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Mathematical formulae are essential in science, but face challenges of ambiguity, due to the use of a small number of identifiers to represent an immense number of concepts. Corresponding to word sense disambiguation in Natural Language Processing, we disambiguate mathematical identifiers. By regarding formulae and natural text as one monolithic information source, we are able to extract the semantics of identifiers in a process we term Mathematical Language Processing (MLP). As scientific communities tend to establish standard (identifier) notations, we use the document domain to infer the actual meaning of an identifier. Therefore, we adapt the software development concept of namespaces to mathematical notation. Thus, we learn namespace definitions by clustering the MLP results and mapping those clusters to subject classification schemata. In addition, this gives fundamental insights into the usage of mathematical notations in science, technology, engineering and mathematics. Our gold standard based evaluation shows that MLP extracts relevant identifierdefinitions. Moreover, we discover that identifier namespaces improve the performance of automated identifier-definition extraction, and elevate it to a level that cannot be achieved within the document context alone.

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With the advent of new contact-less sensors for forensic investigations of latent fingerprint traces, the authors see the need for a benchmarking framework to evaluate existing devices and promising combinations of data acquisition and signal processing techniques. This paper extends the existing benchmarking framework from [1] by categorizing it into properties from a forensic point-of-view (end-user) and a technical point-ofview (scientific-user) and applies a known differential image technique for the subjective evaluation of which traces are visible. We show exemplary results for a chromatic white light (CWL) sensor for the surface quality assessment, using and comparing the experimental setup of 10 surfaces from [1] and additional 10 surfaces, including real-world objects, to determine its potential for detecting latent fingerprints. Using a differential image approach, the particular influence of sensor noise signals is analyzed, showing that this differential approach cannot always be considered as an ideal filter for fingerprint pattern detection.