Publications from our Researchers
Several of our current PhD candidates and fellow researchers at the Data Science Institute have published, or in the proccess of publishing, papers to present their research.
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Book chapterArcucci R, Moutiq L, Guo Y-K, 2020,
Neural Assimilation
, Editors: Krzhizhanovskaya, Zavodszky, Lees, Dongarra, Sloot, Brissos, Teixeira, Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 155-168, ISBN: 978-3-030-50432-8- Author Web Link
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- Citations: 2
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Conference paperLu P, Qiu H, Qin C, et al., 2020,
Going Deeper into Cardiac Motion Analysis to Model Fine Spatio-Temporal Features
, 24th Conference on Medical Image Understanding and Analysis (MIUA), Publisher: SPRINGER INTERNATIONAL PUBLISHING AG, Pages: 294-306, ISSN: 1865-0929- Author Web Link
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- Citations: 5
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Conference paperArcucci R, Casas CQ, Xiao D, et al., 2020,
A Domain Decomposition Reduced Order Model with Data Assimilation (DD-RODA)
, Conference on Parallel Computing - Technology Trends (ParCo), Publisher: IOS PRESS, Pages: 189-198, ISSN: 0927-5452 -
Conference paperTruong N, Sun K, Guo Y, 2019,
Blockchain-based personal data management: from fiction to solution
, The 18th IEEE International Symposium on Network Computing and Applications (NCA 2019), Publisher: IEEE, Pages: 1-8The emerging blockchain technology has enabledvarious decentralised applications in a trustless environmentwithout relying on a trusted intermediary. It is expected as apromising solution to tackle sophisticated challenges on personaldata management, thanks to its advanced features such as im-mutability, decentralisation and transparency. Although certainapproaches have been proposed to address technical difficultiesin personal data management; most of them only provided pre-liminary methodological exploration. Alarmingly, when utilisingBlockchain for developing a personal data management system,fictions have occurred in existing approaches and been promul-gated in the literature. Such fictions are theoretically doable;however, by thoroughly breaking down consensus protocols andtransaction validation processes, we clarify that such existingapproaches are either impractical or highly inefficient due tothe natural limitations of the blockchain and Smart Contractstechnologies. This encourages us to propose a feasible solution inwhich such fictions are reduced by designing a novel systemarchitecture with a blockchain-based “proof of permission”protocol. We demonstrate the feasibility and efficiency of theproposed models by implementing a clinical data sharing servicebuilt on top of a public blockchain platform. We believe thatour research resolves existing ambiguity and take a step furtheron providing a practically feasible solution for decentralisedpersonal data management.
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Conference paperKarri SSK, Bach F, Pock T, 2019,
Fast decomposable submodular function minimization using constrained total variation
, Neural Information Processing Systems, 2019, Publisher: Neural Information Processing Systems Foundation, Inc.We consider the problem of minimizing the sum of submodular set functions as-suming minimization oracles of each summand function. Most existing approachesreformulate the problem as the convex minimization of the sum of the correspond-ing Lovász extensions and the squared Euclidean norm, leading to algorithmsrequiring total variation oracles of the summand functions; without further assump-tions, these more complex oracles require many calls to the simpler minimizationoracles often available in practice. In this paper, we consider a modified convexproblem requiring a constrained version of the total variation oracles that can besolved with significantly fewer calls to the simple minimization oracles. We supportour claims by showing results on graph cuts for 2D and 3D graphs.
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Journal articleSuzuki H, Venkataraman AV, Bai W, et al., 2019,
Associations of regional brain structural differences with aging, modifiable risk factors for dementia, and cognitive performance
, JAMA Network Open, Vol: 2, Pages: 1-19, ISSN: 2574-3805Importance Identifying brain regions associated with risk factors for dementia could guide mechanistic understanding of risk factors associated with Alzheimer disease (AD).Objectives To characterize volume changes in brain regions associated with aging and modifiable risk factors for dementia (MRFD) and to test whether volume differences in these regions are associated with cognitive performance.Design, Setting, and Participants This cross-sectional study used data from UK Biobank participants who underwent T1-weighted structural brain imaging from August 5, 2014, to October 14, 2016. A voxelwise linear model was applied to test for regional gray matter volume differences associated with aging and MRFD (ie, hypertension, diabetes, obesity, and frequent alcohol use). The potential clinical relevance of these associations was explored by comparing their neuroanatomical distributions with the regional brain atrophy found with AD. Mediation models for risk factors, brain volume differences, and cognitive measures were tested. The primary hypothesis was that common, overlapping regions would be found. Primary analysis was conducted on April 1, 2018.Main Outcomes and Measures Gray matter regions that showed relative atrophy associated with AD, aging, and greater numbers of MRFD.Results Among 8312 participants (mean [SD] age, 62.4 [7.4] years; 3959 [47.1%] men), aging and 4 major MRFD (ie, hypertension, diabetes, obesity, and frequent alcohol use) had independent negative associations with specific gray matter volumes. These regions overlapped neuroanatomically with those showing lower volumes in participants with AD, including the posterior cingulate cortex, the thalamus, the hippocampus, and the orbitofrontal cortex. Associations between these MRFD and spatial memory were mediated by differences in posterior cingulate cortex volume (β = 0.0014; SE = 0.0006; P = .02).Conclusions and Relevance This cross-sectional study
