Conforming and nonconforming virtual element methods for elliptic problems

We present in a unified framework new conforming and nonconforming Virtual Element Methods (VEM) for general second order elliptic problems in two and three dimensions. The differential operator is split into its symmetric and non-symmetric parts and conditions for stability and accuracy on their discrete counterparts are established. These conditions are shown to lead to optimal $H^1$- and $L^2$-error estimates, confirmed by numerical experiments on a set of polygonal meshes. The accuracy of the numerical approximation provided by the two methods is shown to be comparable

Towards a mathematical understanding of learning from few examples with nonlinear feature maps

We consider the problem of data classification where the training set consists of just a few data points. We explore this phenomenon mathematically and reveal key relationships between the geometry of an AI model's feature space, the structure of the underlying data distributions, and the model's generalisation capabilities. The main thrust of our analysis is to reveal the influence on the model's generalisation capabilities of nonlinear feature transformations mapping the original data into high, and possibly infinite, dimensional spaces.Comment: 18 pages, 8 figure

The Impact of Atmospheric Fluctuations on Degree-scale Imaging of the Cosmic Microwave Background

Fluctuations in the brightness of the Earth's atmosphere originating from water vapor are an important source of noise for ground-based instruments attempting to measure anisotropy in the Cosmic Microwave Background. This paper presents a model for the atmospheric fluctuations and derives simple expressions to predict the contribution of the atmosphere to experimental measurements. Data from the South Pole and from the Atacama Desert in Chile, two of the driest places on Earth, are used to assess the level of fluctuations at each site.Comment: 29 pages, 7 figures, 1 table, appears in The Astrophysical Journa

Efficient High-Order Space-Angle-Energy Polytopic Discontinuous Galerkin Finite Element Methods for Linear Boltzmann Transport

We introduce an $hp$-version discontinuous Galerkin finite element method (DGFEM) for the linear Boltzmann transport problem. A key feature of this new method is that, while offering arbitrary order convergence rates, it may be implemented in an almost identical form to standard multigroup discrete ordinates methods, meaning that solutions can be computed efficiently with high accuracy and in parallel within existing software. This method provides a unified discretisation of the space, angle, and energy domains of the underlying integro-differential equation and naturally incorporates both local mesh and local polynomial degree variation within each of these computational domains. Moreover, general polytopic elements can be handled by the method, enabling efficient discretisations of problems posed on complicated spatial geometries. We study the stability and $hp$-version a priori error analysis of the proposed method, by deriving suitable $hp$-approximation estimates together with a novel inf-sup bound. Numerical experiments highlighting the performance of the method for both polyenergetic and monoenergetic problems are presented.Comment: 27 pages, 2 figure

The Boundaries of Verifiable Accuracy, Robustness, and Generalisation in Deep Learning

In this work, we assess the theoretical limitations of determining guaranteed stability and accuracy of neural networks in classification tasks. We consider classical distribution-agnostic framework and algorithms minimising empirical risks and potentially subjected to some weights regularisation. We show that there is a large family of tasks for which computing and verifying ideal stable and accurate neural networks in the above settings is extremely challenging, if at all possible, even when such ideal solutions exist within the given class of neural architectures

Anaesthetic subspecialties and sustainable healthcare: a narrative review

Summary: The principles of environmentally sustainable healthcare as applied to anaesthesia and peri‐operative care are well documented. Associated recommendations focus on generic principles that can be applied to all areas of practice. These include reducing the use of inhalational anaesthetic agents and carbon dioxide equivalent emissions of modern peri‐operative care. However, four areas of practice have specific patient, surgical and anaesthetic factors that present barriers to the implementation of some of these principles, namely: neuroanaesthesia; obstetric; paediatric; and cardiac anaesthesia. This narrative review describes these factors and synthesises the available evidence to highlight areas of sustainable practice clinicians can address today, as well as posing several unanswered questions for the future. In neuroanaesthesia, improvements can be made by undertaking awake surgery, moving towards more reusables and embracing telemedicine in quaternary services. Obstetric anaesthesia continues to present questions regarding how services can move away from nitrous oxide use or limit its release to the environment. The focus for paediatric anaesthesia is addressing the barriers to total intravenous and regional anaesthesia. For cardiac anaesthesia, a significant emphasis is determining how to focus the substantial resources required on those who will benefit from cardiac interventions, rather than universal implementation. Whilst the landscape of evidence‐based sustainable practice is evolving, there remains an urgent need for further original evidence in healthcare sustainability targeting these four clinical areas