Search Results

You are looking at 1 - 3 of 3 items for

  • Author: Thomas Riedel x
Clear All Modify Search
Open access

Nicole Wellbrock, Erik Grüneberg, Thomas Riedel and Heino Polley

Abstract

Close to one third of Germany is forested. Forests are able to store significant quantities of carbon (C) in the biomass and in the soil. Coordinated by the Thünen Institute, the German National Forest Inventory (NFI) and the National Forest Soil Inventory (NFSI) have generated data to estimate the carbon storage capacity of forests. The second NFI started in 2002 and had been repeated in 2012. The reporting time for the NFSI was 1990 to 2006. Living forest biomass, deadwood, litter and soils up to a depth of 90 cm have stored 2500 t of carbon within the reporting time. Over all 224 t C ha-1 in aboveground and belowground biomass, deadwood and soil are stored in forests. Specifically, 46% stored in above-ground and below-ground biomass, 1% in dead wood and 53% in the organic layer together with soil up to 90 cm. Carbon stocks in mineral soils up to 30 cm mineral soil increase about 0.4 t C ha-1 yr-1 stocks between the inventories while the carbon pool in the organic layers declined slightly. In the living biomass carbon stocks increased about 1.0 t C ha-1 yr-1. In Germany, approximately 58 mill. tonnes of CO2 were sequestered in 2012 (NIR 2017).

Open access

M. Kaden, M. Lange, D. Nebel, M. Riedel, T. Geweniger and T. Villmann

Abstract

.Classification is one of the most frequent tasks in machine learning. However, the variety of classification tasks as well as classifier methods is huge. Thus the question is coming up: which classifier is suitable for a given problem or how can we utilize a certain classifier model for different tasks in classification learning. This paper focuses on learning vector quantization classifiers as one of the most intuitive prototype based classification models. Recent extensions and modifications of the basic learning vector quantization algorithm, which are proposed in the last years, are highlighted and also discussed in relation to particular classification task scenarios like imbalanced and/or incomplete data, prior data knowledge, classification guarantees or adaptive data metrics for optimal classification.

Open access

Tomasz Zok, Maciej Antczak, Martin Riedel, David Nebel, Thomas Villmann, Piotr Lukasiak, Jacek Blazewicz and Marta Szachniuk

Abstract

An increasing number of known RNA 3D structures contributes to the recognition of various RNA families and identification of their features. These tasks are based on an analysis of RNA conformations conducted at different levels of detail. On the other hand, the knowledge of native nucleotide conformations is crucial for structure prediction and understanding of RNA folding. However, this knowledge is stored in structural databases in a rather distributed form. Therefore, only automated methods for sampling the space of RNA structures can reveal plausible conformational representatives useful for further analysis. Here, we present a machine learning-based approach to inspect the dataset of RNA three-dimensional structures and to create a library of nucleotide conformers. A median neural gas algorithm is applied to cluster nucleotide structures upon their trigonometric description. The clustering procedure is two-stage: (i) backbone- and (ii) ribose-driven. We show the resulting library that contains RNA nucleotide representatives over the entire data, and we evaluate its quality by computing normal distribution measures and average RMSD between data points as well as the prototype within each cluster.