Joint Prediction of Depths, Normals and Surface Curvature from RGB Images using CNNs

Understanding the 3D structure of a scene is of vital importance, when it comes to developing fully autonomous robots. To this end, we present a novel deep learning based framework that estimates depth, surface normals and surface curvature by only using a single RGB image. To the best of our knowledge this is the first work to estimate surface curvature from colour using a machine learning approach. Additionally, we demonstrate that by tuning the network to infer well designed features, such as surface curvature, we can achieve improved performance at estimating depth and normals.This indicates that network guidance is still a useful aspect of designing and training a neural network. We run extensive experiments where the network is trained to infer different tasks while the model capacity is kept constant resulting in different feature maps based on the tasks at hand. We outperform the previous state-of-the-art benchmarks which jointly estimate depths and surface normals while predicting surface curvature in parallel

Unsteady wake modelling for tidal current turbines

The authors present a numerical model for three-dimensional unsteady wake calculations for tidal turbines. Since wakes are characterised by the shedding of a vortex sheet from the rotor blades, the model is based on the vorticity transport equations. A vortex sheet may be considered a jump contact discontinuity in tangential velocity with, in inviscid hydrodynamic terms, certain kinematic and dynamic conditions across the sheet. The kinematic condition is that the sheet is a stream surface with zero normal fluid velocity; the dynamic condition is that the pressure is equal on either side of the sheet. The dynamic condition is explicitly satisfied at the trailing edge only, via an approximation of the Kutta condition. The shed vorticity is the span-wise derivative of bound circulation, and the trailed vorticity is the time derivative of bound circulation, and is convected downstream from the rotors using a finite-volume solution of vorticity transport equations thus satisfying the kinematic conditions. Owing to an absence in the literature of pressure data for marine turbines, results from the code are presented for the NREL-UAE Phase IV turbine. Axial flow cases show a close match in pressure coefficients at various spanwise stations; however, yawed flow cases demonstrate the shortcomings of a modelling strategy lacking viscosity

Histone modifications influence the action of Snf2 family remodelling enzymes by different mechanisms

AbstractAlteration of chromatin structure by chromatin modifying and remodelling activities is a key stage in the regulation of many nuclear processes. These activities are frequently interlinked, and many chromatin remodelling enzymes contain motifs that recognise modified histones. Here we adopt a peptide ligation strategy to generate specifically modified chromatin templates and used these to study the interaction of the Chd1, Isw2 and RSC remodelling complexes with differentially acetylated nucleosomes. Specific patterns of histone acetylation are found to alter the rate of chromatin remodelling in different ways. For example, histone H3 lysine 14 acetylation acts to increase recruitment of the RSC complex to nucleosomes. However, histone H4 tetra-acetylation alters the spectrum of remodelled products generated by increasing octamer transfer in trans. In contrast, histone H4 tetra-acetylation was also found to reduce the activity of the Chd1 and Isw2 remodelling enzymes by reducing catalytic turnover without affecting recruitment. These observations illustrate a range of different means by which modifications to histones can influence the action of remodelling enzymes