Self-Sampling for Neural Point Cloud Consolidation

In this paper, we introduce a deep learning technique for consolidating and sharp feature generation of point clouds using only the input point cloud itself. Rather than explicitly define a prior that describes typical shape characteristics (i.e., piecewise-smoothness), or a heuristic policy for generating novel sharp points, we opt to learn both using a neural network with shared-weights. Instead of relying on a large collection of manually annotated data, we use the self-supervision present within a single shape, i.e., self-prior, to train the network, and learn the underlying distribution of sharp features specific to the given input point cloud. By learning to map a low-curvature subset of the input point cloud to a disjoint high-curvature subset, the network formalizes the shape-specific characteristics and infers how to generate sharp points. During test time, the network is repeatedly fed a random subset of points from the input and displaces them to generate an arbitrarily large set of novel sharp feature points. The local shared weights are optimized over the entire shape, learning non-local statistics and exploiting the recurrence of local-scale geometries. We demonstrate the ability to generate coherent sets of sharp feature points on a variety of shapes, while eliminating outliers and noise

PointGMM: a Neural GMM Network for Point Clouds

Point clouds are a popular representation for 3D shapes. However, they encode a particular sampling without accounting for shape priors or non-local information. We advocate for the use of a hierarchical Gaussian mixture model (hGMM), which is a compact, adaptive and lightweight representation that probabilistically defines the underlying 3D surface. We present PointGMM, a neural network that learns to generate hGMMs which are characteristic of the shape class, and also coincide with the input point cloud. PointGMM is trained over a collection of shapes to learn a class-specific prior. The hierarchical representation has two main advantages: (i) coarse-to-fine learning, which avoids converging to poor local-minima; and (ii) (an unsupervised) consistent partitioning of the input shape. We show that as a generative model, PointGMM learns a meaningful latent space which enables generating consistent interpolations between existing shapes, as well as synthesizing novel shapes. We also present a novel framework for rigid registration using PointGMM, that learns to disentangle orientation from structure of an input shape.Comment: CVPR 2020 -- final versio

Effect of hyperemesis gravidarum on child neurodevelopment

BackgroundPregnancy outcome following hyperemesis gravidarum (HG) has been sparsely reported. This review article aims at critically reviewing the first prospective study of foetal long-term neurodevelopment after maternal HG.AimsThis review aimed at critically appraising the first prospective human study that aimed at investigating long term child neurodevelopment after exposure to maternal HG.MethodsIn this study, women with nausea and vomiting of pregnancy treated with doxylamine–pyridoxine (Diclectin) or with no pharmacotherapy were prospectively recruited. Their children (ages 3 6/12 to 6 11/12 years) were assessed for development using standardized psychological tests. The study cohort was divided into 2 groups: 1) severe NVP necessitating hospitalization of the woman for rehydration and electrolyte corrections (n=22) and 2) all other cases of nausea and vomiting of pregnancy (n=197). ResultsChildren of hospitalized mothers achieved significantly lower IQ scores than the rest of the children on verbal, performance and full scale IQ. In multivariable linear regression duration of hospitalization, maternal depression and maternal IQ were significant predictors for these outcomes. ConclusionThis first prospective human study documented that HG is associated with an increased risk for lower cognitive outcome among HG- exposed offspring. More research is needed to examine whether early use of anti-emetics may prevent hospitalization, leading to favourable child neurodevelopment

Femoral Neuropathy in a Patient with Rheumatoid Arthritis

Femoral mononeuropathy (FMN) as an extraarticular finding of rheumatoid arthritis (RA) is a phenomenon which has not been reported previously. We report a 53-year-old female patient with RA, presenting FMN findings during the course of the disease. On examination, right quadriceps and iliopsoas muscles showed grade 3 weakness on the Medical Research Council (MRC) scale. Sensory examination revealed sensory loss in the right medial leg and thigh. Patellar tendon reflex was absent in the right side. A diagnosis of a partial right femoral neuropathy was confirmed using nerve conduction study and electromyography. The probable mechanism of FMN was thought to be vasculitis

ESO Imaging Survey. The Stellar Catalogue in the Chandra Deep Field South

(abridged) Stellar catalogues in five passbands (UBVRI) over an area of approximately 0.3 deg^2, comprising about 1200 objects, and in seven passbands (UBVRIJK) over approximately 0.1 deg^2, comprising about 400 objects, in the direction of the Chandra Deep Field South are presented. The 90% completeness level of the number counts is reached at approximately U = 23.8, B = 24.0, V = 23.5, R = 23.0, I = 21.0, J = 20.5, K = 19.0. A scheme is presented to select point sources from these catalogues, by combining the SExtractor parameter CLASS_STAR from all available passbands. Probable QSOs and unresolved galaxies are identified by using the previously developed \chi^2-technique (Hatziminaoglou et al 2002), that fits the overall spectral energy distributions to template spectra and determines the best fitting template. The observed number counts, colour-magnitude diagrams, colour-colour diagrams and colour distributions are presented and, to judge the quality of the data, compared to simulations based on the predictions of a Galactic Model convolved with the estimated completeness functions and the error model used to describe the photometric errors of the data. The resulting stellar catalogues and the objects identified as likely QSOs and unresolved galaxies with coordinates, observed magnitudes with errors and assigned spectral types by the $\chi^2$-technique are presented and are publicly available.Comment: Paper as it will appear in print. Complete figures and tables can be obtained from: http://www.eso.org/science/eis/eis_pub/eis_pub.html. Astronomy & Astrophysics, accepted for publicatio