ROSeAnn

Ortona, Stefano; Orsi, Giorgio

doi:10.1145/2567948.2578038

Cited by 3 publications

(1 citation statement)

References 7 publications

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“…Figure 2: (Color online) D-meson R AA (left panel) and v 2 (right panel) in Pb+Pb ( √ s NN =2.76 TeV) collisions, com-pared to ALICE data[28,29]. Our results for charm quarks, D mesons after hadronization at T c and after hadronic diffusion at T kin , are shown separately.…”

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confidence: 99%

Non-perturbative Heavy-Flavor Transport at RHIC and LHC

Fries

Rapp

2013

Nuclear Physics A

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We calculate open heavy-flavor (HF) transport in relativistic heavy-ion collisions by applying a strong-coupling treatment in both macro-and microscopic dynamics (hydrodynamics and non-perturbative diffusion interactions). The hydrodynamic medium evolution is quantitatively constrained by bulk and multi-strange hadron spectra and elliptic flow. The heavy quark (HQ) transport coefficient is evaluated from a non-perturbative T -matrix approach in the Quark-Gluon Plasma (QGP) which, close to the critical temperature, leads to resonance formation and feeds into the recombination of heavy quarks on a hydrodynamic hypersurface. In the hadronic phase, the diffusion of HF mesons is obtained from effective hadronic theory. We compute observables at RHIC and LHC for non-photonic electrons and HF mesons, respectively.

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confidence: 99%

Non-perturbative Heavy-Flavor Transport at RHIC and LHC

Fries

Rapp

2013

Nuclear Physics A

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Value Entity Recognition Task in the Air Transportation on the Base of E-Texts Analysis

Zervina

Stukalina

Pavlyuk

2023

Lecture Notes in Networks and Systems

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Aggregating semantic annotators

2013

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A growing number of resources are available for enriching documents with semantic annotations. While originally focused on a few standard classes of annotations, the ecosystem of annotators is now becoming increasingly diverse. Although annotators often have very different vocabularies, with both high-level and specialist concepts, they also have many semantic interconnections. We will show that both the overlap and the diversity in annotator vocabularies motivate the need for semantic annotation integration: middleware that produces a unified annotation on top of diverse semantic annotators. On the one hand, the diversity of vocabulary allows applications to benefit from the much richer vocabulary available in an integrated vocabulary. On the other hand, we present evidence that the most widely-used annotators on the web suffer from serious accuracy deficiencies: the overlap in vocabularies from individual annotators allows an integrated annotator to boost accuracy by exploiting inter-annotator agreement and disagreement.The integration of semantic annotations leads to new challenges, both compared to usual data integration scenarios and to standard aggregation of machine learning tools. We overview an approach to these challenges that performs ontology-aware aggregation. We introduce an approach that requires no training data, making use of ideas from database repair. We experimentally compare this with a supervised approach, which adapts maximal entropy Markov models to the setting of ontology-based annotations. We further experimentally compare both these approaches with respect to ontologyunaware supervised approaches, and to individual annotators.

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ROSeAnn

Cited by 3 publications

References 7 publications

Non-perturbative Heavy-Flavor Transport at RHIC and LHC

Non-perturbative Heavy-Flavor Transport at RHIC and LHC

Value Entity Recognition Task in the Air Transportation on the Base of E-Texts Analysis

Aggregating semantic annotators

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