Charge Transfer in c-Si(n)/TiO(ALD) at the Amorphous/Anatase Transition: A Transient Surface Photovoltage Spectroscopy Study.

Electronic properties and charge transfer processes were studied in an n-Si(n)/TiO(ALD) system at an amorphous TiO/anatase transition by transient surface photovoltage spectroscopy at constant photon flux. The TiO layers were deposited by atomic layer deposition (ALD) onto highly doped silicon (c-Si(n)), and the phase composition of the TiO layers changed with increasing thickness from amorphous to the anatase polymorph as anatase crystallites started to grow at the surface. Depending on phase composition, the band gap of TiO correlated with the characteristic energy of exponential tails.

Semantic localization-driven partial image retrieval in CT series.

Background: Picture archiving and communication systems (PACS) contain very large amounts of computed tomography (CT) data. When querying a PACS for a particular series, the user is often not interested in the complete series but in a certain region of interest (ROI), described e.g.

2D image registration in CT images using radial image descriptors.

Registering CT scans in a body atlas is an important technique for aligning and comparing different CT scans. It is also required for navigating automatically to certain regions of a scan or if sub volumes should be identified automatically. Common solutions to this problem employ landmark detectors and interpolation techniques.

Supervised ensembles of prediction methods for subcellular localization.

In the past decade, many automated prediction methods for the subcellular localization of proteins have been proposed, utilizing a wide range of principles and learning approaches. Based on an experimental evaluation of different methods and their theoretical properties, we propose to combine a well-balanced set of existing approaches to new, ensemble-based prediction methods. The experimental evaluation shows that our ensembles improve substantially over the underlying base methods.

Extraction of semantic biomedical relations from text using conditional random fields.

Background: The increasing amount of published literature in biomedicine represents an immense source of knowledge, which can only efficiently be accessed by a new generation of automated information extraction tools. Named entity recognition of well-defined objects, such as genes or proteins, has achieved a sufficient level of maturity such that it can form the basis for the next step: the extraction of relations that exist between the recognized entities. Whereas most early work focused on the mere detection of relations, the classification of the type of relation is also of great importance and this is the focus of this work.