An early warning indicator for atmospheric blocking events using transfer operators

The existence of persistent midlatitude atmospheric flow regimes with time-scales larger than 5-10 days and indications of preferred transitions between them motivates to develop early warning indicators for such regime transitions. In this paper, we use a hemispheric barotropic model together with estimates of transfer operators on a reduced phase space to develop an early warning indicator of the zonal to blocked flow transition in this model. It is shown that, the spectrum of the transfer operators can be used to study the slow dynamics of the flow as well as the non-Markovian character of the reduction. The slowest motions are thereby found to have time scales of three to six weeks and to be associated with meta-stable regimes (and their transitions) which can be detected as almost-invariant sets of the transfer operator. From the energy budget of the model, we are able to explain the meta-stability of the regimes and the existence of preferred transition paths. Even though the model is highly simplified, the skill of the early warning indicator is promising, suggesting that the transfer operator approach can be used in parallel to an operational deterministic model for stochastic prediction or to assess forecast uncertainty

Estimation from quantized Gaussian measurements: when and how to use dither

Subtractive dither is a powerful method for removing the signal dependence of quantization noise for coarsely quantized signals. However, estimation from dithered measurements often naively applies the sample mean or midrange, even when the total noise is not well described with a Gaussian or uniform distribution. We show that the generalized Gaussian distribution approximately describes subtractively dithered, quantized samples of a Gaussian signal. Furthermore, a generalized Gaussian fit leads to simple estimators based on order statistics that match the performance of more complicated maximum likelihood estimators requiring iterative solvers. The order statistics-based estimators outperform both the sample mean and midrange for nontrivial sums of Gaussian and uniform noise. Additional analysis of the generalized Gaussian approximation yields rules of thumb for determining when and how to apply dither to quantized measurements. Specifically, we find subtractive dither to be beneficial when the ratio between the Gaussian standard deviation and quantization interval length is roughly less than one-third. When that ratio is also greater than 0.822/K^0.930 for the number of measurements K > 20, estimators we present are more efficient than the midrange.https://arxiv.org/abs/1811.06856Accepted manuscrip

Sphaleron-Bisphaleron bifurcations in a custodial-symmetric two-doublets model

The standard electroweak model is extended by means of a second Brout-Englert-Higgs-doublet. The symmetry breaking potential is chosen is such a way that (i) the Lagrangian possesses a custodial symmetry, (ii) a static, spherically symmetric ansatz of the bosonic fields consistently reduces the Euler-Lagrange equations to a set of differential equations. The potential involves, in particular, products of fields of the two doublets, with a coupling constant $\lambda_3$.Static, finite energy solutions of the classical equations are constructed. The regular, non-trivial solutions having the lowest classical energy can be of two types: sphaleron or bisphaleron, according to the coupling constants. A special emphasis is put to the bifurcation between these two types of solutions which is analyzed in function of the different constants of the model,namely of $\lambda_3$.Comment: 10 pages, 3 figure

Secondary teachers' perceptions of the effectiveness of their pre-service education and strategies to improve pre-service education for teachers: A school based training route in England

This study aims to provide a deeper understanding of the impact of an EBITT course on teachers' early professional development, identify strengths of the course and also the ways in which the training could be improved. Data collected was recorded during individual face- to- face interviews using a structured interview schedule. In devising our approach we utilised the model suggested by Sharon Feiman-Nemser in her article How do Teachers Learn to Teach? in Cochran - Smith et. al. (2008) Handbook of Research on Teacher Education The data was analysed to explore (after 2-4 years reflection): • which elements of initial training were valuable and less valuable • what they have learned since the course • which aspects of the course the teachers feel should be improved It was cross referenced against findings from national surveys of teachers in their post qualifying year of teaching (induction year) and early years of teaching conducted by the TDA. These findings were presented as part of a common wider international study on the same theme in four countries (UK, Spain, Australia, and Ireland)

Fabrication and test of lightweight honeycomb sandwich structures Final report

Fabrication and testing of lightweight honeycomb sandwich structure

Dead Time Compensation for High-Flux Ranging

Dead time effects have been considered a major limitation for fast data acquisition in various time-correlated single photon counting applications, since a commonly adopted approach for dead time mitigation is to operate in the low-flux regime where dead time effects can be ignored. Through the application of lidar ranging, this work explores the empirical distribution of detection times in the presence of dead time and demonstrates that an accurate statistical model can result in reduced ranging error with shorter data acquisition time when operating in the high-flux regime. Specifically, we show that the empirical distribution of detection times converges to the stationary distribution of a Markov chain. Depth estimation can then be performed by passing the empirical distribution through a filter matched to the stationary distribution. Moreover, based on the Markov chain model, we formulate the recovery of arrival distribution from detection distribution as a nonlinear inverse problem and solve it via provably convergent mathematical optimization. By comparing per-detection Fisher information for depth estimation from high- and low-flux detection time distributions, we provide an analytical basis for possible improvement of ranging performance resulting from the presence of dead time. Finally, we demonstrate the effectiveness of our formulation and algorithm via simulations of lidar ranging.Comment: Revision with added estimation results, references, and figures, and modified appendice

Fault-tolerant quantum computation with cluster states

The one-way quantum computing model introduced by Raussendorf and Briegel [Phys. Rev. Lett. 86 (22), 5188-5191 (2001)] shows that it is possible to quantum compute using only a fixed entangled resource known as a cluster state, and adaptive single-qubit measurements. This model is the basis for several practical proposals for quantum computation, including a promising proposal for optical quantum computation based on cluster states [M. A. Nielsen, arXiv:quant-ph/0402005, accepted to appear in Phys. Rev. Lett.]. A significant open question is whether such proposals are scalable in the presence of physically realistic noise. In this paper we prove two threshold theorems which show that scalable fault-tolerant quantum computation may be achieved in implementations based on cluster states, provided the noise in the implementations is below some constant threshold value. Our first threshold theorem applies to a class of implementations in which entangling gates are applied deterministically, but with a small amount of noise. We expect this threshold to be applicable in a wide variety of physical systems. Our second threshold theorem is specifically adapted to proposals such as the optical cluster-state proposal, in which non-deterministic entangling gates are used. A critical technical component of our proofs is two powerful theorems which relate the properties of noisy unitary operations restricted to act on a subspace of state space to extensions of those operations acting on the entire state space.Comment: 31 pages, 54 figure

Simulating Impacts of Extreme Weather Events on Urban Transport Infrastructure in the UK

Urban areas face many risks from future climate change and their infrastructure will be placed under more pressure due to changes in climate extremes. Using the Tyndall Centre Urban Integrated Assessment Framework, this paper describes a methodology used to assess the impacts of future climate extremes on transport infrastructure in London. Utilising high-resolution projections for future climate in the UK, alongside stochastic weather generators for downscaling, urban temperature and flooding models are used to provide information on the likelihood of future extremes. These are then coupled with spatial network models of urban transport infrastructure and, using thresholds to define the point at which systems cease to function normally, disruption to the networks can be simulated. Results are shown for both extreme heat and urban surface water flooding events and the impacts on the travelling population, in terms of both disruption time and monetary cost