Utilizing Graph Structure for Machine Learning

The information age has led to an explosion in the size and availability of data. This data often exhibits graph-structure that is either explicitly defined, as in the web of a social network, or is implicitly defined and can be determined by measuring similarity between objects. Utilizing this graph-structure allows for the design of machine learning algorithms that reflect not only the attributes of individual objects but their relationships to every other object in the domain as well. This thesis investigates three machine learning problems and proposes novel methods that leverage the graph-structure inherent in the tasks. Quantum walk neural networks are classical neural nets that use quantum random walks for classifying and regressing on graphs. Asymmetric directed node embeddings are another neural network architecture designed to embed the nodes of a directed graph into a vector space. Filtered manifold alignment is a novel two-step approach to domain adaptation

Learning EEG Biometrics for Person Identification and Authentication

EEG provides appealing biometrics by presenting some unique attributes, not possessed by common biometric modalities like fingerprints, retina and face scan, in terms of robustness against forgery, secrecy and privacy compliance, aliveness detection and potential of continuous authentication. Meanwhile, the use of EEG to provide cognitive indicators for human workload, fatigue and emotions has created an environment where EEG is well-integrated into systems, making it readily available for biometrics purposes. Yet, still, many challenges need to be properly addressed before any actual deployment of EEG-based biometric systems in real-life scenarios: 1) subjects' inconvenience during the signal acquisition process, 2) the relatively low recognition rates, and 3) the lack of robustness against diverse human states. To address the aforementioned issues, this thesis is devoted to learn biometric traits from EEG signals for stable person identification and authentication. State of the art studies of EEG biometrics are mainly divided into two categories, the event-related potential (ERP) category, which relies on a tight control of the cognitive states of the subjects, and the ongoing EEG category, which uses continuous EEG signals (mainly in resting state) naturally produced by the brain without any particular sensory stimulation. Studies in the ERP category focus more on the design of proper signal elicitation protocols or paradigms which usually require repetitive sensory stimulation. Ongoing EEG, on the contrary, is more flexible in terms of signal acquisition, but needs more advanced computational methods for feature extraction and classification. This study focuses on EEG biometrics using ongoing signals in diverse states. Such a flexible system could lead to an effective deployment in the real world. Specifically, this work focuses on ongoing EEG signals under diverse human states without strict task-specific controls in terms of brain response elicitation during signal acquisition. This is in contrast to previous studies that rely on specific sensory stimulation and synthetic cognitive tasks to tightly control the cognitive state of the subject being reflected in the resulting EEG activity, or to use resting state EEG signals. The relaxation of the reliance of the user's cognitive state makes the signal acquisition process streamlined, which in turn facilitates the actual deployment of the EEG biometrics system. Furthermore, not relying on sensory stimulation and cognitive tasks also allows for flexible and unobtrusive biometric systems that work in the background without interrupting the users, which is especially important in continuous scenarios. However, relaxing the system's reliance on the human state also means losing control of the EEG activity produced. As a result, EEG signals captured from the scalp may be contaminated by the active involvement of the tasks and cognitive states such as workload and emotion. Therefore, it becomes a challenge to learn identity-bearing information from the complicated signals to support high stability EEG biometrics. Possible solutions are proposed and investigated from two main perspectives, feature extraction and pattern classification. Specifically, graph features and learning models are proposed based on the brain connectivity, graph theory, and deep learning algorithms. A comprehensive investigation is conducted to assess the performance of proposed methods and existing methods in biometric identification and authentication, including in continuous scenarios. The methods and experiments are reported and detailed in the corresponding chapters, with the results obtained from data analysis

The development of bioinformatics workflows to explore single-cell multi-omics data from T and B lymphocytes

