Fully Convolutional Neural Networks for Dynamic Object Detection in Grid Maps

Grid maps are widely used in robotics to represent obstacles in the environment and differentiating dynamic objects from static infrastructure is essential for many practical applications. In this work, we present a methods that uses a deep convolutional neural network (CNN) to infer whether grid cells are covering a moving object or not. Compared to tracking approaches, that use e.g. a particle filter to estimate grid cell velocities and then make a decision for individual grid cells based on this estimate, our approach uses the entire grid map as input image for a CNN that inspects a larger area around each cell and thus takes the structural appearance in the grid map into account to make a decision. Compared to our reference method, our concept yields a performance increase from 83.9% to 97.2%. A runtime optimized version of our approach yields similar improvements with an execution time of just 10 milliseconds.Comment: This is a shorter version of the masters thesis of Florian Piewak and it was accapted at IV 201

An evaluation of German active labor market policies and its entrepreneurship promotion

This paper reviews the results of studies that investigate the most important active labour market policy (ALMP) measures in Germany. A particular focus is on programs devoted to foster entrepreneurship which can make important contributions to a country's growth and social welfare. The available evidence suggests that most ALMP measures increase labour market prospects of the participants. Evaluations of the entrepreneurship promotion activities show high success rates as well as high cost efficiency. The bulk share of participants of entrepreneurship measures is still self-employed after several years and nearly one third of these businesses had at least one employee. We mention problems regarding the evaluation of previous programs and highlight future challenges of German ALMP

Impact, Attention, Influence: Early Assessment of Autonomous Driving Datasets

Autonomous Driving (AD), the area of robotics with the greatest potential impact on society, has gained a lot of momentum in the last decade. As a result of this, the number of datasets in AD has increased rapidly. Creators and users of datasets can benefit from a better understanding of developments in the field. While scientometric analysis has been conducted in other fields, it rarely revolves around datasets. Thus, the impact, attention, and influence of datasets on autonomous driving remains a rarely investigated field. In this work, we provide a scientometric analysis for over 200 datasets in AD. We perform a rigorous evaluation of relations between available metadata and citation counts based on linear regression. Subsequently, we propose an Influence Score to assess a dataset already early on without the need for a track-record of citations, which is only available with a certain delay.Comment: Daniel Bogdoll and Jonas Hendl contributed equally. Accepted for publication at ICCRE 202

Ocean acidification affects iron speciation during a coastal seawater mesocosm experiment

Rising atmospheric CO2 is acidifying the surface ocean, a process which is expected to greatly influence the chemistry and biology of the future ocean. Following the development of iron-replete phytoplankton blooms in a coastal mesocosm experiment at 350, 700, and 1050 μatm pCO2, we observed significant increases in dissolved iron concentrations, Fe(II) concentrations, and Fe(II) half-life times during and after the peak of blooms in response to CO2 enrichment and concomitant lowering of pH, suggesting increased iron bioavailability. If applicable to the open ocean this may provide a negative feedback mechanism to the rising atmospheric CO2 by stimulating marine primary production

Availability of phosphate for phytoplankton and bacteria and of labile organic carbon for bacteria at different pCO2 levels in a mesocosm study

Availability of phosphate for phytoplankton and bacteria and of glucose for bacteria at different pCO2 levels were studied in a mesocosm experiment (PeECE III). Using nutrient-depleted SW Norwegian fjord waters, three different levels of pCO2 (350 μatm: 1×CO2; 700 μatm: 2×CO2; 1050 μatm: 3×CO2) were set up, and nitrate and phosphate were added at the start of the experiment in order to induce a phytoplankton bloom. Despite similar responses of total particulate P concentration and phosphate turnover time at the three different pCO2 levels, the size distribution of particulate P and 33PO4 uptake suggested that phosphate transferred to the >10 μm fraction was greater in the 3×CO2 mesocosm during the first 6–10 days when phosphate concentration was high. During the period of phosphate depletion (after Day 12), specific phosphate affinity and specific alkaline phosphatase activity (APA) suggested a P-deficiency (i.e. suboptimal phosphate supply) rather than a P-limitation for the phytoplankton and bacterial community at the three different pCO2 levels. Specific phosphate affinity and specific APA tended to be higher in the 3×CO2 than in the 2×CO2 and 1×CO2 mesocosms during the phosphate depletion period, although no statistical differences were found. Glucose turnover time was correlated significantly and negatively with bacterial abundance and production but not with the bulk DOC concentration. This suggests that even though constituting a small fraction of the bulk DOC, glucose was an important component of labile DOC for bacteria. Specific glucose affinity of bacteria behaved similarly at the three different pCO2 levels with measured specific glucose affinities being consistently much lower than the theoretical maximum predicted from the diffusion-limited model. This suggests that bacterial growth was not severely limited by the glucose availability. Hence, it seems that the lower availability of inorganic nutrients after the phytoplankton bloom reduced the bacterial capacity to consume labile DOC in the upper mixed layer of the stratified mesocosms

Endothelin potentiates TRPV1 via ETA receptor-mediated activation of protein kinase C

© 2007 Plant et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution Licens

Build-up and decline of organic matter during PeECE III

Increasing atmospheric carbon dioxide (CO2) concentrations due to anthropogenic fossil fuel combustion are currently changing the ocean's chemistry. Increasing oceanic [CO2] and consequently decreasing seawater pH have the potential to significantly impact marine life. Here we describe and analyze the build-up and decline of a natural phytoplankton bloom initiated during the 2005 mesocosm Pelagic Ecosystem CO2 Enrichment study (PeECE III). The draw-down of inorganic nutrients in the upper surface layer of the mesocosms was reflected by a concomitant increase of organic matter until day t11, the peak of the bloom. From then on, biomass standing stocks steadily decreased as more and more particulate organic matter was lost into the deeper layer of the mesocosms. We show that organic carbon export to the deeper layer was significantly enhanced at elevated CO2. This phenomenon might have impacted organic matter remineralization leading to decreased oxygen concentrations in the deeper layer of the high CO2 mesocosms as indicated by deep water ammonium concentrations. This would have important implications for our understanding of pelagic ecosystem functioning and future carbon cycling

Benefits from using mixed precision computations in the ELPA-AEO and ESSEX-II eigensolver projects

We first briefly report on the status and recent achievements of the ELPA-AEO (Eigenvalue Solvers for Petaflop Applications - Algorithmic Extensions and Optimizations) and ESSEX II (Equipping Sparse Solvers for Exascale) projects. In both collaboratory efforts, scientists from the application areas, mathematicians, and computer scientists work together to develop and make available efficient highly parallel methods for the solution of eigenvalue problems. Then we focus on a topic addressed in both projects, the use of mixed precision computations to enhance efficiency. We give a more detailed description of our approaches for benefiting from either lower or higher precision in three selected contexts and of the results thus obtained