Technische Universität Darmstadt

About: Technische Universität Darmstadt is a education organization based out in Darmstadt, Germany. It is known for research contribution in the topics: Computer science & Context (language use). The organization has 17316 authors who have published 40619 publications receiving 937916 citations. The organization is also known as: Darmstadt University of Technology & University of Darmstadt.

Structure of the Hoyle State in C 12

Abstract: The first excited 0(+) state in 12C (Hoyle state) has been predicted to be a dilute self-bound gas of bosonic alpha particles, similar to a Bose-Einstein condensate. To clarify this conjecture, precise electron scattering data on form factors of the ground state and the transition to the Hoyle state are compared with results of the fermionic molecular dynamics model, a microscopic alpha-cluster model, and an alpha-cluster model with reduced degrees of freedom (in the spirit of a Bose-Einstein condensed state). The data indicate clearly a dilute density with a large spatial extension of the Hoyle state. A closer inspection of the model calculations, which reproduce the experimental findings, reveals that the term Bose-Einstein condensation of three alpha particles must not be taken too literally.

Supply chain finance: optimizing financial flows in supply chains

Abstract: Issues related to flows of goods and information are frequently discussed in the logistics and Supply Chain Management literature. But, only few contributions are exploring the financial flows associated with supply chains. This article reviews the state-of-the-art of research regarding financial flows in supply chains. In doing so, it becomes apparent that an explicit examination and optimisation of the cost of capital has been missing so far. In order to close this gap, a conceptual framework and a mathematical model of “Supply Chain Finance” is proposed.

Constraints on Neutron Star Radii Based on Chiral Effective Field Theory Interactions

Abstract: We show that microscopic calculations based on chiral effective field theory interactions constrain the properties of neutron-rich matter below nuclear densities to a much higher degree than is reflected in commonly used equations of state. Combined with observed neutron star masses, our results lead to a radius $R=9.7--13.9\text{ }\text{ }\mathrm{km}$ for a $1.4{M}_{\ensuremath{\bigodot}}$ star, where the theoretical range is due, in about equal amounts, to uncertainties in many-body forces and to the extrapolation to high densities.

Conquering aspects with Caesar

Abstract: Join point interception (JPI), is considered an important cornerstone of aspect-oriented languages. However, we claim that JPI alone does not suffice for a modular structuring of aspects. We propose CAESAR, a model for aspect-oriented programming with a higher-level module concept on top of JPI, which enables reuse and componentization of aspects, allows us to use aspects polymorphically, and introduces a novel concept for dynamic aspect deployment.

A Machine-learning Approach for Classifying and Categorizing Android Sources and Sinks

Abstract: Today’s smartphone users face a security dilemma: many apps they install operate on privacy-sensitive data, although they might originate from developers whose trustworthiness is hard to judge. Researchers have addressed the problem with more and more sophisticated static and dynamic analysis tools as an aid to assess how apps use private user data. Those tools, however, rely on the manual configuration of lists of sources of sensitive data as well as sinks which might leak data to untrusted observers. Such lists are hard to come by. We thus propose SUSI, a novel machine-learning guided approach for identifying sources and sinks directly from the code of any Android API. Given a training set of hand-annotated sources and sinks, SUSI identifies other sources and sinks in the entire API. To provide more fine-grained information, SUSI further categorizes the sources (e.g., unique identifier, location information, etc.) and sinks (e.g., network, file, etc.). For Android 4.2, SUSI identifies hundreds of sources and sinks with over 92% accuracy, many of which are missed by current information-flow tracking tools. An evaluation of about 11,000 malware samples confirms that many of these sources and sinks are indeed used. We furthermore show that SUSI can reliably classify sources and sinks even in new, previously unseen Android versions and components like Google Glass or the Chromecast API.