Frankfurt Institute for Advanced Studies

About: Frankfurt Institute for Advanced Studies is a facility organization based out in Frankfurt am Main, Germany. It is known for research contribution in the topics: Baryon & Quark–gluon plasma. The organization has 798 authors who have published 2733 publications receiving 82799 citations. The organization is also known as: FIAS.

Vorticity in peripheral collisions at the Facility for Antiproton and Ion Research and at the JINR Nuclotron-based Ion Collider fAcility

Abstract: The flow vorticity development is studied in the reaction plane of peripheral relativistic heavy-ion reactions at energies just above the threshold of the transition to a quark-gluon plasma (QGP). Earlier calculations at higher energies with larger initial angular momentum predicted significant vorticity leading to measurable $\ensuremath{\Lambda}$ polarization. Here we discuss the possibility of vorticity and circulation in dense plasma at lower temperatures. In low-viscosity QGP this vorticity still remains significant.

Eccentricity fluctuations in an integrated hybrid approach: Influence on elliptic flow

Abstract: of state and for averaged initial conditions or a full event-by-event setup are presented. These investigations allow the conclusion that in mid-central (b = 5‐9 fm) heavy-ion collisions the final elliptic flow is independent of the initial state fluctuations and the equation of state. Furthermore, it is demonstrated that most of the v2 is built up during the hydrodynamic stage of the evolution. Therefore, the use of averaged initial profiles does not contribute to the uncertainties of the extraction of transport properties of hot and dense QCD matter based on viscous hydrodynamic calculations.

Automated detection of circulating tumor cells with naive Bayesian classifiers

Abstract: Personalized medicine is a modern healthcare approach where information on each person's unique clinical constitution is exploited to realize early disease intervention based on more informed medical decisions. The application of diagnostic tools in combination with measurement evaluation that can be performed in a reliable and automated fashion plays a key role in this context. As the progression of various cancer diseases and the effectiveness of their treatments are related to a varying number of tumor cells that circulate in blood, the determination of their extremely low numbers by liquid biopsy is a decisive prognostic marker. To detect and enumerate circulating tumor cells (CTCs) in a reliable and automated fashion, we apply methods from machine learning using a naive Bayesian classifier (NBC) based on a probabilistic generative mixture model. Cells are collected with a functionalized medical wire and are stained for fluorescence microscopy so that their color signature can be used for classification through the construction of Red-Green-Blue (RGB) color histograms. Exploiting the information on the fluorescence signature of CTCs by the NBC does not only allow going beyond previous approaches but also provides a method of unsupervised learning that is required for unlabeled training data. A quantitative comparison with a state-of-the-art support vector machine, which requires labeled data, demonstrates the competitiveness of the NBC method. © 2014 International Society for Advancement of Cytometry