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Conference paperHalliday BP, Balaban G, Costa CM, et al., 2019,
Improving Arrhythmic Risk Stratification in Non-Ischemic Dilated Cardiomyopathy Through the Evaluation of Novel Scar Characteristics Using CMR
, Scientific Sessions of the American-Heart-Association, Publisher: LIPPINCOTT WILLIAMS & WILKINS, ISSN: 0009-7322 -
Conference paperLim EM, Molina Solana M, Pain C, et al., 2019,
Hybrid data assimilation: An ensemble-variational approach
, Pages: 633-640Data Assimilation (DA) is a technique used to quantify and manage uncertainty in numerical models by incorporating observations into the model. Variational Data Assimilation (VarDA) accomplishes this by minimising a cost function which weighs the errors in both the numerical results and the observations. However, large-scale domains pose issues with the optimisation and execution of the DA model. In this paper, ensemble methods are explored as a means of sampling the background error at a reduced rank to condition the problem. The impact of ensemble size on the error is evaluated and benchmarked against other preconditioning methods explored in previous work such as using truncated singular value decomposition (TSVD). Localisation is also investigated as a form of reducing the long-range spurious errors in the background error covariance matrix. Both the mean squared error (MSE) and execution time are used as measure of performance. Experimental results for a 3D case for pollutant dispersion within an urban environment are presented with promise for future work using dynamic ensembles and 4D state vectors.
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Conference paperNadler P, Arcucci R, Guo YK, 2019,
Data assimilation for parameter estimation in economic modelling
, Pages: 649-656We propose a data assimilation approach for latent parameter estimation in economic models. We describe a dynamic model of an economic system with latent state variables describing the relationship of economic entities over time as well as a stochastic volatility component. We show and discuss the model's relationship with data assimilation and how it is derived. We apply it to conduct a multivariate analysis of the cryptocurrency ecosystem. Combining these approaches opens a new dimension of analysis to economic modelling. Economics, Multivariate Analysis, Dynamical System, Bitcoin, Data Assimilation.
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Journal articleAristodemou E, Arcucci R, Mottet L, et al., 2019,
Enhancing CFD-LES air pollution prediction accuracy using data assimilation
, Building and Environment, Vol: 165, ISSN: 0007-3628It is recognised worldwide that air pollution is the cause of premature deaths daily, thus necessitating the development of more reliable and accurate numerical tools. The present study implements a three dimensional Variational (3DVar) data assimilation (DA) approach to reduce the discrepancy between predicted pollution concentrations based on Computational Fluid Dynamics (CFD) with the ones measured in a wind tunnel experiment. The methodology is implemented on a wind tunnel test case which represents a localised neighbourhood environment. The improved accuracy of the CFD simulation using DA is discussed in terms of absolute error, mean squared error and scatter plots for the pollution concentration. It is shown that the difference between CFD results and wind tunnel data, computed by the mean squared error, can be reduced by up to three order of magnitudes when using DA. This reduction in error is preserved in the CFD results and its benefit can be seen through several time steps after re-running the CFD simulation. Subsequently an optimal sensors positioning is proposed. There is a trade-off between the accuracy and the number of sensors. It was found that the accuracy was improved when placing/considering the sensors which were near the pollution source or in regions where pollution concentrations were high. This demonstrated that only 14% of the wind tunnel data was needed, reducing the mean squared error by one order of magnitude.
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Journal articleRajpal H, Rosas De Andraca FE, Jensen HJ, 2019,
Tangled worldview model of opinion dynamics
, Frontiers in Physics, Vol: 7, ISSN: 2296-424XWe study the joint evolution of worldviews by proposing a model of opinion dynamics, which is inspired in notions fromevolutionary ecology. Agents update their opinion on a specific issue based on their propensity to change – asserted by thesocial neighbours – weighted by their mutual similarity on other issues. Agents are, therefore, more influenced by neighbourswith similar worldviews (set of opinions on various issues), resulting in a complex co-evolution of each opinion. Simulationsshow that the worldview evolution exhibits events of intermittent polarization when the social network is scale-free. This, in turn,triggers extreme crashes and surges in the popularity of various opinions. Using the proposed model, we highlight the role ofnetwork structure, bounded rationality of agents, and the role of key influential agents in causing polarization and intermittentreformation of worldviews on scale-free networks.
