Abstract. We discuss the calculation of the leading hadronic vacuum polarization in lattice QCD. Exploiting the excellent quality of the compiled experimental data for the e + e − → hadrons cross-section, we predict the outcome of large-volume lattice calculations at the physical pion mass, and design computational strategies for the lattice to have an impact on important phenomenological quantities such as the leading hadronic contribution to (g − 2)µ and the running of the electromagnetic coupling constant. First, the R(s) ratio can be calculated directly on the lattice in the threshold region, and we provide the formulae to do so with twisted boundary conditions. Second, the current correlator projected onto zero spatial momentum, in a Euclidean time interval where it can be calculated accurately, provides a potentially critical test of the experimental R(s) ratio in the region that is most relevant for (g − 2)µ. This observation can also be turned around: the vector correlator at intermediate distances can be used to determine the lattice spacing in fm, and we make a concrete proposal in this direction. Finally, we quantify the finite-size effects on the current correlator coming from low-energy two-pion states and provide a general parametrization of the vacuum polarization on the torus.

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Proper cloud segmentation can serve as an important precursor to predicting the output of solar power plants. However, due to the high variability of cloud appearance, and the high dynamic range between different sky regions, cloud segmentation is a surprisingly difficult task. In this paper, we present an approach to cloud segmentation and classification that is based on representation learning. Texture primitives of cloud regions are represented within a restricted Boltzmann Machine. Quantitative results are encouraging. Experimental results yield a relative improvement of the unweighted average (pixelwise) precision on a three-class problem by 11% to 94% in comparison to prior work.

Abstract. Feature point tracking and detection of X-ray images is challenging due to overlapping anatomical structures of different depths, which lead to low-contrast images. Tracking of motion in X-ray sequences can support many clinical applications like motion compensation or 2D / 3D registration algorithms. This paper is the first to evaluate the performance of several feature tracking and detection algorithms on artificial and real X-ray image sequences, which involve rigid motion as well as external disturbances. A stand-alone application has been developed to provide an overall test bench for all algorithms, realized by OpenCV implementations. Experiments show that the Kanade-Lucas-Tomasi tracker is the most consistent and effective tracking algorithm. Considering external disturbances, template matching provides the most sufficient results. Furthermore, the influence of feature point detection methods on tracking results is shown.