12,574 research outputs found
Magnetic Susceptibility of the Quark Condensate and Polarization from Chiral Models
We compute the magnetic susceptibility of the quark condensate and the
polarization of quarks at zero temperature and in a uniform magnetic
background. Our theoretical framework consists of two chiral models that allow
to treat self-consistently the spontaneous breaking of chiral symmetry: the
linear model coupled to quarks, dubbed quark-meson model, and the
Nambu-Jona-Lasinio model. We also perform analytic estimates of the same
quantities within the renormalized quark-meson model, both in the regimes of
weak and strong fields. Our numerical results are in agreement with the recent
literature; moreover, we confirm previous Lattice findings, related to the
saturation of the polarization at large fields.Comment: 13 pages, 4 figure
Extinction in neutrally stable stochastic Lotka-Volterra models
Populations of competing biological species exhibit a fascinating interplay
between the nonlinear dynamics of evolutionary selection forces and random
fluctuations arising from the stochastic nature of the interactions. The
processes leading to extinction of species, whose understanding is a key
component in the study of evolution and biodiversity, are influenced by both of
these factors.
In this paper, we investigate a class of stochastic population dynamics
models based on generalized Lotka-Volterra systems. In the case of neutral
stability of the underlying deterministic model, the impact of intrinsic noise
on the survival of species is dramatic: it destroys coexistence of interacting
species on a time scale proportional to the population size. We introduce a new
method based on stochastic averaging which allows one to understand this
extinction process quantitatively by reduction to a lower-dimensional effective
dynamics. This is performed analytically for two highly symmetrical models and
can be generalized numerically to more complex situations. The extinction
probability distributions and other quantities of interest we obtain show
excellent agreement with simulations.Comment: 14 pages, 7 figure
alpha_s and the tau hadronic width: fixed-order, contour-improved and higher-order perturbation theory
The determination of from hadronic decays is revisited,
with a special emphasis on the question of higher-order perturbative
corrections and different possibilities of resumming the perturbative series
with the renormalisation group: fixed-order (FOPT) vs. contour-improved
perturbation theory (CIPT). The difference between these approaches has evolved
into a systematic effect that does not go away as higher orders in the
perturbative expansion are added. We attempt to clarify under which
circumstances one or the other approach provides a better approximation to the
true result. To this end, we propose to describe the Adler function series by a
model that includes the exactly known coefficients and theoretical constraints
on the large-order behaviour originating from the operator product expansion
and the renormalisation group. Within this framework we find that while CIPT is
unable to account for the fully resummed series, FOPT smoothly approaches the
Borel sum, before the expected divergent behaviour sets in at even higher
orders. Employing FOPT up to the fifth order to determine in the
\MSb scheme, we obtain ,
corresponding to . Improving
this result by including yet higher orders from our model yields
, which after evolution leads to
. Our results are lower than previous values
obtained from decays.Comment: 42 pages, 9 figures; appendix on Adler function in the complex plane
added. Version to appear in JHE
Dominance-based Rough Set Approach, basic ideas and main trends
Dominance-based Rough Approach (DRSA) has been proposed as a machine learning
and knowledge discovery methodology to handle Multiple Criteria Decision Aiding
(MCDA). Due to its capacity of asking the decision maker (DM) for simple
preference information and supplying easily understandable and explainable
recommendations, DRSA gained much interest during the years and it is now one
of the most appreciated MCDA approaches. In fact, it has been applied also
beyond MCDA domain, as a general knowledge discovery and data mining
methodology for the analysis of monotonic (and also non-monotonic) data. In
this contribution, we recall the basic principles and the main concepts of
DRSA, with a general overview of its developments and software. We present also
a historical reconstruction of the genesis of the methodology, with a specific
focus on the contribution of Roman S{\l}owi\'nski.Comment: This research was partially supported by TAILOR, a project funded by
European Union (EU) Horizon 2020 research and innovation programme under GA
No 952215. This submission is a preprint of a book chapter accepted by
Springer, with very few minor differences of just technical natur
Investigation on soft computing techniques for airport environment evaluation systems
Spatial and temporal information exist widely in engineering fields, especially
in airport environmental management systems. Airport environment is influenced
by many different factors and uncertainty is a significant part of the
system. Decision support considering this kind of spatial and temporal information
and uncertainty is crucial for airport environment related engineering
planning and operation. Geographical information systems and computer aided
design are two powerful tools in supporting spatial and temporal information
systems. However, the present geographical information systems and computer
aided design software are still too general in considering the special features in
airport environment, especially for uncertainty. In this thesis, a series of parameters
and methods for neural network-based knowledge discovery and training
improvement are put forward, such as the relative strength of effect, dynamic
state space search strategy and compound architecture. [Continues.
Geometric scaling in exclusive processes
We show that according to the present understanding of the energy evolution
of the observables measured in deep-inelastic scattering, the photon-proton
scattering amplitude has to exhibit geometric scaling at each impact parameter.
We suggest a way to test it experimentally at HERA. A qualitative analysis
based on published data is presented and discussed.Comment: 9 pages, 2 figures. v2: references added, some points clarifie
Multi-objective worst case optimization by means of evolutionary algorithms
Many real-world optimization problems are subject to uncertainty. A possible goal is then to find a solution which is robust in the sense that it has the best worst-case performance over all possible scenarios. However, if the problem also involves mul- tiple objectives, which scenario is “best” or “worst” depends on the user’s weighting of the different criteria, which is generally difficult to specify before alternatives are known. Evolutionary multi-objective optimization avoids this problem by searching for the whole front of Pareto optimal solutions. This paper extends the concept of Pareto dominance to worst case optimization problems and demonstrates how evolu- tionary algorithms can be used for worst case optimization in a multi-objective setting
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