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

Recoiling Supermassive Black Hole Escape Velocities from Dark Matter Halos

We simulate recoiling black hole trajectories from $z=20$ to $z=0$ in dark matter halos, quantifying how parameter choices affect escape velocities. These choices include the strength of dynamical friction, the presence of stars and gas, the accelerating expansion of the universe (Hubble acceleration), host halo accretion and motion, and seed black hole mass. $\Lambda$CDM halo accretion increases escape velocities by up to 0.6 dex and significantly shortens return timescales compared to non-accreting cases. Other parameters change orbit damping rates but have subdominant effects on escape velocities; dynamical friction is weak at halo escape velocities, even for extreme parameter values. We present formulae for black hole escape velocities as a function of host halo mass and redshift. Finally, we discuss how these findings affect black hole mass assembly as well as minimum stellar and halo masses necessary to retain supermassive black holes.Comment: 10 pages, 17 figures. Updated to correct a typo (sign error) in fit to escape velocity, for return by z=0 (eq. 19

Tradable Permits

Tradable Permits – a Market-Based Allocation System for the Environment. Tradable Permits and Other Environmental Policy Instruments – Killing one Bird with two Stones. Tradable Permits – Ten Key Design Issues. Tradable Permits with Imperfect Monitoring. Emissions Trading with Greenhouse Gases in the European Union.Umweltzertifikat, Umweltökonomik, Immissionsschutz, Umweltpolitik, Klimaschutz, EU-Umweltpolitik, Wirtschaftspolitische Wirkungsanalyse, EU-Staaten, Vereinigte Staaten, Environmental economics, Emission control, Environmental policy, Climate protection, EU environmental policy, Economic policy analysis, EU countries, United States

Shrinkage Stresses Generated during Resin-Composite Applications: A Review

Many developments have been made in the field of resin composites for dental applications. However, the manifestation of shrinkage due to the polymerization process continues to be a major problem. The material's shrinkage, associated with dynamic development of elastic modulus, creates stresses within the material and its interface with the tooth structure. As a consequence, marginal failure and subsequent secondary caries, marginal staining, restoration displacement, tooth fracture, and/or post-operative sensitivity are clinical drawbacks of resin-composite applications. The aim of the current paper is to present an overview about the shrinkage stresses created during resin-composite applications, consequences, and advances. The paper is based on results of many researches that are available in the literature