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An in-situ neutron diffraction technique was used to investigate the lattice-strain distributions and plastic deformation around a crack tip after overload. The lattice-strain profiles around a crack tip were measured as a function of the applied load during the tensile loading cycles after overload. Dislocation densities calculated from the diffraction peak broadening were presented as a function of the distance from the crack tip. Furthermore, the crystallographic orientation variations were examined near a crack tip using polychromatic X-ray microdiffraction combined with differential aperture microscopy. Crystallographic tilts are considerably observed beneath the surface around a crack tip, and these are consistent with the high dislocation densities near the crack tip measured by neutron peak broadening.

We are interested in the problem of cross-modal retrieval for web image search, where the goal is to retrieve images relevant to a text query. While most of the current approaches for cross-modal retrieval revolve around learning how to represent text and images in a shared latent space, we take a different direction: we propose to generalize the cross-modal relevance feedback mechanism, a simple yet effective unsupervised method, that relies on standard information retrieval heuristics and the choice of a few hyper-parameters. We show that we can cast it as a supervised representation learning problem on graphs, using graph convolutions operating jointly over text and image features, namely crossmodal graph convolutions. The proposed architecture directly learns how to combine image and text features for the ranking task, while taking into account the context given by all the other elements in the set of images to be (re-)ranked. We validate our approach on two datasets: a public dataset from a MediaEval challenge, and a small sample of proprietary image search query logs, referred as WebQ. Our experiments demonstrate that our model improves over standard baselines.