Large Momenta Fluctuations Of Charm Quarks In The Quark-Gluon Plasma

We show that large fluctuations of D mesons kinetic energy (or momentum) distributions might be a signature of a phase transition to the quark gluon plasma (QGP). In particular, a jump in the variance of the momenta or kinetic energy, as a function of a control parameter (temperature or Fermi energy at finite baryon densities) might be a signature for a first order phase transition to the QGP. This behaviour is completely consistent with the order parameter defined for a system of interacting quarks at zero temperature and finite baryon densities which shows a jump in correspondance to a first order phase transition to the QGP. The $J/\Psi$ shows exactly the same behavior of the order parameter and of the variance of the D mesons. We discuss implications for relativistic heavy ion collisions within the framework of a transport model and possible hints for experimental data.Comment: 4 pages 3 figure

Significance of interface anisotropy in laser induced magnetization precession in ferromagnetic metal films

Laser induced ultrafast demagnetization in ferromagnetic metals was discovered almost 20 years ago, but currently there is still lack of consensus on the microscopic mechanism responsible for the corresponding transfer of angular momentum and energy between electron, lattice and spin subsystems. A distinct, but intrinsically correlated phenomenon occurring on a longer timescale is the magnetization precession after the ultrafast demagnetization process, if a magnetic field is applied to tilt the magnetization vector away from its easy direction, which can be attributed to the change of anisotropy after laser heating. In an in-plane magnetized Pt/Co/Pt thin film with perpendicular interface anisotropy, we found excellent agreement between theoretical prediction with plausible parameters and experimental data measured using time resolved magneto-optical Kerr effect. This agreement confirms that the time evolution of the anisotropy field, which is driven by the interaction between electrons and phonons, determines the magnetization precession completely. A detailed analysis shows that, even though the whole sample is magnetized in-plane, the dynamic interface anisotropy field dictates the initial phase of the magnetization precession, highlighting the significance of the interface anisotropy field in laser induced magnetization precession.Comment: 11 pages, 2 figure

Bayesian model comparison for compartmental models with applications in positron emission tomography

We develop strategies for Bayesian modelling as well as model comparison, averaging and selection for compartmental models with particular emphasis on those that occur in the analysis of positron emission tomography (PET) data. Both modelling and computational issues are considered. Biophysically inspired informative priors are developed for the problem at hand, and by comparison with default vague priors it is shown that the proposed modelling is not overly sensitive to prior specification. It is also shown that an additive normal error structure does not describe measured PET data well, despite being very widely used, and that within a simple Bayesian framework simultaneous parameter estimation and model comparison can be performed with a more general noise model. The proposed approach is compared with standard techniques using both simulated and real data. In addition to good, robust estimation performance, the proposed technique provides, automatically, a characterisation of the uncertainty in the resulting estimates which can be considerable in applications such as PET

On Horizontal and Vertical Separation in Hierarchical Text Classification

Hierarchy is a common and effective way of organizing data and representing their relationships at different levels of abstraction. However, hierarchical data dependencies cause difficulties in the estimation of "separable" models that can distinguish between the entities in the hierarchy. Extracting separable models of hierarchical entities requires us to take their relative position into account and to consider the different types of dependencies in the hierarchy. In this paper, we present an investigation of the effect of separability in text-based entity classification and argue that in hierarchical classification, a separation property should be established between entities not only in the same layer, but also in different layers. Our main findings are the followings. First, we analyse the importance of separability on the data representation in the task of classification and based on that, we introduce a "Strong Separation Principle" for optimizing expected effectiveness of classifiers decision based on separation property. Second, we present Hierarchical Significant Words Language Models (HSWLM) which capture all, and only, the essential features of hierarchical entities according to their relative position in the hierarchy resulting in horizontally and vertically separable models. Third, we validate our claims on real-world data and demonstrate that how HSWLM improves the accuracy of classification and how it provides transferable models over time. Although discussions in this paper focus on the classification problem, the models are applicable to any information access tasks on data that has, or can be mapped to, a hierarchical structure.Comment: Full paper (10 pages) accepted for publication in proceedings of ACM SIGIR International Conference on the Theory of Information Retrieval (ICTIR'16

Entanglement between two fermionic atoms inside a cylindrical harmonic trap

We investigate quantum entanglement between two (spin-1/2) fermions inside a cylindrical harmonic trap, making use of the von Neumann entropy for the reduced single particle density matrix as the pure state entanglement measure. We explore the dependence of pair entanglement on the geometry and strength of the trap and on the strength of the pairing interaction over the complete range of the effective BCS to BEC crossover. Our result elucidates an interesting connection between our model system of two fermions and that of two interacting bosons.Comment: to appear in PR

Globally Optimal Crowdsourcing Quality Management

We study crowdsourcing quality management, that is, given worker responses to a set of tasks, our goal is to jointly estimate the true answers for the tasks, as well as the quality of the workers. Prior work on this problem relies primarily on applying Expectation-Maximization (EM) on the underlying maximum likelihood problem to estimate true answers as well as worker quality. Unfortunately, EM only provides a locally optimal solution rather than a globally optimal one. Other solutions to the problem (that do not leverage EM) fail to provide global optimality guarantees as well. In this paper, we focus on filtering, where tasks require the evaluation of a yes/no predicate, and rating, where tasks elicit integer scores from a finite domain. We design algorithms for finding the global optimal estimates of correct task answers and worker quality for the underlying maximum likelihood problem, and characterize the complexity of these algorithms. Our algorithms conceptually consider all mappings from tasks to true answers (typically a very large number), leveraging two key ideas to reduce, by several orders of magnitude, the number of mappings under consideration, while preserving optimality. We also demonstrate that these algorithms often find more accurate estimates than EM-based algorithms. This paper makes an important contribution towards understanding the inherent complexity of globally optimal crowdsourcing quality management