Flare Hybrids

Svestka (Solar Phys. 1989, 121, 399) on the basis of the Solar Maximum Mission observations introduced a new class of flares, the so-called flare hybrids. When they start, they look as typical compact flares (phase 1), but later on they look like flares with arcades of magnetic loops (phase 2). We summarize the features of flare hybrids in soft and hard X-rays as well as in extreme-ultraviolet; these allow us to distinguish them from other flares. Additional energy release or long plasma cooling timescales have been suggested as possible cause of phase 2. Estimations of frequency of flare hybrids have been given. Magnetic configurations supporting their origin have been presented. In our opinion, flare hybrids are quite frequent and a difference between lengths of two interacting systems of magnetic loops is a crucial parameter for recognizing their features.Comment: 15 pages, 4 figures, to appear in Solar Physic

Materials Design using Correlated Oxides: Optical Properties of Vanadium Dioxide

Materials with strong electronic Coulomb interactions play an increasing role in modern materials applications. "Thermochromic" systems, which exhibit thermally induced changes in their optical response, provide a particularly interesting case. The optical switching associated with the metal-insulator transition of vanadium dioxide (VO2), for example, has been proposed for use in "intelligent" windows, which selectively filter radiative heat in hot weather conditions. In this work, we develop the theoretical tools for describing such a behavior. Using a novel scheme for the calculation of the optical conductivity of correlated materials, we obtain quantitative agreement with experiments for both phases of VO2. On the example of an optimized energy-saving window setup, we further demonstrate that theoretical materials design has now come into reach, even for the particularly challenging class of correlated electron systems.Comment: 4+x pages, 2 figure

VAE with a VampPrior

Many different methods to train deep generative models have been introduced in the past. In this paper, we propose to extend the variational auto-encoder (VAE) framework with a new type of prior which we call "Variational Mixture of Posteriors" prior, or VampPrior for short. The VampPrior consists of a mixture distribution (e.g., a mixture of Gaussians) with components given by variational posteriors conditioned on learnable pseudo-inputs. We further extend this prior to a two layer hierarchical model and show that this architecture with a coupled prior and posterior, learns significantly better models. The model also avoids the usual local optima issues related to useless latent dimensions that plague VAEs. We provide empirical studies on six datasets, namely, static and binary MNIST, OMNIGLOT, Caltech 101 Silhouettes, Frey Faces and Histopathology patches, and show that applying the hierarchical VampPrior delivers state-of-the-art results on all datasets in the unsupervised permutation invariant setting and the best results or comparable to SOTA methods for the approach with convolutional networks.Comment: 16 pages, final version, AISTATS 201