical Reconstruction Microscopy (STORM) Imaging, (2) particle tracking, (3) voltage smoothing, (4) detect color-labeled neurons in c elegans and assign identity to the detections

Understanding and Measuring Privacy and Security Assertions of Mobile and VR Applications

The emergence of the COVID-19 pandemic has catalysed a profound transformation in the way mobile applications are utilised and engaged with by consumers. There has been a noticeable surge in people relying on applications for various purposes such as entertainment, remote work, and daily activities. These services collect large amounts of users’ personal information and use them in many areas, such as in medical and financial systems, but they also pose an unprecedented threat to users’ privacy and security. Many international jurisdictions have enacted privacy laws and regulations to restrict the behaviour of apps and define the obligations of app developers. Although various privacy assertions are required in app stores, such as the permission list and the privacy policies, it is usually difficult for regular users to understand the potential threats the app may pose, let alone identify undesired or malicious application behaviours. In this thesis, I have developed a comprehensive framework to assess the current privacy practices of mobile applications. The framework first establishes a knowledge base (including datasets) to model privacy and security assertions. It then builds a sound evaluation system to analyse the privacy practices of mobile applications. Large-scale privacy evaluations were conducted on different realworld datasets, including privacy policies, contact tracing apps, and children’s apps, with the aim of revealing the risks associated with mobile application privacy. Lastly, a novel approach to applying differential privacy on streamed spatial data in VR applications is proposed. This thesis provides a comprehensive guideline for the mobile software industry and legislators to build a stronger and safer privacy ecosystem.Thesis (Ph.D.) -- University of Adelaide, School of Computer and Mathematical Sciences, 202

Does GNN Pretraining Help Molecular Representation?

Extracting informative representations of molecules using Graph neural networks (GNNs) is crucial in AI-driven drug discovery. Recently, the graph research community has been trying to replicate the success of self-supervised pretraining in natural language processing, with several successes claimed. However, we find the benefit brought by self-supervised pretraining on small molecular data can be negligible in many cases. We conduct thorough ablation studies on the key components of GNN pretraining, including pretraining objectives, data splitting methods, input features, pretraining dataset scales, and GNN architectures, to see how they affect the accuracy of the downstream tasks. Our first important finding is, self-supervised graph pretraining do not always have statistically significant advantages over non-pretraining methods in many settings. Secondly, although noticeable improvement can be observed with additional supervised pretraining, the improvement may diminish with richer features or more balanced data splits. Thirdly, hyper-parameters could have larger impacts on accuracy of downstream tasks than the choice of pretraining tasks, especially when the scales of downstream tasks are small. Finally, we provide our conjectures where the complexity of some pretraining methods on small molecules might be insufficient, followed by empirical evidences on different pretraining datasets

SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL

One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramount importance, both in in-context learning and fine-tuning settings, depending on the amount of adaptation data used. In this paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is based on an execution-based self-consistency prompting approach designed for Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our best knowledge is the first to outperform previous state-of-the-art with fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying SQL-PaLM to real-world scenarios we further evaluate its robustness on other challenging variants of Spider and demonstrate the superior generalization capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate the impressive intelligent capabilities and various success enablers of LLM-based Text-to-SQL.Comment: 16 page

Higher visceral adiposity index is associated with increased likelihood of abdominal aortic calcification

Background: The negative effects of visceral adiposity accumulation on cardiovascular health have drawn much attention. However, the association between the Visceral Adiposity Index (VAI) and Abdominal Aortic Calcification (AAC) has never been reported before. The authors aimed to investigate the association between the VAI and AAC in US adults. Methods: Cross-sectional data were derived from the 2013 to 2014 National Health and Nutrition Examination Survey (NHANES) of participants with complete data of VAI and AAC scores. Weighted multivariable regression and logistic regression analysis were conducted to explore the independent relationship between VAI and AAC. Subgroup analysis and interaction tests were also performed. Results: A total of 2958 participants were enrolled and participants in the higher VAI tertile tended to have a higher mean AAC score and prevalence of severe AAC. In the fully adjusted model, a positive association between VAI and AAC score and severe AAC was observed (β = 0.04, 95% CI 0.01‒0.08; OR = 1.04, 95% CI 1.01‒1.07). Participants in the highest VAI tertile had a 0.41-unit higher AAC score (β = 0.41, 95% CI 0.08‒0.73) and a significantly 68% higher risk of severe AAC than those in the lowest VAI tertile (OR = 1.68, 95% CI 1.04‒2.71). Subgroup analysis and interaction tests indicated that there was no dependence for the association of VAI and AAC. Conclusion: Visceral adiposity accumulation evaluated by the VAI was associated with a higher AAC score and an increased likelihood of severe AAC

Universal Self-adaptive Prompting

A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through prompt-based and/or in-context learning. However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks when ground-truth labels are unavailable. In this study, we address this by presenting Universal Self-adaptive Prompting (USP), an automatic prompt design approach specifically tailored for zero-shot learning (while compatible with few-shot). Requiring only a small amount of unlabeled data & an inference-only LLM, USP is highly versatile: to achieve universal prompting, USP categorizes a possible NLP task into one of the three possible task types, and then uses a corresponding selector to select the most suitable queries & zero-shot model-generated responses as pseudo-demonstrations, thereby generalizing ICL to the zero-shot setup in a fully automated way. We evaluate zero-shot USP with two PaLM models, and demonstrate performances that are considerably stronger than standard zero-shot baselines and are comparable to or even superior than few-shot baselines across more than 20 natural language understanding (NLU) and natural language generation (NLG) tasks.Comment: 10 pages, 3 figures, 4 tables (19 pages, 5 figures and 9 tables including references and appendices