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Journal articleCofré R, Herzog R, Corcoran D, et al., 2019,
A comparison of the maximum entropy principle across biological spatial scales
, Entropy: international and interdisciplinary journal of entropy and information studies, Vol: 21, Pages: 1-20, ISSN: 1099-4300Despite their differences, biological systems at different spatial scales tend to exhibit common organizational patterns. Unfortunately, these commonalities are often hard to grasp due to the highly specialized nature of modern science and the parcelled terminology employed by various scientific sub-disciplines. To explore these common organizational features, this paper provides a comparative study of diverse applications of the maximum entropy principle, which has found many uses at different biological spatial scales ranging from amino acids up to societies. By presenting these studies under a common approach and language, this paper aims to establish a unified view over these seemingly highly heterogeneous scenarios.
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Journal articleBhuva AN, Bai W, Lau C, et al., 2019,
A multicenter, scan-rescan, human and machine learning CMR study to test generalizability and precision in imaging biomarker analysis
, Circulation: Cardiovascular Imaging, Vol: 12, Pages: 1-11, ISSN: 1941-9651Background:Automated analysis of cardiac structure and function using machine learning (ML) has great potential, but is currently hindered by poor generalizability. Comparison is traditionally against clinicians as a reference, ignoring inherent human inter- and intraobserver error, and ensuring that ML cannot demonstrate superiority. Measuring precision (scan:rescan reproducibility) addresses this. We compared precision of ML and humans using a multicenter, multi-disease, scan:rescan cardiovascular magnetic resonance data set.Methods:One hundred ten patients (5 disease categories, 5 institutions, 2 scanner manufacturers, and 2 field strengths) underwent scan:rescan cardiovascular magnetic resonance (96% within one week). After identification of the most precise human technique, left ventricular chamber volumes, mass, and ejection fraction were measured by an expert, a trained junior clinician, and a fully automated convolutional neural network trained on 599 independent multicenter disease cases. Scan:rescan coefficient of variation and 1000 bootstrapped 95% CIs were calculated and compared using mixed linear effects models.Results:Clinicians can be confident in detecting a 9% change in left ventricular ejection fraction, with greater than half of coefficient of variation attributable to intraobserver variation. Expert, trained junior, and automated scan:rescan precision were similar (for left ventricular ejection fraction, coefficient of variation 6.1 [5.2%–7.1%], P=0.2581; 8.3 [5.6%–10.3%], P=0.3653; 8.8 [6.1%–11.1%], P=0.8620). Automated analysis was 186× faster than humans (0.07 versus 13 minutes).Conclusions:Automated ML analysis is faster with similar precision to the most precise human techniques, even when challenged with real-world scan:rescan data. Assessment of multicenter, multi-vendor, multi-field strength scan:rescan data (available at www.thevolumesresource.com) permits a generalizable assessment of ML precision and may facili
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Conference paperChen C, Biffi C, Tarroni G, et al., 2019,
Learning shape priors for robust cardiac MR segmentation from multi-view images
, International Conference on Medical Image Computing and Computer-Assisted Intervention, Publisher: Springer International Publishing, Pages: 523-531, ISSN: 0302-9743Cardiac MR image segmentation is essential for the morphological and functional analysis of the heart. Inspired by how experienced clinicians assess the cardiac morphology and function across multiple standard views (i.e. long- and short-axis views), we propose a novel approach which learns anatomical shape priors across different 2D standard views and leverages these priors to segment the left ventricular (LV) myocardium from short-axis MR image stacks. The proposed segmentation method has the advantage of being a 2D network but at the same time incorporates spatial context from multiple, complementary views that span a 3D space. Our method achieves accurate and robust segmentation of the myocardium across different short-axis slices (from apex to base), outperforming baseline models (e.g. 2D U-Net, 3D U-Net) while achieving higher data efficiency. Compared to the 2D U-Net, the proposed method reduces the mean Hausdorff distance (mm) from 3.24 to 2.49 on the apical slices, from 2.34 to 2.09 on the middle slices and from 3.62 to 2.76 on the basal slices on the test set, when only 10% of the training data was used.
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Journal articleBalaban G, Halliday BP, Bai W, et al., 2019,
Scar shape analysis and simulated electrical instabilities in a non-ischemic dilated cardiomyopathy patient cohort.
, PLoS Computational Biology, Vol: 15, Pages: 1-18, ISSN: 1553-734XThis paper presents a morphological analysis of fibrotic scarring in non-ischemic dilated cardiomyopathy, and its relationship to electrical instabilities which underlie reentrant arrhythmias. Two dimensional electrophysiological simulation models were constructed from a set of 699 late gadolinium enhanced cardiac magnetic resonance images originating from 157 patients. Areas of late gadolinium enhancement (LGE) in each image were assigned one of 10 possible microstructures, which modelled the details of fibrotic scarring an order of magnitude below the MRI scan resolution. A simulated programmed electrical stimulation protocol tested each model for the possibility of generating either a transmural block or a transmural reentry. The outcomes of the simulations were compared against morphological LGE features extracted from the images. Models which blocked or reentered, grouped by microstructure, were significantly different from one another in myocardial-LGE interface length, number of components and entropy, but not in relative area and transmurality. With an unknown microstructure, transmurality alone was the best predictor of block, whereas a combination of interface length, transmurality and number of components was the best predictor of reentry in linear discriminant analysis.
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