Shifting atomic patterns: on the origin of the different atomic-scale patterns of graphite as observed using scanning tunnelling microscopy.

We present an in-depth study of the myriad atomically resolved patterns observed on graphite using the scanning tunnelling microscope (STM) over the past three decades. Through the use of highly resolved atomic resolution images, we demonstrate how the interactions between the different graphene layers comprising graphite affect the local surface atomic charge density and its resulting symmetry orientation, with particular emphasis on interactions that are thermodynamically unstable. Moreover, the interlayer graphene coupling is controlled experimentally by varying the tip-surface interaction, leading to associated changes in the atomic patterns. The images are corroborated by first-principles calculations, further validating our claim that surface graphene displacement, coming both from lateral and vertical displacement of the top graphene layer, forms the basis of the rich variety of atomic patterns observed in STM experiments on graphite.

The nature and origin of deep ocean clay crust from the Gulf of Guinea

Deep ocean sediments off the west coast of Africa exhibit a peculiar undrained strength profile in the form of a crust, albeit of exceptionally high water content, overlying normally consolidated clay. Hot-oil pipelines are installed into these crustal sediments, so their origins and characteristics are of great interest to pipeline designers. This paper provides evidence for the presence of burrowing invertebrates in crust material, and for the way sediment properties are modified through their creation of burrows, and through the deposition of faecal pellets. A variety of imaging techniques are used to make these connections, including photography, scanning electron microscopy and X-ray computer tomography. However, the essential investigative technology is simply the wet-sieving of natural cores, which reveals that up to 60% by dry mass of the crustal material can consist of smooth, highly regular, sand-sized capsules that have been identified as the faecal pellets of invertebrates such as polychaetes. Mechanical tests reveal that these pellets are quite robust under effective stresses of the order of 10 kPa, acting like sand grains within a matrix of fines. Their abundance correlates closely with the measured strength of the crust. While this can easily be accepted in the context of a pellet fraction as high as 60%, the question arises how a smaller proportion of pellets, such as 20%, is apparently able to enhance significantly the strength of a sediment that otherwise appears to be normally consolidated. A hypothesis is suggested based on the composition of the matrix of fines around the pellets. These appear to consist of agglomerates of clay platelets, which may be the result of the breakdown of pellets by other organisms. Their continued degradation at depths in excess of 1 m is taken to explain the progressive loss of crustal strength thereafter.

Origin of transient crosstalk and its reduction in phase-only LCOS wavelength selective switches

The transient crosstalk in a phase-only liquid crystal on silicon (LCOS) based wavelength selective switch using a Fourier transform setup was investigated. Its origin was identified using an in situ test procedure and found to be related to the transient phase patterns displayed by the LCOS device during the switching. Two different methods were proposed to reduce the transient crosstalk without the need to modify the optics or electronics in use. Experimental results show both methods are able to reduce the worst-case transient crosstalk by at least 5 dB. © 1983-2013 IEEE.

Automatic construction and natural-language description of nonparametric regression models

Copyright © 2014, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved. This paper presents the beginnings of an automatic statistician, focusing on regression problems. Our system explores an open-ended space of statistical models to discover a good explanation of a data set, and then produces a detailed report with figures and natural- language text. Our approach treats unknown regression functions non- parametrically using Gaussian processes, which has two important consequences. First, Gaussian processes can model functions in terms of high-level properties (e.g. smoothness, trends, periodicity, changepoints). Taken together with the compositional structure of our language of models this allows us to automatically describe functions in simple terms. Second, the use of flexible nonparametric models and a rich language for composing them in an open-ended manner also results in state- of-the-art extrapolation performance evaluated over 13 real time series data sets from various domains.