The adaptive immune response is responsible for recognising, containing and eliminating viral infection, and protecting from further reinfection. This antigen-specific response is driven by T and B cells, which recognise antigenic epitopes via highly specific heterodimeric surface receptors, termed T-cell receptors (TCRs) and B cell receptors (BCRs). The theoretical diversity of the receptor repertoire that can be generated via homologous recombination of V, D and J genes is large enough (>1015 unique sequences) that virtually any antigen can be recognised. However, only a subset of these are generated within the human body, and how they succeed in specifically recognising any pathogen(s) and distinguishing these from self-proteins remains largely unresolved. The recent advances in applying single-cell genomics technologies to simultaneously measure the clonality, surface phenotype and transcriptomic signature of pathogen- specific immune cells have significantly improved understanding of these questions. Single-cell multi-omics permits the accurate identification of clonally expanded populations, their differentiation trajectories, the level of immune receptor repertoire diversity involved in the response and the phenotypic and molecular heterogeneity. This thesis aims to develop a bioinformatic workflow utilising single-cell multi-omics data to explore, quantify and predict the clonal and transcriptomic signatures of the human T-cell response during and following viral infection. In the first aim, a web application, VDJView, was developed to facilitate the simultaneous analysis and visualisation of clonal, transcriptomic and clinical metadata of T and B cell multi-omics data. The application permits non-bioinformaticians to perform quality control and common analyses of single-cell genomics data integrated with other metadata, thus permitting the identification of biologically and clinically relevant parameters. The second aim pertains to analysing the functional, molecular and immune receptor profiles of CD8+ T cells in the acute phase of primary hepatitis C virus (HCV) infection. This analysis identified a novel population of progenitors of exhausted T cells, and lineage tracing revealed distinct trajectories with multiple fates and evolutionary plasticity. Furthermore, it was observed that high-magnitude IFN-γ CD8+ T-cell response is associated with the increased probability of viral escape and chronic infection. Finally, in the third aim, a novel analysis is presented based on the topological characteristics of a network generated on pathogen-specific, paired-chain, CD8+ TCRs. This analysis revealed how some cross-reactivity between TCRs can be explained via the sequence similarity between TCRs and that this property is not uniformly distributed across all pathogen-specific TCR repertoires. Strong correlations between the topological properties of the network and the biological properties of the TCR sequences were identified and highlighted. The suite of workflows and methods presented in this thesis are designed to be adaptable to various T and B cell multi-omic datasets. The associated analyses contribute to understanding the role of T and B cells in the adaptive immune response to viral-infection and cancer

LIPIcs, Volume 277, GIScience 2023, Complete Volume

LIPIcs, Volume 277, GIScience 2023, Complete Volum

12th International Conference on Geographic Information Science: GIScience 2023, September 12–15, 2023, Leeds, UK

No abstract available

Study on open science: The general state of the play in Open Science principles and practices at European life sciences institutes

Nowadays, open science is a hot topic on all levels and also is one of the priorities of the European Research Area. Components that are commonly associated with open science are open access, open data, open methodology, open source, open peer review, open science policies and citizen science. Open science may a great potential to connect and influence the practices of researchers, funding institutions and the public. In this paper, we evaluate the level of openness based on public surveys at four European life sciences institute

Using MapReduce Streaming for Distributed Life Simulation on the Cloud

Distributed software simulations are indispensable in the study of large-scale life models but often require the use of technically complex lower-level distributed computing frameworks, such as MPI. We propose to overcome the complexity challenge by applying the emerging MapReduce (MR) model to distributed life simulations and by running such simulations on the cloud. Technically, we design optimized MR streaming algorithms for discrete and continuous versions of Conway’s life according to a general MR streaming pattern. We chose life because it is simple enough as a testbed for MR’s applicability to a-life simulations and general enough to make our results applicable to various lattice-based a-life models. We implement and empirically evaluate our algorithms’ performance on Amazon’s Elastic MR cloud. Our experiments demonstrate that a single MR optimization technique called strip partitioning can reduce the execution time of continuous life simulations by 64%. To the best of our knowledge, we are the first to propose and evaluate MR streaming algorithms for lattice-based simulations. Our algorithms can serve as prototypes in the development of novel MR simulation algorithms for large-scale lattice-based a-life models.https://digitalcommons.chapman.edu/scs_books/1014/thumbnail.jp

Measuring knowledge sharing processes through social network analysis within construction organisations

The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes

The 45th Australasian Universities Building Education Association Conference: Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment, Conference Proceedings, 23 - 25 November 2022, Western Sydney University, Kingswood Campus, Sydney, Australia

This is the proceedings of the 45th Australasian Universities Building Education Association (AUBEA) conference which will be hosted by Western Sydney University in November 2022. The conference is organised by the School of Engineering, Design, and Built Environment in collaboration with the Centre for Smart Modern Construction, Western Sydney University. This year’s conference theme is “Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment”, and expects to publish over a hundred double-blind peer review papers under the proceedings