http://hdl.handle.net/1893/2279 Electronic Department of Computing Science and Mathematics book chapters and sections. 2026-10-03T19:11:04Z http://hdl.handle.net/1893/37757 Title: Graph Pseudometrics from a Topological Point of View Author(s): Garcia-Pulido, Ana Lucia; Hess, Kathryn; Tan, Jane; Turner, Katharine; Wang, Bei; Yerolemou, Naya Editor(s): Gasparovic, Ellen; Robins, Vanessa; Turner, Katharine Abstract: We explore pseudometrics for directed graphs in order to better understand their topological properties. The directed flag complex associated to a directed graph provides a useful bridge between network science and topology. Indeed, it has often been observed that phenomena exhibited by real-world networks reflect the topology of their flag complexes, as measured, for example, by Betti numbers or simplex counts. As it is often computationally expensive (or even unfeasible) to determine such topological features exactly, it would be extremely valuable to have pseudometrics on the set of directed graphs that can both detect the topological differences and be computed efficiently. To facilitate work in this direction, we introduce methods to measure how well a graph pseudometric captures the topology of a directed graph. We then use these methods to evaluate some well-established pseudometrics, using test data drawn from several families of random graphs. 2023-01-01T00:00:00Z http://hdl.handle.net/1893/37543 Title: LON/D - Sub-problem Landscape Analysis in Decomposition-Based Multi-objective Optimization Author(s): Liefooghe, Arnaud; Ochoa, Gabriela; Verel, Sébastien Abstract: We explore the underlying difficulties of sub-problems arising from decomposition in multi-objective optimization. Decomposition algorithms, such as MOEA/D, split the original multi-objective problem into a set of single-objective sub-problems using a scalarizing function. A weighting coefficient vector defines each sub-problem. We examine the relative difficulty of these sub-problems based on their weight vector and the chosen scalar function—either weighted sum or weighted Tchebycheff. Our approach involves creating a landscape for each sub-problem and analyzing its local optima network (LON). We contribute by jointly visualizing the LONs of sub-problems, defining LON features for decomposition, and examining their interaction with problem properties and their impact on algorithm performance. An extensive experimental analysis of bi-objective NK-landscapes reveals that landscape properties depend not only on the weight vector and scalar function but also on the objectives’ intrinsic difficulty and their degree of conflict. These factors directly affect the relative performance of MOEA/D for each sub-problem. Among the landscape features explored, the size of each sub-problem’s global optimum basin of attraction showed the strongest impact on the performance of decomposition-based multi-objective optimization. 2025-01-01T00:00:00Z http://hdl.handle.net/1893/36857 Title: Horizon Scanning For Aquatic Food Security Author(s): Norman, Rachel; Crumlish, Margaret Editor(s): Crumlish, Margaret; Norman, Rachel http://hdl.handle.net/1893/36200 Title: Characteristics and hospital activity of elderly patients receiving admission avoidance home visits: a population-level record linkage study Author(s): Cristina Martin, Maria; Bouamrane, Matt-Mouley; Woolman, Paul; Kavanagh, Kimberley; Young, David Editor(s): Ohno-Machado, Lucila; Seroussi, Brigitte Abstract: As pressures on healthcare systems increase, due to an ageing population, hospital admission avoidance interventions have been emphasised. These interventions can be difficult to objectively evaluate due to non-randomised roll-out, requiring observational methods with carefully selected control groups. This study aims to identify the defining characteristics of elderly patients receiving admission avoidance home visits. We conducted a record linkage study using routinely collected data to compare characteristics and outcomes of the general elderly population and a subset of high-risk patients. Intervention patients were found to have significantly different demographics and admission rates compared to the general population, having four times higher admission rates at baseline. However, they share similarities with high-risk patients, particularly in that after a period of increased admissions, both groups experienced a reduction in the following year. Identifying defining characteristics of the target intervention population can guide the careful selection of a control group for evaluation. 2019-01-01T00:00:00Z