1,244 research outputs found

    Collapse of an Instanton

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    We construct a two parameter family of collapsing solutions to the 4+1 Yang-Mills equations and derive the dynamical law of the collapse. Our arguments indicate that this family of solutions is stable. The latter fact is also supported by numerical simulations.Comment: 17 pages, 1 figur

    Resonances Width in Crossed Electric and Magnetic Fields

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    We study the spectral properties of a charged particle confined to a two-dimensional plane and submitted to homogeneous magnetic and electric fields and an impurity potential. We use the method of complex translations to prove that the life-times of resonances induced by the presence of electric field are at least Gaussian long as the electric field tends to zero.Comment: 3 figure

    Keep it SMPL: Automatic Estimation of 3D Human Pose and Shape from a Single Image

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    We describe the first method to automatically estimate the 3D pose of the human body as well as its 3D shape from a single unconstrained image. We estimate a full 3D mesh and show that 2D joints alone carry a surprising amount of information about body shape. The problem is challenging because of the complexity of the human body, articulation, occlusion, clothing, lighting, and the inherent ambiguity in inferring 3D from 2D. To solve this, we first use a recently published CNN-based method, DeepCut, to predict (bottom-up) the 2D body joint locations. We then fit (top-down) a recently published statistical body shape model, called SMPL, to the 2D joints. We do so by minimizing an objective function that penalizes the error between the projected 3D model joints and detected 2D joints. Because SMPL captures correlations in human shape across the population, we are able to robustly fit it to very little data. We further leverage the 3D model to prevent solutions that cause interpenetration. We evaluate our method, SMPLify, on the Leeds Sports, HumanEva, and Human3.6M datasets, showing superior pose accuracy with respect to the state of the art.Comment: To appear in ECCV 201

    Short-term mucosal disruption enables colibactin-producing E. coli to cause long-term perturbation of colonic homeostasis

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    Colibactin, a bacterial genotoxin produced by E. coli strains harboring the pks genomic island, induces cytopathic effects, such as DNA breaks, cell cycle arrest, and apoptosis. Patients with inflammatory bowel diseases, such as ulcerative colitis, display changes in their microbiota with the expansion of E. coli. Whether and how colibactin affects the integrity of the colonic mucosa and whether pks+ E. coli contributes to the pathogenesis of colitis is not clear. Using a gnotobiotic mouse model, we show that under homeostatic conditions, pks+ E. coli do not directly interact with the epithelium or affect colonic integrity. However, upon short-term chemical disruption of mucosal integrity, pks+ E. coli gain direct access to the epithelium, causing epithelial injury and chronic colitis, while mice colonized with an isogenic ΔclbR mutant incapable of producing colibactin show a rapid recovery. pks+ E. coli colonized mice are unable to reestablish a functional barrier. In turn, pks+ E. coli remains in direct contact with the epithelium, perpetuating the process and triggering chronic mucosal inflammation that morphologically and transcriptionally resembles human ulcerative colitis. This state is characterized by impaired epithelial differentiation and high proliferative activity, which is associated with high levels of stromal R-spondin 3. Genetic overexpression of R-spondin 3 in colon myofibroblasts is sufficient to mimic barrier disruption and expansion of E. coli. Together, our data reveal that pks+ E. coli are pathobionts that promote severe injury and initiate a proinflammatory trajectory upon contact with the colonic epithelium, resulting in a chronic impairment of tissue integrity

    High-Dimensional Feature Selection by Feature-Wise Kernelized Lasso

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    The goal of supervised feature selection is to find a subset of input features that are responsible for predicting output values. The least absolute shrinkage and selection operator (Lasso) allows computationally efficient feature selection based on linear dependency between input features and output values. In this paper, we consider a feature-wise kernelized Lasso for capturing non-linear input-output dependency. We first show that, with particular choices of kernel functions, non-redundant features with strong statistical dependence on output values can be found in terms of kernel-based independence measures. We then show that the globally optimal solution can be efficiently computed; this makes the approach scalable to high-dimensional problems. The effectiveness of the proposed method is demonstrated through feature selection experiments with thousands of features.Comment: 18 page

    Monocular Expressive Body Regression through Body-Driven Attention

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    To understand how people look, interact, or perform tasks, we need to quickly and accurately capture their 3D body, face, and hands together from an RGB image. Most existing methods focus only on parts of the body. A few recent approaches reconstruct full expressive 3D humans from images using 3D body models that include the face and hands. These methods are optimization-based and thus slow, prone to local optima, and require 2D keypoints as input. We address these limitations by introducing ExPose (EXpressive POse and Shape rEgression), which directly regresses the body, face, and hands, in SMPL-X format, from an RGB image. This is a hard problem due to the high dimensionality of the body and the lack of expressive training data. Additionally, hands and faces are much smaller than the body, occupying very few image pixels. This makes hand and face estimation hard when body images are downscaled for neural networks. We make three main contributions. First, we account for the lack of training data by curating a dataset of SMPL-X fits on in-the-wild images. Second, we observe that body estimation localizes the face and hands reasonably well. We introduce body-driven attention for face and hand regions in the original image to extract higher-resolution crops that are fed to dedicated refinement modules. Third, these modules exploit part-specific knowledge from existing face- and hand-only datasets. ExPose estimates expressive 3D humans more accurately than existing optimization methods at a small fraction of the computational cost. Our data, model and code are available for research at https://expose.is.tue.mpg.de .Comment: Accepted in ECCV'20. Project page: http://expose.is.tue.mpg.d

    Local energy decay of massive Dirac fields in the 5D Myers-Perry metric

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    We consider massive Dirac fields evolving in the exterior region of a 5-dimensional Myers-Perry black hole and study their propagation properties. Our main result states that the local energy of such fields decays in a weak sense at late times. We obtain this result in two steps: first, using the separability of the Dirac equation, we prove the absence of a pure point spectrum for the corresponding Dirac operator; second, using a new form of the equation adapted to the local rotations of the black hole, we show by a Mourre theory argument that the spectrum is absolutely continuous. This leads directly to our main result.Comment: 40 page
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