4‐Benzoyl‐3,4‐dihydro‐2H‐1,4‐benzoxazine‐2‐carbonitrile: refinement using a multipolar atom model

The structural model for the title compound, C₁₆H₁₂N₂O₂, was refined using a multipolar atom model transferred from an experimental electron‐density database. The refinement showed some improvements of crystallographic statistical indices when compared with a conventional spherical neutral‐atom refinement. The title compound adopts a half‐chair conformation. The amide N atom lies almost in the plane defined by the three neighbouring C atoms. In the crystal structure, molecules are linked by weak intermolecular C—H...O and C—H...π hydrogen bonds.     

Gold Catalysis: In Situ EXAFS Study of Homogeneous Oxidative Esterification

Gold complexes were prepared and investigated as catalysts for the oxidative esterification of aldehydes. Stabilisation by pyridine ligands gave good conversions and the in situ extended X‐ray absorption fine structure (EXAFS) study of the reactions indicated that the reaction mixtures contained only mononuclear gold species. Thus, this is the first proof for a homogeneous gold‐catalysed oxidation reaction; the presence of nanoparticles could be excluded experimentally.     

Nonlinear Schrödinger Equations and Their Spectral Semi-Discretizations Over Long Times

Cubic Schrödinger equations with small initial data (or small nonlinearity) and their spectral semi-discretizations in space are analyzed. It is shown that along both the solution of the nonlinear Schrödinger equation as well as the solution of the semi-discretized equation the actions of the linear Schrödinger equation are approximately conserved over long times. This also allows us to show approximate conservation of energy and momentum along the solution of the semi-discretized equation over long times. These results are obtained by analyzing a modulated Fourier expansion in time. They are valid in arbitrary spatial dimension.     

Clustering of imbalanced high-dimensional media data

Media content in large repositories usually exhibits multiple groups of strongly varying sizes. Media of potential interest often form notably smaller groups. Such media groups differ so much from the remaining data that it may be worthy to look at them in more detail. In contrast, media with popular content appear in larger groups. Identifying groups of varying sizes is addressed by clustering of imbalanced data. Clustering highly imbalanced media groups is additionally challenged by the high dimensionality of the underlying features. In this paper, we present the imbalanced clustering (IClust) algorithm designed to reveal group structures in high-dimensional media data. IClust employs an existing clustering method in order to find an initial set of a large number of potentially highly pure clusters which are then successively merged. The main advantage of IClust is that the number of clusters does not have to be pre-specified and that no specific assumptions about the cluster or data characteristics need to be made. Experiments on real-world media data demonstrate that in comparison to existing methods, IClust is able to better identify media groups, especially groups of small sizes.