Additional Exploratory Photoelastic Studies in Stress Wave Propagation

In a previous report to the sponsor, the design and description of a high speed framing camera was presented along with several film strips representing the results of a series of qualitative investigations of dynamic stress wave phenomena. These studies included crack propagation, layered media, compressed bars and beams, and cross sections of rocket heads. As part of a continuing study in these and related fields, a final report is submitted covering (1) exploratory experimental studies of shock wave propagation initiated by explosive caps and by nitrogen shock wave impingement, and (2) theoretical studies of a series of dynamic stress wave problems carried out in conjunction with the overall problem

Screening fungi isolated from historic Discovery Hut on Ross Island, Antarctica for cellulose degradation

To survive in Antarctica, early explorers of Antarctica's Heroic Age erected wooden buildings and brought in large quantities of supplies. The introduction of wood and other organic materials may have provided new nutrient sources for fungi that were indigenous to Antarctica or were brought in with the materials. From 30 samples taken from Discovery Hut, 156 filamentous fungi were isolated on selective media. Of these, 108 were screened for hydrolytic activity on carboxymethyl cellulose, of which 29 demonstrated activities. Endo-1, 4-β-glucanase activity was confirmed in the extracellular supernatant from seven isolates when grown at 4°C, and also when they were grown at 15°C. Cladosporium oxysporum and Geomyces sp. were shown to grow on a variety of synthetic cellulose substrates and to use cellulose as a nutrient source at temperate and cold temperatures. The research findings from the present study demonstrate that Antarctic filamentous fungi isolated from a variety of substrates (wood, straw, and food stuffs) are capable of cellulose degradation and can grow well at low temperatures

Improved Upper Limit on the Neutrino Mass from a Direct Kinematic Method by KATRIN.

We report on the neutrino mass measurement result from the first four-week science run of the Karlsruhe Tritium Neutrino experiment KATRIN in spring 2019. Beta-decay electrons from a high-purity gaseous molecular tritium source are energy analyzed by a high-resolution MAC-E filter. A fit of the integrated electron spectrum over a narrow interval around the kinematic end point at 18.57 keV gives an effective neutrino mass square value of (-1.0_{-1.1}^{+0.9})  eV^{2}. From this, we derive an upper limit of 1.1 eV (90% confidence level) on the absolute mass scale of neutrinos. This value coincides with the KATRIN sensitivity. It improves upon previous mass limits from kinematic measurements by almost a factor of 2 and provides model-independent input to cosmological studies of structure formation

Commissioning of the vacuum system of the KATRIN Main Spectrometer

The KATRIN experiment will probe the neutrino mass by measuring the beta-electron energy spectrum near the endpoint of tritium beta-decay. An integral energy analysis will be performed by an electro-static spectrometer (Main Spectrometer), an ultra-high vacuum vessel with a length of 23.2 m, a volume of 1240 m^3, and a complex inner electrode system with about 120000 individual parts. The strong magnetic field that guides the beta-electrons is provided by super-conducting solenoids at both ends of the spectrometer. Its influence on turbo-molecular pumps and vacuum gauges had to be considered. A system consisting of 6 turbo-molecular pumps and 3 km of non-evaporable getter strips has been deployed and was tested during the commissioning of the spectrometer. In this paper the configuration, the commissioning with bake-out at 300{\deg}C, and the performance of this system are presented in detail. The vacuum system has to maintain a pressure in the 10^{-11} mbar range. It is demonstrated that the performance of the system is already close to these stringent functional requirements for the KATRIN experiment, which will start at the end of 2016.Comment: submitted for publication in JINST, 39 pages, 15 figure

Trust-Region Variational Inference with Gaussian Mixture Models

Many methods for machine learning rely on approximate inference from intractable probability distributions. Variational inference approximates such distributions by tractable models that can be subsequently used for approximate inference. Learning sufficiently accurate approximations requires a rich model family and careful exploration of the relevant modes of the target distribution. We propose a method for learning accurate GMM approximations of intractable probability distributions based on insights from policy search by using information-geometric trust regions for principled exploration. For efficient improvement of the GMM approximation, we derive a lower bound on the corresponding optimization objective enabling us to update the components independently. Our use of the lower bound ensures convergence to a stationary point of the original objective. The number of components is adapted online by adding new components in promising regions and by deleting components with negligible weight. We demonstrate on several domains that we can learn approximations of complex, multimodal distributions with a quality that is unmet by previous variational inference methods, and that the GMM approximation can be used for drawing samples that are on par with samples created by state-of-theart MCMC samplers while requiring up to three orders of magnitude less computational resources