Neural Nearest Neighbors Networks

Non-local methods exploiting the self-similarity of natural signals have been well studied, for example in image analysis and restoration. Existing approaches, however, rely on k-nearest neighbors (KNN) matching in a fixed feature space. The main hurdle in optimizing this feature space w.r.t. application performance is the non-differentiability of the KNN selection rule. To overcome this, we propose a continuous deterministic relaxation of KNN selection that maintains differentiability w.r.t. pairwise distances, but retains the original KNN as the limit of a temperature parameter approaching zero. To exploit our relaxation, we propose the neural nearest neighbors block (N3 block), a novel non-local processing layer that leverages the principle of self-similarity and can be used as building block in modern neural network architectures. We show its effectiveness for the set reasoning task of correspondence classification as well as for image restoration, including image denoising and single image super-resolution, where we outperform strong convolutional neural network (CNN) baselines and recent non-local models that rely on KNN selection in hand-chosen features spaces.Comment: to appear at NIPS*2018, code available at https://github.com/visinf/n3net

Tree of Life Synagogue Shooting in Pittsburgh: Preparedness, Prehospital Care, and Lessons Learned

On Saturday, October 27, 2018, a man with anti-Semitic motivations entered Tree of Life synagogue in the Squirrel Hill section of Pittsburgh, Pennsylvania; he had an AR-15 semi-automatic rifle and three handguns, opening fire upon worshippers. Eventually 11 civilians died at the scene and eight people sustained non-fatal injuries, including five police officers. Each person injured but alive at the scene received care at one of three local level-one trauma centers.  The injured had wounds often seen in war-settings, with the signature of high velocity weaponry.  We describe the scene response, specific elements of our hospital plans, the overall out-of-hospital preparedness in Pittsburgh, and the lessons learned

Optimal minimum wages

We develop a quantitative spatial model with heterogeneous firms and a monopsonistic labour market to derive minimum wages that maximize employment or welfare. Quantifying the model for German micro regions, we find that the German minimum wage, set at 48% of the national mean wage, has increased aggregate worker welfare by about 2.1% at the cost or reducing employment by about 0.3%. The welfare-maximizing federal minimum wage, at 60% of the national mean wage, would increase aggregate worker welfare by 4%, but reduce employment by 5.6%. An employment-maximizing regional wage, set at 50% of the regional mean wage, would achieve a similar aggregate welfare effect and increase employment by 1.1%

The regional effects of Germany’s national minimum wage

We show that the minimum wage introduced in Germany in 2015 led to spatial wage convergence, in particular in the left tail of the distribution, without reducing relative employment in low-wage regions within the first two years