AdS Black Holes from Duality in Gauged Supergravity

We study and utilize duality transformations in a particular STU-model of four dimensional gauged supergravity. This model is a truncation of the de Wit-Nicolai N=8 theory and as such has a lift to eleven-dimensional supergravity on the seven-sphere. Our duality group is $U(1)^3$ and while it can be applied to any solution of this theory, we consider the known asymptotically AdS$_4$, supersymmetric black holes and focus on duality transformations which preserve supersymmetry. For static black holes we generalize the supersymmetric solutions of Cacciatori and Klemm from three magnetic charges to include two additional electric charges and argue that this is co-dimension one in the full space of supersymmetric static black holes in the STU-model. These new static black holes have nontrivial profiles for axions. For rotating black holes, we generalize the known two-parameter supersymmetric solution to include an additional parameter which represents scalar hair. When lifted to M-theory, these black holes correspond to the near horizon geometry of a stack of BPS rotating M2-branes, spinning on an $S^7$ which is fibered non-trivially over a Riemann surface.Comment: 21 page

Oat variety characteristics for suppressing weeds

Oats are a valuable food source and useful in the crop rotation both in organic and conventional farming systems, partly because of their excellent weed suppression ability. Thomas Döring, Louisa Winkler and Nick Fradgley report new results that show how plant breeding can make oats even better

3D finite element modeling of edge and width drop behavior in hot rolling mill

Hot rough rolling is a conventional forming process in modern steelmaking practice in which high deformations are applied to a steel slab at high temperatures. Due to the sequence of edge rolling followed by rough rolling, so-called edge and width drop phenomena are observed at the head and tail of the slab. These unwanted effects govern a yield loss and need to be minimized as much as possible. By means of a finite element study this research aims to discover the main influencing parameters on the observed edge and width drop behavior. An overview and comparison of the relative contributions of several edge rolling settings are presented. The net edger roll opening is the most important influencing parameter on edge and width drop behavior. The effect of width and thickness of the slab on the edge drop is less strongly pronounced; only the thickness influences the width drop behavior.</jats:p

Sparsity Invariant CNNs

In this paper, we consider convolutional neural networks operating on sparse inputs with an application to depth upsampling from sparse laser scan data. First, we show that traditional convolutional networks perform poorly when applied to sparse data even when the location of missing data is provided to the network. To overcome this problem, we propose a simple yet effective sparse convolution layer which explicitly considers the location of missing data during the convolution operation. We demonstrate the benefits of the proposed network architecture in synthetic and real experiments with respect to various baseline approaches. Compared to dense baselines, the proposed sparse convolution network generalizes well to novel datasets and is invariant to the level of sparsity in the data. For our evaluation, we derive a novel dataset from the KITTI benchmark, comprising 93k depth annotated RGB images. Our dataset allows for training and evaluating depth upsampling and depth prediction techniques in challenging real-world settings and will be made available upon publication

Physics-informed Neural Networks for Solving Inverse Problems of Nonlinear Biot's Equations: Batch Training

In biomedical engineering, earthquake prediction, and underground energy harvesting, it is crucial to indirectly estimate the physical properties of porous media since the direct measurement of those are usually impractical/prohibitive. Here we apply the physics-informed neural networks to solve the inverse problem with regard to the nonlinear Biot's equations. Specifically, we consider batch training and explore the effect of different batch sizes. The results show that training with small batch sizes, i.e., a few examples per batch, provides better approximations (lower percentage error) of the physical parameters than using large batches or the full batch. The increased accuracy of the physical parameters, comes at the cost of longer training time. Specifically, we find the size should not be too small since a very small batch size requires a very long training time without a corresponding improvement in estimation accuracy. We find that a batch size of 8 or 32 is a good compromise, which is also robust to additive noise in the data. The learning rate also plays an important role and should be used as a hyperparameter.Comment: arXiv admin note: text overlap with arXiv:2002.0823