Publication Server of Zuse Institute Berlin (ZIB)
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Calculation of the local environment of a barium monofluoride molecule in an argon matrix: a step towards using matrix-isolated BaF for determining the electron electric dipole moment
No full textAccurate calculation of the interaction of a barium monofluoride molecule with an argon atom: A step towards using matrix isolation of BaF for determining the electron electric dipole moment
No full textBerliner Kulturerbe digital: Ein quantitativer Blick auf die Metadatenqualität
No full textDie Metadatenqualität bestimmt wesentlich den Nutzen und Wert von Kulturerbedaten. ‚Gute‘ Metadaten erhöhen die Auffindbarkeit, Interoperabilität und Nutzbarkeit von Daten signifikant. Mit Blick auf Retrieval bzw. Discovery, Vernetzung im Kontext von Linked Open Data und
wissenschaftliches Data Mining hängt die Qualität dabei wesentlich von der Verwendung von maschinenlesbaren kontrollierten Vokabularen ab. Diese wird in der vorliegenden Arbeit quantitativ untersucht. Als Datengrundlage dienen die in der Deutschen Digitalen Bibliothek aggregierten Metadaten aus Berliner Museen (ca. 1,2 Millionen Metadatenobjekte im LIDO-Format
To What Extent Can Individualisation in Terms of Different Types of Mode Improve Learning Outcomes and Learner Satisfaction? A Pre-study
No full textOptimal Reaction Coordinates: Variational Characterization and Sparse Computation
No full textReaction coordinates (RCs) are indicators of hidden, low-dimensional mechanisms that govern the long-term behavior of high-dimensional stochastic processes. We present a novel and general variational characterization of optimal RCs and provide conditions for their existence. Optimal RCs are minimizers of a certain loss function, and reduced models based on them guarantee a good approximation of the statistical long-term properties of the original high-dimensional process. We show that for slow-fast systems, metastable systems, and other systems with known good RCs, the novel theory reproduces previous insight. Remarkably, for reversible systems, the numerical effort required to evaluate the loss function scales only with the variability of the underlying, low-dimensional mechanism, and not with that of the full system. The theory provided lays the foundation for an efficient and data-sparse computation of RCs via modern machine learning techniques
Global Optimization of Mixed-Integer Nonlinear Programs with SCIP 8.0
No full textFor over ten years, the constraint integer programming framework SCIP has been extended by capabilities for the solution of convex and nonconvex mixed-integer nonlinear programs (MINLPs). With the recently published version 8.0, these capabilities have been largely reworked and extended. This paper discusses the motivations for recent changes and provides an overview of features that are particular to MINLP solving in SCIP. Further, difficulties in benchmarking global MINLP solvers are discussed and a comparison with several state-of-the-art global MINLP solvers is provided
Sasaki Metric for Spline Models of Manifold-Valued Trajectories
No full textWe propose a generic spatiotemporal framework to analyze manifold-valued measurements, which allows for employing an intrinsic and computationally efficient Riemannian hierarchical model. Particularly, utilizing regression, we represent discrete trajectories in a Riemannian manifold by composite Bézier splines, propose a natural metric induced by the Sasaki metric to compare the trajectories, and estimate average trajectories as group-wise trends. We evaluate our framework in comparison to state-of-the-art methods within qualitative and quantitative experiments on hurricane tracks. Notably, our results demonstrate the superiority of spline-based approaches for an intensity classification of the tracks
