Theory and Simulation of the diffusion of kinks on dislocations in bcc metals

Isolated kinks on thermally fluctuating (1/2) screw, edge and (1/2) edge dislocations in bcc iron are simulated under zero stress conditions using molecular dynamics (MD). Kinks are seen to perform stochastic motion in a potential landscape that depends on the dislocation character and geometry, and their motion provides fresh insight into the coupling of dislocations to a heat bath. The kink formation energy, migration barrier and friction parameter are deduced from the simulations. A discrete Frenkel-Kontorova-Langevin (FKL) model is able to reproduce the coarse grained data from MD at a fraction of the computational cost, without assuming an a priori temperature dependence beyond the fluctuation-dissipation theorem. Analytic results reveal that discreteness effects play an essential r\^ole in thermally activated dislocation glide, revealing the existence of a crucial intermediate length scale between molecular and dislocation dynamics. The model is used to investigate dislocation motion under the vanishingly small stress levels found in the evolution of dislocation microstructures in irradiated materials

Validation of Bayesian posterior distributions using a multidimensional Kolmogorov-Smirnov test

We extend the Kolmogorov–Smirnov (K-S) test to multiple dimensions by suggesting a R^n → [0, 1] mapping based on the probability content of the highest probability density region of the reference distribution under consideration; this mapping reduces the problem back to the one-dimensional case to which the standard K-S test may be applied. The universal character of this mapping also allows us to introduce a simple, yet general, method for the validation of Bayesian posterior distributions of any dimensionality. This new approach goes beyond validating software implementations; it provides a sensitive test for all assumptions, explicit or implicit, that underlie the inference. In particular, the method assesses whether the inferred posterior distribution is a truthful representation of the actual constraints on the model parameters. We illustrate our multidimensional K-S test by applying it to a simple two- dimensional Gaussian toy problem, and demonstrate our method for posterior validation in the real-world astrophysical application of estimating the physical parameters of galaxy clusters parameters from their Sunyaev–Zel’dovich effect in microwave background data. In the latter example, we show that the method can validate the entire Bayesian inference process across a varied population of objects for which the derived posteriors are different in each case.This work was supported by the UK Space Agency under grant ST/K003674/1. This work was performed using the Darwin Supercomputer of the University of Cambridge High Performance Computing Service (http://www.hpc.cam.ac.uk/), provided by Dell Inc. using Strategic Research Infrastructure Funding from the Higher Education Funding Council for England and funding from the Science and Technology Facilities Council.This is the author accepted manuscript. The final version is available from Oxford University Press via http://dx.doi.org/10.1093/mnras/stv111

Visualising gas heating from an RF plasma loudspeaker

In an electro-acoustic transduction mechanism, an ac modulation (here in the audio frequency range) of the electric field in an atmospheric pressure air plasma gives rise to a rapid increase in the gas temperature and dimensions of the gas volume. As in natural lightning, the rapid expansion in the ionised column though the air produces external pressure variations at the modulation frequency.  \ud Spatial and temporal measurement of the gas temperature can identify the nature of the thermal expansion and provide a direct approach to understanding its relationship to the sound pressure wave that is generated. However, the established method through spectroscopic measurement of rotational line emission from nitrogen molecules is limited to the main current channel where relaxation and subsequent optical emission of the excited nitrogen molecules occurs. The wider picture is revealed through the use of the Schlieren method where the refractive index gradients caused by gas heating in the plasma are imaged

Temperature dependence of surface reconstructions of Au on Pd(110)

Surface reconstructions of Au film on Pd(110) substrate are studied using a local Einstein approximation to quasiharmonic theory with the Sutton-Chen interatomic potential. Temperature dependent surface free energies for different coverages and surface structures are calculated. Experimentally observed transformations from $(1\times1)$ to $(1 \times 2)$ and $(1 \times 3)$ structures can be explained in the framework of this model. Also conditions for Stranski-Krastanov growth mode are found to comply with experiments. The domain of validity of the model neglecting mixing entropy is analyzed.Comment: 7 pages, REVTeX two-column format, 3 postscript figures available on request from [email protected] To appear in Phys. Rev. Letter

How to identify when a performance indicator has run its course

The official published version can be found at the link below.Increasing numbers of countries are using indicators to evaluate the quality of clinical care, with some linking payment to achievement. For performance frameworks to remain effective the indicators need to be regularly reviewed. The frameworks cannot cover all clinical areas, and achievement on chosen indicators will eventually reach a ceiling beyond which further improvement is not feasible. However, there has been little work on how to select indictors for replacement. The Department of Health decided in 2008 that it would regularly replace indicators in the national primary care pay for performance scheme, the Quality and Outcomes Framework, making a rigorous approach to removal a priority. We draw on our previous work on pay for performance and our current work advising the National Institute for Health and Clinical Excellence (NICE) on the Quality and Outcomes Framework to suggest what should be considered when planning to remove indicators from a clinical performance framework

Hi-Val: Iterative Learning of Hierarchical Value Functions for Policy Generation

Task decomposition is effective in manifold applications where the global complexity of a problem makes planning and decision-making too demanding. This is true, for example, in high-dimensional robotics domains, where (1) unpredictabilities and modeling limitations typically prevent the manual specification of robust behaviors, and (2) learning an action policy is challenging due to the curse of dimensionality. In this work, we borrow the concept of Hierarchical Task Networks (HTNs) to decompose the learning procedure, and we exploit Upper Confidence Tree (UCT) search to introduce HOP, a novel iterative algorithm for hierarchical optimistic planning with learned value functions. To obtain better generalization and generate policies, HOP simultaneously learns and uses action values. These are used to formalize constraints within the search space and to reduce the dimensionality of the problem. We evaluate our algorithm both on a fetching task using a simulated 7-DOF KUKA light weight arm and, on a pick and delivery task with a Pioneer robot