Electronic and vibrational states of single tin-phthalocyanine molecules in double layers on Ag(111)

Electronic and vibrational properties of the two stable molecular configurations of Sn-phthalocyanine adsorbed on an ultrathin Sn-phthalocyanine buffer film on Ag(111) have been investigated with scanning tunneling microscopy and density functional calculations. Complex submolecular patterns are experimentally observed in unoccupied states images. The calculations show that they result from a superposition of Sn p orbitals. Furthermore, the characteristic features in spectra of the differential conductance are reproduced by the calculations together with a remarkable difference between the two configurations. First-principles calculations show that rather than a single vibrational mode and its higher harmonics the excitations of different molecular vibrational quanta induce replica of orbital spectroscopic signatures. The replicated orbital features appear for the configuration with a low molecule-surface coupling. To model spectra of molecules with a larger coupling to the surface it is sufficient to consider elastic tunneling to orbital resonances alone

Interpreting and Unifying Outlier Scores

Outlier scores provided by different outlier models differ widely in their meaning, range, and contrast between different outlier models and, hence, are not easily comparable or interpretable. We propose a unification of outlier scores provided by various outlier models and a translation of the arbitrary “outlier factors ” to values in the range [0, 1] interpretable as values describing the probability of a data object of being an outlier. As an application, we show that this unification facilitates enhanced ensembles for outlier detection.

Outlier detection in axis-parallel subspaces of high dimensional data

We propose an original outlier detection schema that detects outliers in varying subspaces of a high dimensional feature space. In particular, for each object in the data set, we explore the axis-parallel subspace spanned by its neighbors and determine how much the object deviates from the neighbors in this subspace. In our experiments, we show that our novel subspace outlier detection is superior to existing fulldimensional approaches and scales well to high dimensional databases