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- Frankfurt Institute for Advanced Studies (FIAS) (1015) (remove)
We study the phase diagram of a generalized chiral SU(3)-flavor model in mean-field approxi- mation. In particular, the influence of the baryon resonances, and their couplings to the scalar and vector fields, on the characteristics of the chiral phase transition as a function of temperature and baryon-chemical potential is investigated. Present and future finite-density lattice calculations might constrain the couplings of the fields to the baryons. The results are compared to recent lattice QCD calculations and it is shown that it is non-trivial to obtain, simultaneously, stable cold nuclear matter.
Compelling evidence for the creation of a new form of matter has been claimed to be found in Pb+Pb collisions at SPS. We discuss the uniqueness of often proposed experimental signatures for quark matter formation in relativistic heavy ion collisions. It is demonstrated that so far none of the proposed signals like J/psi meson production/suppression, strangeness enhancement, dileptons, and directed flow unambigiously show that a phase of deconfined matter has been formed in SPS Pb+Pb collisions. We emphasize the need for systematic future measurements to search for simultaneous irregularities in the excitation functions of several observables in order to come close to pinning the properties of hot, dense QCD matter from data.
Elliptic flow analysis at RHIC with the Lee-Yang Zeroes method in a relativistic transport approach
(2006)
The Lee-Yang zeroes method is applied to study elliptic flow (v_2) in Au+Au collisions at sqrt s =200 A GeV, with the UrQMD model. In this transport approach, the true event plane is known and both the nonflow effects and event-by-event v_2 fluctuations exist. Although the low resolutions prohibit the application of the method for most central and peripheral collisions, the integral and differential elliptic flow from the Lee-Yang zeroes method agrees with the exact v_2 values very well for semi-central collisions.
The cumulant method is applied to study elliptic flow (v_2) in Au+Au collisions at sqrt s=200 AGeV, with the UrQMD model. In this approach, the true event plane is known and both the non-flow effects and event-by-event spatial (epsilon) and v_2 fluctuations exist. Qualitatively, the hierarchy of v_2 's from two, four and six-particle cumulants is consistent with the STAR data, however, the magnitude of v_2 in the UrQMD model is only 60% of the data. We find that the four and six-particle cumulants are good measures of the real elliptic flow over a wide range of centralities except for the most central and very peripheral events. There the cumulant method is affected by the v_2 fluctuations. In mid-central collisions, the four and six-particle cumulants are shown to give a good estimation of the true differential v_2, especially at large transverse momentum, where the two-particle cumulant method is heavily affected by the non-flow effects.
We propose to measure correlations of heavy-flavor hadrons to address the status of thermalization at the partonic stage of light quarks and gluons in high-energy nuclear collisions, shown on the example of azimuthal correlations of D-Dbar pairs. We show that hadronic interactions at the late stage can not disturb these correlations significantly. Thus, a decrease or the complete absence of these initial correlations indicates frequent interactions of heavy-flavor quarks in the partonic stage. Therefore, early thermalization of light quarks is likely to be reached. PACS numbers: 25.75.-q
The behavior of hadronic matter at high baryon densities is studied within Ultrarelativistic Quantum Molecular Dynamics (URQMD). Baryonic stopping is observed for Au+Au collisions from SIS up to SPS energies. The excitation function of flow shows strong sensitivities to the underlying equation of state (EOS), allowing for systematic studies of the EOS. Effects of a density dependent pole of the rho-meson propagator on dilepton spectra are studied for different systems and centralities at CERN energies.
A key competence for open-ended learning is the formation of increasingly abstract representations useful for driving complex behavior. Abstract representations ignore specific details and facilitate generalization. Here we consider the learning of abstract representations in a multi-modal setting with two or more input modalities. We treat the problem as a lossy compression problem and show that generic lossy compression of multimodal sensory input naturally extracts abstract representations that tend to strip away modalitiy specific details and preferentially retain information that is shared across the different modalities. Furthermore, we propose an architecture to learn abstract representations by identifying and retaining only the information that is shared across multiple modalities while discarding any modality specific information.
Orientation hypercolumns in the visual cortex are delimited by the repeating pinwheel patterns of orientation selective neurons. We design a generative model for visual cortex maps that reproduces such orientation hypercolumns as well as ocular dominance maps while preserving retinotopy. The model uses a neural placement method based on t–distributed stochastic neighbour embedding (t–SNE) to create maps that order common features in the connectivity matrix of the circuit. We find that, in our model, hypercolumns generally appear with fixed cell numbers independently of the overall network size. These results would suggest that existing differences in absolute pinwheel densities are a consequence of variations in neuronal density. Indeed, available measurements in the visual cortex indicate that pinwheels consist of a constant number of ∼30, 000 neurons. Our model is able to reproduce a large number of characteristic properties known for visual cortex maps. We provide the corresponding software in our MAPStoolbox for Matlab.
We study the effects of isovector-scalar meson delta on the equation of state (EOS) of neutron star matter in strong magnetic fields. The EOS of neutron-star matter and nucleon effective masses are calculated in the framework of Lagrangian field theory, which is solved within the mean-field approximation. From the numerical results one can find that the delta-field leads to a remarkable splitting of proton and neutron effective masses. The strength of delta-field decreases with the increasing of the magnetic field and is little at ultrastrong field. The proton effective mass is highly influenced by magnetic fields, while the effect of magnetic fields on the neutron effective mass is negligible. The EOS turns out to be stiffer at B < 10^15G but becomes softer at stronger magnetic field after including the delta-field. The AMM terms can affect the system merely at ultrastrong magnetic field(B > 10^19G). In the range of 10^15 G - 10^18 G the properties of neutron-star matter are found to be similar with those without magnetic fields.
How much data do we need? Lower bounds of brain activation states to predict human cognitive ability
(2022)
Human functional brain connectivity can be temporally decomposed into states of high and low cofluctuation, defined as coactivation of brain regions over time. Despite their low frequency of occurrence, states of particularly high cofluctuation have been shown to reflect fundamentals of intrinsic functional network architecture (derived from resting-state fMRI) and to be highly subject-specific. However, it is currently unclear whether such network-defining states of high cofluctuation also contribute to individual variations in cognitive abilities – which strongly rely on the interactions among distributed brain regions. By introducing CMEP, an eigenvector-based prediction framework, we show that functional connectivity estimates from as few as 20 temporally separated time frames (< 3% of a 10 min resting-state fMRI scan) are significantly predictive of individual differences in intelligence (N = 281, p < .001). In contrast and against previous expectations, individual’s network-defining time frames of particularly high cofluctuation do not achieve significant prediction of intelligence. Multiple functional brain networks contribute to the prediction, and all results replicate in an independent sample (N = 831). Our results suggest that although fundamentals of person-specific functional connectomes can be derived from few time frames of highest brain connectivity, temporally distributed information is necessary to extract information about cognitive abilities from functional connectivity time series. This information, however, is not restricted to specific connectivity states, like network-defining high-cofluctuation states, but rather reflected across the entire length of the brain connectivity time series.