Shock accelerated cylindrical gas inhomogeneities. Part 2 - A heavy gas cylinder

Experiments have been carried out in which a cylindrical volume of a heavy gas is impulsively accelerated by a weak shock wave. A laminar jet of sulphur hexafluoride (SF_6) is used to produce the heavy gas cylinder. Planar laser induced fluorescence (PLIF) is used to visualize the flow. In viewing the PLIF images it is discovered that the vorticity that early on resides on the boundary between the two gasses, separates from the cylinder to form a pair of vortices. Subsequently these vortices wrap the heavy gas around them. This process is quite different from what is observed when the cylinder is lighter than its surroundings. Similar experiments with helium (part 1 of this series) showed that a small amount light gas stays with the vorticity, eventually becoming part of the vortex cores. A simple model capable of explaining these differences is presented. In addition, the displacement of the jet cross section is measured and agrees reasonably well with previous experimental and computational results

Shock-induced mixing of a light-gas cylinder

Experiments have been carried out to quantify the mixing induced by the interaction of a weak shock wave with a cylindrical volume of a gas (helium) that is lighter than its surroundings (air). In these experiments a round laminar jet was used to produce the light-gas cylinder, and planar laser-induced fluorescence (PLIF), utilizing a fluorescent tracer (biacetyl) mixed with the helium, was used to visualize the flow. These techniques provide a higher quality of flow visualization than that obtained in previous investigations. In addition, the PLIF technique could be used for the measurement of species concentration. The distortion of the helium cylinder produced by the passing shock wave was found to be similar to that displayed by images from previous experimental and computational investigations. The downstream displacement of several points on the boundary of the light-gas cylinder are measured and agree reasonably well with the results of earlier experimental and theoretical studies as well. Because the mixing process causes the helium originally contained within the cylinder to be dispersed into the surrounding air, the PLIF image area inside the contour at one half the maximum concentration of the fluorescent tracer decreases as the two gases mixed. The change in this area is used as a measure of the mixing rate, and it is found that the time rate of change of this area divided by the area of the initial jet is approximately - 0.7 X 10^3 S^(-1)

Statically checking confidentiality via dynamic labels

This paper presents a new approach for verifying confidentiality for programs, based on abstract interpretation. The framework is formally developed and proved correct in the theorem prover PVS. We use dynamic labeling functions to abstractly interpret a simple programming language via modification of security levels of variables. Our approach is sound and compositional and results in an algorithm for statically checking confidentiality

Presence of Salmonella and Campylobacter spp. in Wild Small Mammals on Organic Farms

The presence of Salmonella and Campylobacter spp. in rodents and insectivores (n 282) was investigated on organic farms. Infections were encountered in house mice (8 of 83 Campylobacter positive and 1 of 83 Salmonella sp. strain Livingstone positive) and brown rats (1 of 8 Campylobacter positive) but not in other species. No shared Campylobacter genotypes were found between rodent and pig manure isolates. Effective on-farm rodent management is recommended

WarpNet: Weakly Supervised Matching for Single-view Reconstruction

We present an approach to matching images of objects in fine-grained datasets without using part annotations, with an application to the challenging problem of weakly supervised single-view reconstruction. This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone. We overcome this challenge through a novel deep learning architecture, WarpNet, that aligns an object in one image with a different object in another. We exploit the structure of the fine-grained dataset to create artificial data for training this network in an unsupervised-discriminative learning approach. The output of the network acts as a spatial prior that allows generalization at test time to match real images across variations in appearance, viewpoint and articulation. On the CUB-200-2011 dataset of bird categories, we improve the AP over an appearance-only network by 13.6%. We further demonstrate that our WarpNet matches, together with the structure of fine-grained datasets, allow single-view reconstructions with quality comparable to using annotated point correspondences.Comment: to appear in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 201

Deep Hierarchical Parsing for Semantic Segmentation

This paper proposes a learning-based approach to scene parsing inspired by the deep Recursive Context Propagation Network (RCPN). RCPN is a deep feed-forward neural network that utilizes the contextual information from the entire image, through bottom-up followed by top-down context propagation via random binary parse trees. This improves the feature representation of every super-pixel in the image for better classification into semantic categories. We analyze RCPN and propose two novel contributions to further improve the model. We first analyze the learning of RCPN parameters and discover the presence of bypass error paths in the computation graph of RCPN that can hinder contextual propagation. We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function. Secondly, we use an MRF on the parse tree nodes to model the hierarchical dependency present in the output. Both modifications provide performance boosts over the original RCPN and the new system achieves state-of-the-art performance on Stanford Background, SIFT-Flow and Daimler urban datasets.Comment: IEEE CVPR 201