Terminology mining in social media

The highly variable and dynamic word usage in social media presents serious challenges for both research and those commercial applications that are geared towards blogs or other user-generated non-editorial texts. This paper discusses and exemplifies a terminology mining approach for dealing with the productive character of the textual environment in social media. We explore the challenges of practically acquiring new terminology, and of modeling similarity and relatedness of terms from observing realistic amounts of data. We also discuss semantic evolution and density, and investigate novel measures for characterizing the preconditions for terminology mining

A survey of carbon nanotube interconnects for energy efficient integrated circuits

This article is a review of the state-of-art carbon nanotube interconnects for Silicon application with respect to the recent literature. Amongst all the research on carbon nanotube interconnects, those discussed here cover 1) challenges with current copper interconnects, 2) process & growth of carbon nanotube interconnects compatible with back-end-of-line integration, and 3) modeling and simulation for circuit-level benchmarking and performance prediction. The focus is on the evolution of carbon nanotube interconnects from the process, theoretical modeling, and experimental characterization to on-chip interconnect applications. We provide an overview of the current advancements on carbon nanotube interconnects and also regarding the prospects for designing energy efficient integrated circuits. Each selected category is presented in an accessible manner aiming to serve as a survey and informative cornerstone on carbon nanotube interconnects relevant to students and scientists belonging to a range of fields from physics, processing to circuit design

Coupled Transformations of Graph Structures applied to Model Migration

Model-Driven Engineering (MDE) is a relatively new paradigm in software engineering that pursues the goal to master the increased complexity of modern software products. While software applications have been developed for a specific platform in the past, today they are targeting various platforms and devices from classical desktop PCs to smart phones. In addition, they interact with other applications. To easier cope with these new requirements, software applications are specified in MDE at a high abstraction level in so called models prior to their implementation. Afterward, model transformations are used to automate recurring development tasks as well as to generate software artifacts for different runtime environments. Thereby, software artifacts are not necessarily files containing program code, they can also cover configuration files as well as machine readable input for model checking tools. However, MDE does not only address software engineering problems, it also raises new challenges. One of these new challenges is connected to the specification of modeling languages, which are used to create models. The creation of a modeling language is a creative process that requires several iterations similar to the creation of models. New requirements as well as a better understanding of the application domain result in an evolution of modeling languages over time. Models developed in an earlier version of a modeling language often needs to be co-adopted (migrated) to language changes. This migration should be automated, as migrating models manually is time consuming and error-prone. While application modelers use ad-hoc solutions to migrate their models, there is still a lack of theory to ensure well-defined migration results. This work contributes to a formalization of modeling language evolution with corresponding model migration on the basis of algebraic graph transformations that have successfully been used earlier as theoretical foundations of model transformation. The goal of this research is to develop a theory that considers the problem of modeling language evolution with corresponding model migration on a conceptual level, independent of a specific modeling framework

Ab initio Stellar Astrophysics: Reliable Modeling of Cool White Dwarf Atmospheres

Over the last decade {\it ab initio} modeling of material properties has become widespread in diverse fields of research. It has proved to be a powerful tool for predicting various properties of matter under extreme conditions. We apply modern computational chemistry and materials science methods, including density functional theory (DFT), to solve lingering problems in the modeling of the dense atmospheres of cool white dwarfs ($T_{\rm eff}\rm <7000 \, K$). Our work on the revision and improvements of the absorption mechanisms in the hydrogen and helium dominated atmospheres resulted in a new set of atmosphere models. By inclusion of the Ly-$\rm \alpha$ red wing opacity we successfully fitted the entire spectral energy distributions of known cool DA stars. In the subsequent work we fitted the majority of the coolest stars with hydrogen-rich models. This finding challenges our understanding of the spectral evolution of cool white dwarfs. We discuss a few examples, including the cool companion to the pulsar PSR J0437-4715. The two problems important for the understanding of cool white dwarfs are the behavior of negative hydrogen ion and molecular carbon in a fluid-like, helium dominated medium. Using {\it ab initio} methods we investigate the stability and opacity of these two species in dense helium. Our investigation of $\rm C_2$ indicates that the absorption features observed in the ``peculiar'' DQp white dwarfs resemble the absorption of perturbed $\rm C_2$ in dense helium.Comment: 6 pages, 4 figures, submitted to proceedings of 17th European White Dwarf Workshop, Tuebingen, Germany 201