116,979 research outputs found
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 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
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