Aquafed - another pressure group for private water

An analysis of the new private water lobby group, Aquafed, and the other forms of lobbying and pressure used by the private water companies

Multi-symplectic discretisation of wave map equations

We present a new multi-symplectic formulation of constrained Hamiltonian partial differential equations, and we study the associated local conservation laws. A multi-symplectic discretisation based on this new formulation is exemplified by means of the Euler box scheme. When applied to the wave map equation, this numerical scheme is explicit, preserves the constraint and can be seen as a generalisation of the Shake algorithm for constrained mechanical systems. Furthermore, numerical experiments show excellent conservation properties of the numerical solutions

‘You don't understand us!’ An inside perspective on adventure climbing

This paper presents a specific (insider) perspective of a small group of experienced male Scottish adventure climbers and explores through in-depth semi-structured interviews their attitudes, strategies and justifications associated with potentially high-risk climbing situations. Attention is paid to how participants feel that they are represented and viewed by others (outsiders) who do not participate in mountaineering and climbing activities. Climbers identify the significance of media, commercial and social representations of them as risk takers. The analysis explores risk as being socially constructed, with the associated assumptions being embedded in particular discourses. Climbers present themselves as rational managers of risk and provide examples of their risk-management strategies, with such characterizations being central to their identity as climbers

Single-index copulae

We introduce so-called "single-index copulae". They are semi-parametric conditional copulae whose parameter is an unknown "link" function of a univariate index only. We provide estimates of this link function and of the finite dimensional unknown parameter. The asymptotic properties of the latter estimates are stated. Thanks to some properties of conditional Kendall's tau, we illustrate our technical conditions with several usual copula families.Comment: Revised version: correction of Assumption 3 and some minor induced modification

Additive Kernels for Gaussian Process Modeling

Gaussian Process (GP) models are often used as mathematical approximations of computationally expensive experiments. Provided that its kernel is suitably chosen and that enough data is available to obtain a reasonable fit of the simulator, a GP model can beneficially be used for tasks such as prediction, optimization, or Monte-Carlo-based quantification of uncertainty. However, the former conditions become unrealistic when using classical GPs as the dimension of input increases. One popular alternative is then to turn to Generalized Additive Models (GAMs), relying on the assumption that the simulator's response can approximately be decomposed as a sum of univariate functions. If such an approach has been successfully applied in approximation, it is nevertheless not completely compatible with the GP framework and its versatile applications. The ambition of the present work is to give an insight into the use of GPs for additive models by integrating additivity within the kernel, and proposing a parsimonious numerical method for data-driven parameter estimation. The first part of this article deals with the kernels naturally associated to additive processes and the properties of the GP models based on such kernels. The second part is dedicated to a numerical procedure based on relaxation for additive kernel parameter estimation. Finally, the efficiency of the proposed method is illustrated and compared to other approaches on Sobol's g-function

Random-bit optimal uniform sampling for rooted planar trees with given sequence of degrees and Applications

In this paper, we redesign and simplify an algorithm due to Remy et al. for the generation of rooted planar trees that satisfies a given partition of degrees. This new version is now optimal in terms of random bit complexity, up to a multiplicative constant. We then apply a natural process "simulate-guess-and-proof" to analyze the height of a random Motzkin in function of its frequency of unary nodes. When the number of unary nodes dominates, we prove some unconventional height phenomenon (i.e. outside the universal square root behaviour.)Comment: 19 page

Inference for Partially Observed Multitype Branching Processes and Ecological Applications

Multitype branching processes with immigration in one type are used to model the dynamics of stage-structured plant populations. Parametric inference is first carried out when count data of all types are observed. Statistical identifiability is proved together with derivation of consistent and asymptotically Gaussian estimators for all the parameters ruling the population dynamics model. However, for many ecological data, some stages (i.e. types) cannot be observed in practice. We study which mechanisms can still be estimated given the model and the data available in this context. Parametric inference is investigated in the case of Poisson distributions. We prove that identifiability holds for only a subset of the parameter set depend- ing on the number of generations observed, together with consistent and asymptotic properties of estimators. Finally, simulations are performed to study the behaviour of the estimators when the model is no longer Poisson. Quite good results are obtained for a large class of models with distributions having mean and variance within the same order of magnitude, leading to some stability results with respect to the Poisson assumption.Comment: 31 pages, 1 figur