Vienna University of Technology

About: Vienna University of Technology is a education organization based out in Vienna, Austria. It is known for research contribution in the topics: Laser & Context (language use). The organization has 16723 authors who have published 49341 publications receiving 1302168 citations.

Quantum dynamics of impurities in a one-dimensional Bose gas

Abstract: Using a species-selective dipole potential, we create initially localized impurities and investigate their interactions with a majority species of bosonic atoms in a one-dimensional configuration during expansion. We find an interaction-dependent amplitude reduction of the oscillation of the impurities' size with no measurable frequency shift, and study it as a function of the interaction strength. We discuss possible theoretical interpretations of the data. We compare, in particular, with a polaronic mass shift model derived following Feynman variational approach.

robCompositions: An R-package for Robust Statistical Analysis of Compositional Data

Abstract: Compositional data are data that contain only relative information (see, e.g. Aitchison 1986)). Typical examples are data describing expenditures of persons on certain goods, or environmental data like the concentration of chemical elements in the soil. If all the compositional parts would be available, they would sum up to a total, like 100case of geochemical concentrations. Frequently, practical data sets include outliers, and thus a robust analysis is desirable. The R-package robCompositions (Templ et al., 2009) contains functions for robust statistical methods designed for compositional data, like principal component analysis (Filzmoser et al., 2009a) (including the robust compositional biplot), factor analysis (Filzmoser et al., 2009b), and discriminant analysis (Filzmoser et al., 2009c). Furthermore, methods to improve the quality of compositional data sets are implemented, like outlier detection (Filzmoser et al., 2008), and imputation of missing values (Hron et al, 2010). The latter one, based on a modified k-nearest neighbor algorithm and a model-based imputation, is also supported with measures of quality of imputation and diagnostic plots. The usage of the package will be illustrated on practical examples.

Evolution of the ESA CCI Soil Moisture climate data records and their underlying merging methodology

Abstract: . The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent quality-controlled long-term (1978–2018) climate data records for soil moisture, which serves thousands of scientists and data users worldwide. It harmonises and merges soil moisture retrievals from multiple satellites into (i) an active-microwave-based-only product, (ii) a passive-microwave-based-only product and (iii) a combined active–passive product, which are sampled to daily global images on a 0.25 ∘ regular grid. Since its first release in 2012 the algorithm has undergone substantial improvements which have so far not been thoroughly reported in the scientific literature. This paper fills this gap by reviewing and discussing the science behind the three major ESA CCI SM merging algorithms, versions 2 ( https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c ; Wagner et al. , 2018 ), 3 ( https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c ; Dorigo et al. , 2018 ) and 4 ( https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e ; Dorigo et al. , 2019 ), and provides an outlook on the expected improvements planned for the next algorithm, version 5.

Origin of NBTI variability in deeply scaled pFETs

Abstract: The similarity between Random Telegraph Noise and Negative Bias Temperature Instability (NBTI) relaxation is further demonstrated by the observation of exponentially-distributed threshold voltage shifts corresponding to single-carrier discharges in NBTI transients in deeply scaled pFETs. A SPICE-based simplified channel percolation model is devised to confirm this behavior. The overall device-to-device ΔV th distribution following NBTI stress is argued to be a convolution of exponential distributions of uncorrelated individual charged defects Poisson-distributed in number. An analytical description of the total NBTI threshold voltage shift distribution is derived, allowing, among other things, linking its first two moments with the average number of defects per device.