Technical University of Berlin

About: Technical University of Berlin is a education organization based out in Berlin, Germany. It is known for research contribution in the topics: Quantum dot & Laser. The organization has 27292 authors who have published 59342 publications receiving 1414623 citations. The organization is also known as: Technische Universität Berlin & TU Berlin.

Transport inequalities, gradient estimates, entropy and Ricci curvature

Abstract: We present various characterizations of uniform lower bounds for the Ricci curvature of a smooth Riemannian manifold M in terms of convexity properties of the entropy (considered as a function on the space of probability measures on M) as well as in terms of transportation inequalities for volume measures, heat kernels, and Brownian motions and in terms of gradient estimates for the heat semigroup. © 2004 Wiley Periodicals, Inc.

Manganese Compounds as Water-Oxidizing Catalysts: From the Natural Water-Oxidizing Complex to Nanosized Manganese Oxide Structures

Abstract: All cyanobacteria, algae, and plants use a similar water-oxidizing catalyst for water oxidation. This catalyst is housed in Photosystem II, a membrane-protein complex that functions as a light-driven water oxidase in oxygenic photosynthesis. Water oxidation is also an important reaction in artificial photosynthesis because it has the potential to provide cheap electrons from water for hydrogen production or for the reduction of carbon dioxide on an industrial scale. The water-oxidizing complex of Photosystem II is a Mn–Ca cluster that oxidizes water with a low overpotential and high turnover frequency number of up to 25–90 molecules of O2 released per second. In this Review, we discuss the atomic structure of the Mn–Ca cluster of the Photosystem II water-oxidizing complex from the viewpoint that the underlying mechanism can be informative when designing artificial water-oxidizing catalysts. This is followed by consideration of functional Mn-based model complexes for water oxidation and the issue of Mn com...

InGaAs-GaAs quantum-dot lasers

Abstract: Quantum-dot (QD) lasers provide superior lasing characteristics compared to quantum-well (QW) and QW wire lasers due to their delta like density of states. Record threshold current densities of 40 A/spl middot/cm/sup -2/ at 77 K and of 62 A/spl middot/cm/sup -2/ at 300 K are obtained while a characteristic temperature of 385 K is maintained up to 300 K. The internal quantum efficiency approaches values of /spl sim/80 %. Currently, operating QD lasers show broad-gain spectra with full-width at half-maximum (FWHM) up to /spl sim/50 meV, ultrahigh material gain of /spl sim/10/sup 5/ cm/sup -1/, differential gain of /spl sim/10/sup -13/ cm/sup 2/ and strong nonlinear gain effects with a gain compression coefficient of /spl sim/10/sup -16/ cm/sup 3/. The modulation bandwidth is limited by nonlinear gain effects but can be increased by careful choice of the energy difference between QD and barrier states. The linewidth enhancement factor is /spl sim/0.5. The InGaAs-GaAs QD emission can be tuned between 0.95 /spl mu/m and 1.37 /spl mu/m at 300 K.

CayleyNets: Graph Convolutional Neural Networks With Complex Rational Spectral Filters

Abstract: The rise of graph-structured data such as social networks, regulatory networks, citation graphs, and functional brain networks, in combination with resounding success of deep learning in various applications, has brought the interest in generalizing deep learning models to non-Euclidean domains. In this paper, we introduce a new spectral domain convolutional architecture for deep learning on graphs. The core ingredient of our model is a new class of parametric rational complex functions (Cayley polynomials) allowing to efficiently compute spectral filters on graphs that specialize on frequency bands of interest. Our model generates rich spectral filters that are localized in space, scales linearly with the size of the input data for sparsely connected graphs, and can handle different constructions of Laplacian operators. Extensive experimental results show the superior performance of our approach, in comparison to other spectral domain convolutional architectures, on spectral image classification, community detection, vertex classification, and matrix completion tasks.