University of Mainz

About: University of Mainz is a education organization based out in Mainz, Rheinland-Pfalz, Germany. It is known for research contribution in the topics: Population & Immune system. The organization has 37673 authors who have published 71163 publications receiving 2497880 citations. The organization is also known as: Johannes Gutenberg-Universität Mainz & Universität Mainz.

Cooperative Formation of Inorganic-Organic Interfaces in the Synthesis of Silicate Mesostructures

Abstract: A model is presented to explain the formation and morphologies of surfactant-silicate mesostructures. Three processes are identified: multidentate binding of silicate oligomers to the cationic surfactant, preferential silicate polymerization in the interface region, and charge density matching between the surfactant and the silicate. The model explains present experimental data, including the transformation between lamellar and hexagonal mesophases, and provides a guide for predicting conditions that favor the formation of lamellar, hexagonal, or cubic mesostructures. Model Q(230) proposed by Mariani and his co-workers satisfactorily fits the x-ray data collected on the cubic mesostructure material. This model suggests that the silicate polymer forms a unique infinite silicate sheet sitting on the gyroid minimal surface and separating the surfactant molecules into two disconnected volumes.

Precomputed Real-Time Texture Synthesis with Markovian Generative Adversarial Networks

Abstract: This paper proposes Markovian Generative Adversarial Networks (MGANs), a method for training generative networks for efficient texture synthesis. While deep neural network approaches have recently demonstrated remarkable results in terms of synthesis quality, they still come at considerable computational costs (minutes of run-time for low-res images). Our paper addresses this efficiency issue. Instead of a numerical deconvolution in previous work, we precompute a feed-forward, strided convolutional network that captures the feature statistics of Markovian patches and is able to directly generate outputs of arbitrary dimensions. Such network can directly decode brown noise to realistic texture, or photos to artistic paintings. With adversarial training, we obtain quality comparable to recent neural texture synthesis methods. As no optimization is required at generation time, our run-time performance (0.25 M pixel images at 25 Hz) surpasses previous neural texture synthesizers by a significant margin (at least 500 times faster). We apply this idea to texture synthesis, style transfer, and video stylization.