Insight Centre for Data Analytics - Journal Articles

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A constraint-based parallel local search for the edge-disjoint rooted distance-constrained minimum spanning tree problem
(Springer Nature, 2017-06-16) Arbelaez, Alejandro; Mehta, Deepak; O’Sullivan, Barry; Quesada, Luis
Many network design problems arising in areas as diverse as VLSI circuit design, QoS routing, traffic engineering, and computational sustainability require clients to be connected to a facility under path-length constraints and budget limits. These problems can be seen as instances of the rooted distance-constrained minimum spanning-tree problem (RDCMST), which is NP-hard. An inherent feature of these networks is that they are vulnerable to a failure. Therefore, it is often important to ensure that all clients are connected to two or more facilities via edge-disjoint paths. We call this problem the edge-disjoint RDCMST (ERDCMST). Previous work on the RDCMST has focused on dedicated algorithms and therefore it is difficult to use these algorithms to tackle the ERDCMST. We present a constraint-based parallel local search algorithm for solving the ERDCMST. Traditional ways of extending a sequential algorithm to run in parallel perform either portfolio-based search in parallel or parallel neighbourhood search. Instead, we exploit the semantics of the constraints of the problem to perform multiple moves in parallel by ensuring that they are mutually independent. The ideas presented in this paper are general and can be adapted to other problems as well. The effectiveness of our approach is demonstrated by experimenting with a set of problem instances taken from real-world passive optical network deployments in Ireland, Italy, and the UK. Our results show that performing moves in parallel can significantly reduce the elapsed time and improve the quality of the solutions of our local search approach.
Solving complex optimisation problems by machine learning
(MDPI, 2024) Prestwich, Steven D.; Science Foundation Ireland; Horizon 2020
Most optimisation research focuses on relatively simple cases: one decision maker, one objective, and possibly a set of constraints. However, real-world optimisation problems often come with complications: they might be multi-objective, multi-agent, multi-stage or multi-level, and they might have uncertainty, partial knowledge or nonlinear objectives. Each has led to research areas with dedicated solution methods. However, when new hybrid problems are encountered, there is typically no solver available. We define a broad class of discrete optimisation problem called an influence program, and describe a lightweight algorithm based on multi-agent multi-objective reinforcement learning with sampling. We show that it can be used to solve problems from a wide range of literatures: constraint programming, Bayesian networks, stochastic programming, influence diagrams (standard, limited memory and multi-objective), and game theory (multi-level programming, Bayesian games and level-k reasoning). We expect it to be useful for the rapid prototyping of solution methods for new hybrid problems.
Radiomic study of antenatal prediction of severe placenta accreta spectrum from MRI
(Oxford University Press, 2024-08-17) Bartels, Helena C.; Wolsztynski, Eric; O'Doherty, Jim; Brophy, David P.; MacDermott, Roisin; Atallah, David; Saliba, Souha; El Kassis, Nadine; Moubarak, Malak; Young, Constance; Downey, Paul; Donnelly, Jennifer; Geoghegan, Tony; Brennan, Donal J.; Curran, Kathleen M.; National Maternity Hospital, Ireland; Science Foundation Ireland
Objectives: We previously demonstrated the potential of radiomics for the prediction of severe histological placenta accreta spectrum (PAS) subtypes using T2-weighted MRI. We aim to validate our model using an additional dataset. Secondly, we explore whether the performance is improved using a new approach to develop a new multivariate radiomics model. Methods: Multi-centre retrospective analysis was conducted between 2018 and 2023. Inclusion criteria: MRI performed for suspicion of PAS from ultrasound, clinical findings of PAS at laparotomy and/or histopathological confirmation. Radiomic features were extracted from T2-weighted MRI. The previous multivariate model was validated. Secondly, a 5-radiomic feature random forest classifier was selected from a randomized feature selection scheme to predict invasive placenta increta PAS cases. Prediction performance was assessed based on several metrics including area under the curve (AUC) of the receiver operating characteristic curve (ROC), sensitivity, and specificity. Results: We present 100 women [mean age 34.6 (±3.9) with PAS], 64 of whom had placenta increta. Firstly, we validated the previous multivariate model and found that a support vector machine classifier had a sensitivity of 0.620 (95% CI: 0.068; 1.0), specificity of 0.619 (95% CI: 0.059; 1.0), an AUC of 0.671 (95% CI: 0.440; 0.922), and accuracy of 0.602 (95% CI: 0.353; 0.817) for predicting placenta increta. From the new multivariate model, the best 5-feature subset was selected via the random subset feature selection scheme comprised of 4 radiomic features and 1 clinical variable (number of previous caesareans). This clinical-radiomic model achieved an AUC of 0.713 (95% CI: 0.551; 0.854), accuracy of 0.695 (95% CI 0.563; 0.793), sensitivity of 0.843 (95% CI 0.682; 0.990), and specificity of 0.447 (95% CI 0.167; 0.667). Conclusion: We validated our previous model and present a new multivariate radiomic model for the prediction of severe placenta increta from a well-defined, cohort of PAS cases. Advances in knowledge: Radiomic features demonstrate good predictive potential for identifying placenta increta. This suggests radiomics may be a useful adjunct to clinicians caring for women with this high-risk pregnancy condition.
The role of FPGAs in Modern Option Pricing techniques: A survey
(MDPI, 2024-08-12) O'Mahony, Aidan; Hanzon, Bernard; Popovici, Emanuel; Science Foundation Ireland; Intel Corporation; Dell Technologies
In financial computation, Field Programmable Gate Arrays (FPGAs) have emerged as a transformative technology, particularly in the domain of option pricing. This study presents the impact of Field Programmable Gate Arrays (FPGAs) on computational methods in finance, with an emphasis on option pricing. Our review examined 99 selected studies from an initial pool of 131, revealing how FPGAs substantially enhance both the speed and energy efficiency of various financial models, particularly Black–Scholes and Monte Carlo simulations. Notably, the performance gains—ranging from 270- to 5400-times faster than conventional CPU implementations—are highly dependent on the specific option pricing model employed. These findings illustrate FPGAs’ capability to efficiently process complex financial computations while consuming less energy. Despite these benefits, this paper highlights persistent challenges in FPGA design optimization and programming complexity. This study not only emphasises the potential of FPGAs to further innovate financial computing but also outlines the critical areas for future research to overcome existing barriers and fully leverage FPGA technology in future financial applications.
Road pavement health monitoring system using smartphone sensing with a two-stage machine learning model
(Elsevier, 2024-08-17) Zhao, Kai; Xu, Shuoshuo; Loney, James; Visentin, Andrea; Li, Zili; Science Foundation Ireland; European Regional Development Fund
Drive-by road pavement monitoring, using smartphone sensing, has faced longstanding challenges in adoption due to low accuracy and limited applicability. This stems from significant uncertainties during data collection in real-world scenarios, making it prohibitively difficult in applying conventional machine learning models to the detection of road pavement anomalies. This paper presents a two-stage machine learning approach that extracts potential anomalies from the dataset and classifies them into four typical road feature categories. Unlike time-series data analysis, this approach transforms time-series into geospatial series, allowing the analysis to be time-independent thereby capable of detecting road anomalies regardless of driving speeds. Additionally, a framework for a road pavement health monitoring system is proposed to collect data, integrate the machine learning engine, and visualise road anomalies. The developed system was tested on two shuttle buses with normal smartphones, which achieved 87% overall accuracy compared against manual inspection.

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