This work is supported by the Engineering and Physical Sciences Research Council (EPSRC) grant EP/M507970/1. The author acknowledges further funding from the World Bank GFDRR and a grant from University College London’s STEaPP pump-priming fund. The…

Machine learning systems are increasingly used to support public sector decision-making across a variety of sectors. Given concerns around accountabi…

When inferring unknown parameters or comparing different models, data must be compared to underlying theory. Even if a model has no closed-form solut…

Novel uses for 2-dimensional materials like graphene and hexagonal boron nitride (h-BN) are being frequently discovered especially for membrane and c…

HCI is well-placed to help enable the regulatory effectiveness of the GDPR in relation to algorithmic fairness and accountability. Here we have touched on different points where governance might come into play—model training and model application—bu…

In this short paper, we consider the roles of HCI in enabling the better governance of consequential machine learning systems using the rights and ob…

In this section we establish the joint probability (6.7) and then prove Theorems 1.2 and 1.3.

We consider a Galton-Watson process with immigration $(\mathcal{Z}_n)$, with offspring probabilities $(p_i)$ and immigration probabilities $(q_i)$. I…

This section is devoted to the proof of Theorem 3.2. We begin showing that strong solutions on closed stochastic intervals exist.

We prove existence and uniqueness of strong solutions for a class of semilinear stochastic evolution equations driven by general Hilbert space-valued…

We propose a Nitsche-based fictitious domain method for the three field Stokes problem in which the boundary of the domain is allowed to cross throug…

Inference on a scalar parameter of interest is commonly constructed using a Wald statistic, on the grounds of the validity of the standard normal app…

Dirichlet Process Mixture (DPM) models have been increasingly employed to specify random partition models that take into account possible patterns wi…

Moving mesh methods provide an efficient way of solving partial differential equations for which large, localised variations in the solution necessit…

Studies of collective human behavior in the social sciences, often grounded in details of actions by individuals, have much to offer `social' models …

Driven-dissipative systems in two dimensions can differ substantially from their equilibrium counterparts. In particular, a dramatic loss of off-diag…

We propose a new class of univariate nonstationary time series models, using the framework of modulated time series, which is appropriate for the ana…

We present and test a method that dramatically reduces variance arising from the sparse sampling of wavemodes in cosmological simulations. The method…

Models of complex systems are widely used in the physical and social sciences, and the concept of layering, typically building upon graph-theoretic s…

Reconstruction of PET images is an ill-posed inverse problem and often requires iterative algorithms to achieve good image quality for reliable clini…

We study the fully nonlinear dynamical Cosserat micropolar elasticity problem in space with three dimensionals with various energy functionals depend…

Universal quantum computation encoded over continuous variables can be achieved via Gaussian measurements acting on entangled non-Gaussian states. Ho…

Tidal gravitational forces can modify the shape of galaxies and clusters of galaxies, thus correlating their orientation with the surrounding matter …

Computational modelling is helpful for elucidating the cellular mechanisms driving biological morphogenesis. Previous simulation studies of blood ves…

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