Johannes Kepler University of Linz

About: Johannes Kepler University of Linz is a education organization based out in Linz, Oberösterreich, Austria. It is known for research contribution in the topics: Thin film & Quantum dot. The organization has 6605 authors who have published 19243 publications receiving 385667 citations.

Toward Improved Predictions in Ungauged Basins: Exploiting the Power of Machine Learning

Abstract: Long Short-Term Memory (LSTM) networks offer unprecedented accuracy for prediction in ungauged basins. We trained and tested an LSTM on the CAMELS basins (approximately 30 years of daily rainfall/runoff data from 531 catchments in the US of sizes ranging from 4 km² to 2,000 km²) using k-fold validation, so that predictions were made in basins that supplied no training data. This effectively `ungauged model was benchmarked over a 15-year validation period against the Sacramento Soil Moisture Accounting (SAC-SMA) model and also against the NOAA National Water Model reanalysis. SAC-SMA was calibrated separately for each basin using 15 years of daily data (i.e., this is a `gauged model). The out-of-sample LSTM had higher median Nash-Sutcliffe Efficiencies across the 531 basins (0.69) than either the calibrated SAC-SMA (0.64) or the National Water Model (0.58). We outline several future research directions that would help develop this technology into a comprehensive regional hydrology model.

Quantum biology revisited

Abstract: Photosynthesis is a highly optimized process from which valuable lessons can be learned about the operating principles in nature. Its primary steps involve energy transport operating near theoretical quantum limits in efficiency. Recently, extensive research was motivated by the hypothesis that nature used quantum coherences to direct energy transfer. This body of work, a cornerstone for the field of quantum biology, rests on the interpretation of small-amplitude oscillations in two-dimensional electronic spectra of photosynthetic complexes. This Review discusses recent work reexamining these claims and demonstrates that interexciton coherences are too short lived to have any functional significance in photosynthetic energy transfer. Instead, the observed long-lived coherences originate from impulsively excited vibrations, generally observed in femtosecond spectroscopy. These efforts, collectively, lead to a more detailed understanding of the quantum aspects of dissipation. Nature, rather than trying to avoid dissipation, exploits it via engineering of exciton-bath interaction to create efficient energy flow.

The relaxation of polymers with linear flexible chains of uniform length

Abstract: The analysis of dynamic mechanical data indicates that linear flexible polymer chains of uniform length follow a scaling relation during their relaxation, having a linear viscoelastic relaxation spectrum of the formH(λ) = n 1 G 0 × (λ/λ max) n1 forλ≤λ max. Data are well represented with a scaling exponent of about 0.22 for polystyrene and 0.42 for polybutadiene. The plateau modulusG 0 is a material-specific constant and the longest relaxation time depends on the molecular weight in the expected way. At high frequencies, the scaling behavior is masked by the transition to the glassy response. Surprisingly, this transition seems to follow a Chambon-Winter spectrumH(λ) = Cλ−n2, which was previously adopted for describing other liquid/solid transitions. The analysis shows that the Rouse spectrum is most suitable for low molecular-weight polymersM ≈ M c , and that the de Gennes-Doi-Edwards spectrum clearly predicts terminal relaxation, but deviates from the observed behavior in the plateau region.

Towards learning universal, regional, and local hydrological behaviors via machine learning applied to large-sample datasets

Abstract: . Regional rainfall–runoff modeling is an old but still mostly outstanding problem in the hydrological sciences. The problem currently is that traditional hydrological models degrade significantly in performance when calibrated for multiple basins together instead of for a single basin alone. In this paper, we propose a novel, data-driven approach using Long Short-Term Memory networks (LSTMs) and demonstrate that under a “big data” paradigm, this is not necessarily the case. By training a single LSTM model on 531 basins from the CAMELS dataset using meteorological time series data and static catchment attributes, we were able to significantly improve performance compared to a set of several different hydrological benchmark models. Our proposed approach not only significantly outperforms hydrological models that were calibrated regionally, but also achieves better performance than hydrological models that were calibrated for each basin individually. Furthermore, we propose an adaption to the standard LSTM architecture, which we call an Entity-Aware-LSTM (EA-LSTM), that allows for learning catchment similarities as a feature layer in a deep learning model. We show that these learned catchment similarities correspond well to what we would expect from prior hydrological understanding.

On the Use of Neurophysiological Tools in IS Research: Developing a Research Agenda for NeuroIS

Abstract: This article aims to discuss the use of common neurophysiological tools, such as psychophysiological tools (e.g., EKG, eye tracking) and neuroimaging tools (e.g., fMRI, EEG) in Information Systems (IS) research. There is much interest in the social sciences in capturing objective data directly from the human body, and this interest has also been gaining momentum in IS research (termed NeuroIS). This article first introduces several commonly-used neurophysiological tools, and it then discusses several application areas and research questions where IS researchers can benefit from neurophysiological data toward developing a research agenda for NeuroIS. The proposed research areas are presented within four fundamental levels of analysis - individuals, groups, organizations, and markets - that are typically used to examine the use of IT.The article concludes with a set of recommendations on how to use neurophysiological tools in IS research along with practical suggestions for establishing NeuroIS as a viable sub-field in the IS literature.