Barbican Centre

Nina Crummy and Richard Henry, Double-sided Antler and Bone Combs in Late Roman Britain. Stylistic Groups, Context and Status (Archaeopress Roman Archaeology 116). Oxford: Archaeopress. Pp. vi + 136, illus. isbn 9781803276441. £30.00. Available free as Open Access PDF eBook.

<p xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" class="first" dir="auto" id="d4045909e55">The UN describes current progress in closing the CO2 Emissions Reduction Gap as representing a “massive gap between rhetoric and reality”. To meet the goal of the Paris agreement “we would need global mobilization on a scale and pace never seen before. Many will say this is impossible.” I don't believe it's impossible but, to achieve the necessary turn-around, we have to unlock new and relatively unexplored digital opportunities for reaching net zero. What I'm proposing is a cybernetic approach to solving the problem. In 1959, the UK cyberneticist, Stafford Beer, prepared an analysis of comparative systems (i.e. what organizations do and how they do it) by grading them from simple to complex. He placed only three systems in his ‘exceedingly complex’ category: ‘the economy’, ‘the brain’ and ‘the Company’. Beer’s list was a starting point in defining each system's predictability and its susceptibility for control by tried and tested scientific methods. If he were to be alive today, I feel sure he would have included ‘Climate’ in his ‘exceedingly complex’ shortlist. This system, too, must be subjected to new theories of prediction together with proposed methods for testing the validity of these theories, in order to tackle the most formidable challenge of all time. I am advocating that research should be undertaken, as a matter of some urgency, into the idea that the digital attributes of the metaverse can help in lighting small fires of hope in an otherwise dire situation,

BackgroundThe onset and development of the COVID-19 pandemic have placed pressure on hospital resources and staff worldwide. The integration of more streamlined predictive modeling in prognosis and triage–related decision-making can partly ease this pressure.ObjectiveThe objective of this study is to assess the performance impact of dimensionality reduction on COVID-19 mortality prediction models, demonstrating the high impact of a limited number of features to limit the need for complex variable gathering before reaching meaningful risk labelling in clinical settings.MethodsStandard machine learning classifiers were employed to predict an outcome of either death or recovery using 25 patient-level variables, spanning symptoms, comorbidities, and demographic information, from a geographically diverse sample representing 17 countries. The effects of feature reduction on the data were tested by running classifiers on a high-quality data set of 212 patients with populated entries for all 25 available features. The full data set was compared to two reduced variations with 7 features and 1 feature, respectively, extracted using univariate mutual information and chi-square testing. Classifier performance on each data set was then assessed on the basis of accuracy, sensitivity, specificity, and received operating characteristic–derived area under the curve metrics to quantify benefit or loss from reduction.ResultsThe performance of the classifiers on the 212-patient sample resulted in strong mortality detection, with the highest performing model achieving specificity of 90.7% (95% CI 89.1%-92.3%) and sensitivity of 92.0% (95% CI 91.0%-92.9%). Dimensionality reduction provided strong benefits for performance. The baseline accuracy of a random forest classifier increased from 89.2% (95% CI 88.0%-90.4%) to 92.5% (95% CI 91.9%-93.0%) when training on 7 chi-square–extracted features and to 90.8% (95% CI 89.8%-91.7%) when training on 7 mutual information–extracted features. Reduction impact on a separate logistic classifier was mixed; however, when present, losses were marginal compared to the extent of feature reduction, altogether showing that reduction either improves performance or can reduce the variable-sourcing burden at hospital admission with little performance loss. Extreme feature reduction to a single most salient feature, often age, demonstrated large standalone explanatory power, with the best-performing model achieving an accuracy of 81.6% (95% CI 81.1%-82.1%); this demonstrates the relatively marginal improvement that additional variables bring to the tested models.ConclusionsPredictive statistical models have promising performance in early prediction of death among patients with COVID-19. Strong dimensionality reduction was shown to further improve baseline performance on selected classifiers and only marginally reduce it in others, highlighting the importance of feature reduction in future model construction and the feasibility of deprioritizing large, hard-to-source, and nonessential feature sets in real world settings.

"Dress and identity in Iron Age Britain: a study of glass beads and other objects of personal adornment." Archaeological Journal, 176(2), pp. 378–379

ABSTRACTWithin the ongoing debate on the roles and tensions between teaching and research in higher education, this paper explores the extent to which the pedagogical case study method can contribute to bridge the gap between teaching and research. Based on an initial survey on 1,057 university academics, the results reveal the existence of five major groups of academics with varying degrees of link between the two topics and demonstrate the variations in the contributions that pedagogical case studies contribute to classroom teaching and to scholarly research for each subgroup. The implications for higher education at both the institutional level and the personal academic level are then explored.

Makers (1912) 44 1.7 Yeats, The Wake House (c.1908) 45 1.8 Yeats, The Felons of our Land (1910) 46 1.9 Paul Henry, The Potato Diggers (1912) 48 1.10 Yeats, The Double Jockey Act (1916) 50 1.11Yeats, Two Travellers (1942) 53 1.