CLOTH3D: Clothed 3D Humans

This work presents CLOTH3D, the first big scale synthetic dataset of 3D clothed human sequences. CLOTH3D contains a large variability on garment type, topology, shape, size, tightness and fabric. Clothes are simulated on top of thousands of different pose sequences and body shapes, generating realistic cloth dynamics. We provide the dataset with a generative model for cloth generation. We propose a Conditional Variational Auto-Encoder (CVAE) based on graph convolutions (GCVAE) to learn garment latent spaces. This allows for realistic generation of 3D garments on top of SMPL model for any pose and shape

The Ekman-Hartmann layer in MHD Taylor-Couette flow

We study magnetic effects induced by rigidly rotating plates enclosing a cylindrical MHD Taylor-Couette flow at the finite aspect ratio $H/D=10$. The fluid confined between the cylinders is assumed to be liquid metal characterized by small magnetic Prandtl number, the cylinders are perfectly conducting, an axial magnetic field is imposed \Ha \approx 10, the rotation rates correspond to \Rey of order $10^2-10^3$. We show that the end-plates introduce, besides the well known Ekman circulation, similar magnetic effects which arise for infinite, rotating plates, horizontally unbounded by any walls. In particular there exists the Hartmann current which penetrates the fluid, turns into the radial direction and together with the applied magnetic field gives rise to a force. Consequently the flow can be compared with a Taylor-Dean flow driven by an azimuthal pressure gradient. We analyze stability of such flows and show that the currents induced by the plates can give rise to instability for the considered parameters. When designing an MHD Taylor-Couette experiment, a special care must be taken concerning the vertical magnetic boundaries so they do not significantly alter the rotational profile.Comment: 9 pages, 6 figures; accepted to PR

Deep Autoencoder for Combined Human Pose Estimation and body Model Upscaling

We present a method for simultaneously estimating 3D human pose and body shape from a sparse set of wide-baseline camera views. We train a symmetric convolutional autoencoder with a dual loss that enforces learning of a latent representation that encodes skeletal joint positions, and at the same time learns a deep representation of volumetric body shape. We harness the latter to up-scale input volumetric data by a factor of $4 \times$, whilst recovering a 3D estimate of joint positions with equal or greater accuracy than the state of the art. Inference runs in real-time (25 fps) and has the potential for passive human behaviour monitoring where there is a requirement for high fidelity estimation of human body shape and pose

Integral closure of rings of integer-valued polynomials on algebras

Let $D$ be an integrally closed domain with quotient field $K$. Let $A$ be a torsion-free $D$-algebra that is finitely generated as a $D$-module. For every $a$ in $A$ we consider its minimal polynomial $\mu_a(X)\in D[X]$, i.e. the monic polynomial of least degree such that $\mu_a(a)=0$. The ring ${\rm Int}_K(A)$ consists of polynomials in $K[X]$ that send elements of $A$ back to $A$ under evaluation. If $D$ has finite residue rings, we show that the integral closure of ${\rm Int}_K(A)$ is the ring of polynomials in $K[X]$ which map the roots in an algebraic closure of $K$ of all the $\mu_a(X)$, $a\in A$, into elements that are integral over $D$. The result is obtained by identifying $A$ with a $D$-subalgebra of the matrix algebra $M_n(K)$ for some $n$ and then considering polynomials which map a matrix to a matrix integral over $D$. We also obtain information about polynomially dense subsets of these rings of polynomials.Comment: Keywords: Integer-valued polynomial, matrix, triangular matrix, integral closure, pullback, polynomially dense set. accepted for publication in the volume "Commutative rings, integer-valued polynomials and polynomial functions", M. Fontana, S. Frisch and S. Glaz (editors), Springer 201

Radiography of the Earth's Core and Mantle with Atmospheric Neutrinos

A measurement of the absorption of neutrinos with energies in excess of 10 TeV when traversing the Earth is capable of revealing its density distribution. Unfortunately, the existence of beams with sufficient luminosity for the task has been ruled out by the AMANDA South Pole neutrino telescope. In this letter we point out that, with the advent of second-generation kilometer-scale neutrino detectors, the idea of studying the internal structure of the Earth may be revived using atmospheric neutrinos instead.Comment: 4 pages, LaTeX file using RevTEX4, 2 figures and 1 table included. Matches published versio

Linear-time inference for Gaussian Processes on one dimension

Gaussian Processes (GPs) provide powerful probabilistic frameworks for interpolation, forecasting, and smoothing, but have been hampered by computational scaling issues. Here we investigate data sampled on one dimension (e.g., a scalar or vector time series sampled at arbitrarily-spaced intervals), for which state-space models are popular due to their linearly-scaling computational costs. It has long been conjectured that state-space models are general, able to approximate any one-dimensional GP. We provide the first general proof of this conjecture, showing that any stationary GP on one dimension with vector-valued observations governed by a Lebesgue-integrable continuous kernel can be approximated to any desired precision using a specifically-chosen state-space model: the Latent Exponentially Generated (LEG) family. This new family offers several advantages compared to the general state-space model: it is always stable (no unbounded growth), the covariance can be computed in closed form, and its parameter space is unconstrained (allowing straightforward estimation via gradient descent). The theorem's proof also draws connections to Spectral Mixture Kernels, providing insight about this popular family of kernels. We develop parallelized algorithms for performing inference and learning in the LEG model, test the algorithm on real and synthetic data, and demonstrate scaling to datasets with billions of samples.Comment: Accepted to JML

Effect of Antimony and Cerium on the Formation of Chunky Graphite during Solidification of Heavy-Section Castings of Near-Eutectic Spheroidal Graphite Irons

Thermal analysis is applied to the study of the formation of chunky graphite (CHG) in heavysection castings of spheroidal graphite cast irons. To that aim, near-eutectic melts prepared in one single cast house were poured into molds containing up to four large cubic blocks 30 cm in size. Four melts have been prepared and cast that had a cerium content varying in relation with the spheroidizing alloy used. Postinoculation or addition of antimony was achieved by fixing appropriate amounts of materials in the gating system of each block. Cooling curves recorded in the center of the blocks show that solidification proceeds in three steps: a short primary deposition of graphite followed by an initial and then a bulk eutectic reaction. Formation of CHG could be unambiguously associated with increased recalescence during the bulk eutectic reaction. While antimony strongly decreases the amount of CHG, it appears that the ratio of the contents in antimony and cerium should be higher than 0.8 in order to avoid this graphite degeneracy

Linguistic and statistically derived features for cause of death prediction from verbal autopsy text

Automatic Text Classification (ATC) is an emerging technology with economic importance given the unprecedented growth of text data. This paper reports on work in progress to develop methods for predicting Cause of Death from Verbal Autopsy (VA) documents recommended for use in low-income countries by the World Health Organisation. VA documents contain both coded data and open narrative. The task is formulated as a Text Classification problem and explores various combinations of linguistic and statistical approaches to determine how these may improve on the standard bag-of-words approach using a dataset of over 6400 VA documents that were manually annotated with cause of death. We demonstrate that a significant improvement of prediction accuracy can be obtained through a novel combination of statistical and linguistic features derived from the VA text. The paper explores the methods by which ATC may leads to improved accuracy in Cause of Death prediction