Community Structure Characterization

This entry discusses the problem of describing some communities identified in a complex network of interest, in a way allowing to interpret them. We suppose the community structure has already been detected through one of the many methods proposed in the literature. The question is then to know how to extract valuable information from this first result, in order to allow human interpretation. This requires subsequent processing, which we describe in the rest of this entry

Robust Poisson Surface Reconstruction

Abstract. We propose a method to reconstruct surfaces from oriented point clouds with non-uniform sampling and noise by formulating the problem as a convex minimization that reconstructs the indicator func-tion of the surface’s interior. Compared to previous models, our recon-struction is robust to noise and outliers because it substitutes the least-squares fidelity term by a robust Huber penalty; this allows to recover sharp corners and avoids the shrinking bias of least squares. We choose an implicit parametrization to reconstruct surfaces of unknown topology and close large gaps in the point cloud. For an efficient representation, we approximate the implicit function by a hierarchy of locally supported basis elements adapted to the geometry of the surface. Unlike ad-hoc bases over an octree, our hierarchical B-splines from isogeometric analysis locally adapt the mesh and degree of the splines during reconstruction. The hi-erarchical structure of the basis speeds-up the minimization and efficiently represents clustered data. We also advocate for convex optimization, in-stead isogeometric finite-element techniques, to efficiently solve the min-imization and allow for non-differentiable functionals. Experiments show state-of-the-art performance within a more flexible framework.

No iron fertilization in the equatorial Pacific Ocean during the last ice age

The equatorial Pacific Ocean is one of the major high-nutrient, low-chlorophyll regions in the global ocean. In such regions, the consumption of the available macro-nutrients such as nitrate and phosphate is thought to be limited in part by the low abundance of the critical micro-nutrient iron1. Greater atmospheric dust deposition2 could have fertilized the equatorial Pacific with iron during the last ice age—the Last Glacial Period (LGP) but the effect of increased ice-age dust fluxes on primary productivity in the equatorial Pacific remains uncertain. Here we present meridional transects of dust (derived from the 232Th proxy), phytoplankton productivity (using opal, 231Pa/230Th and excess Ba), and the degree of nitrate consumption (using foraminifera-bound δ15N) from six cores in the central equatorial Pacific for the Holocene (0–10,000 years ago) and the LGP (17,000–27,000 years ago). We find that, although dust deposition in the central equatorial Pacific was two to three times greater in the LGP than in the Holocene, productivity was the same or lower, and the degree of nitrate consumption was the same. These biogeochemical findings suggest that the relatively greater ice-age dust fluxes were not large enough to provide substantial iron fertilization to the central equatorial Pacific. This may have been because the absolute rate of dust deposition in the LGP (although greater than the Holocene rate) was very low. The lower productivity coupled with unchanged nitrate consumption suggests that the subsurface major nutrient concentrations were lower in the central equatorial Pacific during the LGP. As these nutrients are today dominantly sourced from the Subantarctic Zone of the Southern Ocean, we propose that the central equatorial Pacific data are consistent with more nutrient consumption in the Subantarctic Zone, possibly owing to iron fertilization as a result of higher absolute dust fluxes in this region7,8. Thus, ice-age iron fertilization in the Subantarctic Zone would have ultimately worked to lower, not raise, equatorial Pacific productivity

DensePose 3D: lifting canonical surface maps of articulated objects to the third dimension

We tackle the problem of monocular 3D reconstruction of articulated objects like humans and animals. We contribute DensePose 3D, a method that can learn such reconstructions in a weakly supervised fashion from 2D image annotations only. This is in stark contrast with previous deformable reconstruction methods that use parametric models such as SMPL pre-trained on a large dataset of 3D object scans. Because it does not require 3D scans, DensePose 3D can be used for learning a wide range of articulated categories such as different animal species. The method learns, in an end-to-end fashion, a soft partition of a given category-specific 3D template mesh into rigid parts together with a monocular reconstruction network that predicts the part motions such that they reproject correctly onto 2D DensePose-like surface annotations of the object. The decomposition of the object into parts is regularized by expressing part assignments as a combination of the smooth eigenfunctions of the Laplace-Beltrami operator. We show significant improvements compared to state-of-the-art nonrigid structure-from-motion baselines on both synthetic and real data on categories of humans and animals

Unsupervised learning of 3D object categories from videos in the wild

Our goal is to learn a deep network that, given a small number of images of an object of a given category, reconstructs it in 3D. While several recent works have obtained analogous results using synthetic data or assuming the availability of 2D primitives such as keypoints, we are interested in working with challenging real data and with no manual annotations. We thus focus on learning a model from multiple views of a large collection of object instances. We contribute with a new large dataset of object centric videos suitable for training and benchmarking this class of models. We show that existing techniques leveraging meshes, voxels, or implicit surfaces, which work well for reconstructing isolated objects, fail on this challenging data. Finally, we propose a new neural network design, called warp-conditioned ray embedding (WCR), which significantly improves reconstruction while obtaining a detailed implicit representation of the object surface and texture, also compensating for the noise in the initial SfM reconstruction that bootstrapped the learning process. Our evaluation demonstrates performance improvements over several deep monocular reconstruction baselines on existing benchmarks and on our novel dataset. For additional material please visit: https://henzler. github.io/publication/unsupervised_videos/

Companion papers to information and development : a strategy for IDRC

Related title: Information and development : a strategy for IDRC; executive summar

AlNi coatings on the internal surfaces of tubes

The oxidation resistance of nickel based superalloys used for high temperature components in gas turbine can be increased by the formation of high temperature intermetallic NiAl compounds. In this study the internal surfaces of Inconel tubes, with an internal diameter of 1 mm and a total length of 280 mm were aluminised by means of a CVD process using AlCl3 and Al as precursors. Subsequently, the aluminised samples were given a heat treatment under vacuum. Afterwards, the thickness and the composition homogeneity of the resulting NiAl layers before and after heat treatment were examined by electro-optical techniques that included optical and scanning electron microscopes and EDS X-ray microanalysis. The homogeneity of the resulted coatings was correlated to the deposition temperature and deposition time