Adaptive Bayesian Quantum Tomography

In this letter we revisit the problem of optimal design of quantum tomographic experiments. In contrast to previous approaches where an optimal set of measurements is decided in advance of the experiment, we allow for measurements to be adaptively and efficiently re-optimised depending on data collected so far. We develop an adaptive statistical framework based on Bayesian inference and Shannon's information, and demonstrate a ten-fold reduction in the total number of measurements required as compared to non-adaptive methods, including mutually unbiased bases.Comment: 4 pages, 3 figures, updated references, clarified expositio

Contributions to Foundation Engineering in Geotechnique

Many of the important developments in the field of foundation engineering have been addressed in Géotechnique papers over the past 60 years. This paper briefly reviews some of these developments and related articles, particularly with respect to shallow and deep foundations. In the early days of Géotechnique, the power to perform sophisticated numerical analyses did not exist. Papers tended to focus on the solution of problems using simple models in which soil was modelled either as linear elastic or as perfectly plastic. Engineers sought simple closed-form analytical solutions for boundary-value problems. With the development of more powerful analytical, computational and experimental capabilities, and of more sophisticated pile installation technology (especially offshore), more recent papers have explored much more sophisticated approaches to a range of foundation problems, striving to achieve more realistic representation of working conditions. Géotechnique papers have attempted to solve the problems faced by the foundation engineering industry, with a strong emphasis on the underlying science; as a result, these papers have played a key role in the advancement of both the science and its applications in our discipline

Observed dynamic soil–structure interaction in scale testing of offshore wind turbine foundations

Monopile foundations have been commonly used to support offshore wind turbine generators (WTGs), but this type of foundation encounters economic and technical limitations for larger WTGs in water depths exceeding 30 m. Offshore wind farm projects are increasingly turning to alternative multipod foundations (for example tetrapod, jacket and tripods) supported on shallow foundations to reduce the environmental effects of piling noise. However the characteristics of these foundations under dynamic loading or long term cyclic wind turbine loading are not fully understood. This paper summarises the results from a series of small scaled tests (1:100, 1:150 and 1:200) of a complete National Renewable Energy Laboratory (NREL) wind turbine model on three types of foundations: monopiles, symmetric tetrapod and asymmetric tripod. The test bed used consists of either kaolin clay or sand and up to 1.4 million loading cycles were applied. The results showed that the multipod foundations (symmetric or asymmetric) exhibit two closely spaced natural frequencies corresponding to the rocking modes of vibration in two principle axes. Furthermore, the corresponding two spectral peaks change with repeated cycles of loading and they converge for symmetric tetrapods but not for asymmetric tripods. From the fatigue design point of view, the two spectral peaks for multipod foundations broaden the range of frequencies that can be excited by the broadband nature of the environmental loading (wind and wave) thereby impacting the extent of motions. Thus the system lifespan (number of cycles to failure) may effectively increase for symmetric foundations as the two peaks will tend to converge. However, for asymmetric foundations the system life may continue to be affected adversely as the two peaks will not converge. In this sense, designers should prefer symmetric foundations to asymmetric foundations

Probabilistic machine learning and artificial intelligence.

How can a machine learn from experience? Probabilistic modelling provides a framework for understanding what learning is, and has therefore emerged as one of the principal theoretical and practical approaches for designing machines that learn from data acquired through experience. The probabilistic framework, which describes how to represent and manipulate uncertainty about models and predictions, has a central role in scientific data analysis, machine learning, robotics, cognitive science and artificial intelligence. This Review provides an introduction to this framework, and discusses some of the state-of-the-art advances in the field, namely, probabilistic programming, Bayesian optimization, data compression and automatic model discovery.The author acknowledges an EPSRC grant EP/I036575/1, the DARPA PPAML programme, a Google Focused Research Award for the Automatic Statistician and support from Microsoft Research.This is the author accepted manuscript. The final version is available from NPG at http://www.nature.com/nature/journal/v521/n7553/full/nature14541.html#abstract

Models, measurement and inference in epithelial tissue dynamics

The majority of solid tumours arise in epithelia and therefore much research effort has gone into investigating the growth, renewal and regulation of these tissues. Here we review different mathematical and computational approaches that have been used to model epithelia. We compare different models and describe future challenges that need to be overcome in order to fully exploit new data which present, for the first time, the real possibility for detailed model validation and comparison

Statistical fitting of undrained strength data

An approach is described, based on Bayesian statistical methods, that allows the fitting of a design profile to a set of measurements of undrained strengths. In particular allowance is made for the automatic determination of not only the positions of boundaries between geological units, but also the selection of the number of units to model the data in an appropriate way

DEVELOPMENT OF THE CONE PRESSUREMETER

Methods of interpretation of the cone pressuremeter in clay and in sand are discussed. A method of analysis for the test in clay had previously been compared with results in stiff overconsolidated clay, and is compared here with results in soft clay at Bothkennar. Both shear strength and shear modulus can be measured successfully, and a discussion is presented of comparisons of shear modulus measured by different techniques. In sand interpretation methods are based on correlations determined from calibration chamber tests. The correlations can be used to estimate horizontal stress and relative density, and unload-reload loops can be used to determine stiffness. -from Author