Technical University of Dortmund

About: Technical University of Dortmund is a education organization based out in Dortmund, Nordrhein-Westfalen, Germany. It is known for research contribution in the topics: Context (language use) & Large Hadron Collider. The organization has 13028 authors who have published 27666 publications receiving 615557 citations. The organization is also known as: Dortmund University & University of Dortmund.

Kubaturformeln mit minimaler Knotenzahl

Abstract: For two-fold integrals, a lower bound is derived for the number of nodes in a cubature formula of degree 2s-1. There is a formula of degree 2s-1 for which the number of nodes attains this lower bound, iff a certain condition is fullfilled. By this condition, all formulas of degree 2s-1 with that minimal number of nodes can be constructed. Examples and a generalization to then-dimensional case are given.

Model of the local structure of random ternary alloys: Experiment versus theory.

Abstract: We have performed an extended x-ray-absorption fine-structure (EXAFS) measurement of ${\mathrm{Cd}}_{1\mathrm{\ensuremath{-}}\mathrm{x}}$${\mathrm{Mn}}_{\mathrm{x}}$Te solid solutions for various concentrations x in the single-phase range 0\ensuremath{\le}x\ensuremath{\le}0.7. Data have been collected on the Mn K, Cd ${L}_{\mathrm{III}}$, and Te ${L}_{\mathrm{III}}$ edges. We have found well-defined different nearest-neighbor Cd-Te and Mn-Te distances almost independent of x. A model of the microscopic structure of the zinc-blende-type ${A}_{1\mathrm{\ensuremath{-}}x}$${B}_{x}$C ternary alloys based on a random distribution of cations has been developed. The model describes the bimodal distribution of near-neighbor distances in terms of distortion of the anion sublattice (the cation sublattice is assumed to remain fixed) with use only of the lattice constants of the alloy and the bond-stretching constants of each binary component. Its application to ${\mathrm{Cd}}_{1\mathrm{\ensuremath{-}}\mathrm{x}}$${\mathrm{Mn}}_{\mathrm{x}}$Te and ${\mathrm{In}}_{1\mathrm{\ensuremath{-}}\mathrm{x}}$${\mathrm{Ga}}_{\mathrm{x}}$As alloys is proved to be in good agreement with the EXAFS results. Within the framework of this model we also consider the problem of the structural stability of ${\mathrm{Cd}}_{1\mathrm{\ensuremath{-}}\mathrm{x}}$${\mathrm{Mn}}_{\mathrm{x}}$Te.

Exceptional Model Mining

Abstract: Finding subsets of a dataset that somehow deviate from the norm, i.e. where something interesting is going on, is a classical Data Mining task. In traditional local pattern mining methods, such deviations are measured in terms of a relatively high occurrence (frequent itemset mining), or an unusual distribution for one designated target attribute (common use of subgroup discovery). These, however, do not encompass all forms of "interesting". To capture a more general notion of interestingness in subsets of a dataset, we develop Exceptional Model Mining (EMM). This is a supervised local pattern mining framework, where several target attributes are selected, and a model over these targets is chosen to be the target concept. Then, we strive to find subgroups: subsets of the dataset that can be described by a few conditions on single attributes. Such subgroups are deemed interesting when the model over the targets on the subgroup is substantially different from the model on the whole dataset. For instance, we can find subgroups where two target attributes have an unusual correlation, a classifier has a deviating predictive performance, or a Bayesian network fitted on several target attributes has an exceptional structure. We give an algorithmic solution for the EMM framework, and analyze its computational complexity. We also discuss some illustrative applications of EMM instances, including using the Bayesian network model to identify meteorological conditions under which food chains are displaced, and using a regression model to find the subset of households in the Chinese province of Hunan that do not follow the general economic law of demand.

The Open-Source LearnLib

Abstract: In this paper, we present LearnLib, a library for active automata learning. The current, open-source version of LearnLib was completely rewritten from scratch, incorporating the lessons learned from the decade-spanning development process of the previous versions of LearnLib. Like its immediate predecessor, the open-source LearnLib is written in Java to enable a high degree of flexibility and extensibility, while at the same time providing a performance that allows for large-scale applications. Additionally, LearnLib provides facilities for visualizing the progress of learning algorithms in detail, thus complementing its applicability in research and industrial contexts with an educational aspect. Open image in new window