103 research outputs found
Stochastic Resonance and Dynamic First-Order Pseudo-Phase Transitions in the Irreversible Growth of Thin Films under Spatially Periodic Magnetic Fields
We study the irreversible growth of magnetic thin films under the influence
of spatially periodic fields by means of extensive Monte Carlo simulations. We
find first-order pseudo-phase transitions that separate a dynamically
disordered phase from a dynamically ordered phase. By analogy with
time-dependent oscillating fields applied to Ising-type models, we
qualitatively associate this dynamic transition with the
localization/delocalization transition of "spatial hysteresis" loops. Depending
on the relative width of the magnetic film, , compared to the wavelength of
the external field, , different transition regimes are observed. For
small systems (), the transition is associated with the Standard
Stochastic Resonance regime, while, for large systems (), the
transition is driven by Anomalous Stochastic Resonance. The origin of the
latter is identified as due to the emergence of an additional relevant
lengthscale, namely the roughness of the spin domain switching interface. The
distinction between different stochastic resonance regimes is discussed at
length, both qualitatively by means of snapshot configurations, as well as
quantitatively via residence-length and order-parameter probability
distributions.Comment: 21 pages, 8 figures. To appear in Phys. Rev.
Non-equilibrium wetting transition in a magnetic Eden model
Magnetic Eden clusters with ferromagnetic interaction between
nearest-neighbor spins are grown in a confined 2d-geometry with short range
magnetic fields acting on the surfaces. The change of the growing interface
curvature driven by the field and the temperature is identified as a
non-equilibrium wetting transition and the corresponding phase diagram is
evaluated.Comment: 11 pages, 6 figure
Far-from-equilibrium growth of thin films in a temperature gradient
The irreversible growth of thin films under far-from-equilibrium conditions
is studied in dimensional strip geometries. Across one of the
transverse directions, a temperature gradient is applied by thermal baths at
fixed temperatures between and , where and
is the critical temperature of the system in contact with
an homogeneous thermal bath. By using standard finite-size scaling methods, we
characterized a continuous order-disorder phase transition driven by the
thermal bath gradient with critical temperature and critical
exponents , , and , which belong
to a different universality class from that of films grown in an homogeneous
bath. Furthermore, the effects of the temperature gradient are analyzed by
means of a bond model that captures the growth dynamics. The interplay of
geometry and thermal bath asymmetries leads to growth bond flux asymmetries and
the onset of transverse ordering effects that explain qualitatively the shift
in the critical temperature.Comment: 4 pages, 4 figures. arXiv admin note: substantial text overlap with
arXiv:1207.253
Understanding Health and Disease with Multidimensional Single-Cell Methods
Current efforts in the biomedical sciences and related interdisciplinary
fields are focused on gaining a molecular understanding of health and disease,
which is a problem of daunting complexity that spans many orders of magnitude
in characteristic length scales, from small molecules that regulate cell
function to cell ensembles that form tissues and organs working together as an
organism. In order to uncover the molecular nature of the emergent properties
of a cell, it is essential to measure multiple cell components simultaneously
in the same cell. In turn, cell heterogeneity requires multiple cells to be
measured in order to understand health and disease in the organism. This review
summarizes current efforts towards a data-driven framework that leverages
single-cell technologies to build robust signatures of healthy and diseased
phenotypes. While some approaches focus on multicolor flow cytometry data and
other methods are designed to analyze high-content image-based screens, we
emphasize the so-called Supercell/SVM paradigm (recently developed by the
authors of this review and collaborators) as a unified framework that captures
mesoscopic-scale emergence to build reliable phenotypes. Beyond their specific
contributions to basic and translational biomedical research, these efforts
illustrate, from a larger perspective, the powerful synergy that might be
achieved from bringing together methods and ideas from statistical physics,
data mining, and mathematics to solve the most pressing problems currently
facing the life sciences.Comment: 25 pages, 7 figures; revised version with minor changes. To appear in
J. Phys.: Cond. Mat
Criticality and the Onset of Ordering in the Standard Vicsek Model
Experimental observations of animal collective behavior have shown stunning
evidence for the emergence of large-scale cooperative phenomena resembling
phase transitions in physical systems. Indeed, quantitative studies have found
scale-free correlations and critical behavior consistent with the occurrence of
continuous, second-order phase transitions. The Standard Vicsek Model (SVM), a
minimal model of self-propelled particles in which their tendency to align with
each other competes with perturbations controlled by a noise term, appears to
capture the essential ingredients of critical flocking phenomena. In this
paper, we review recent finite-size scaling and dynamical studies of the SVM,
which present a full characterization of the continuous phase transition
through dynamical and critical exponents. We also present a complex network
analysis of SVM flocks and discuss the onset of ordering in connection with
XY-like spin models.Comment: 15 pages, 4 figures. To appear in Interface Focu
Complex Network Structure of Flocks in the Standard Vicsek Model
In flocking models, the collective motion of self-driven individuals leads to
the formation of complex spatiotemporal patterns. The Standard Vicsek Model
(SVM) considers individuals that tend to adopt the direction of movement of
their neighbors under the influence of noise. By performing an extensive
complex network characterization of the structure of SVM flocks, we show that
flocks are highly clustered, assortative, and non-hierarchical networks with
short-tailed degree distributions. Moreover, we also find that the SVM dynamics
leads to the formation of complex structures with an effective dimension higher
than that of the space where the actual displacements take place. Furthermore,
we show that these structures are capable of sustaining mean-field-like
orientationally ordered states when the displacements are suppressed, thus
suggesting a linkage between the onset of order and the enhanced dimensionality
of SVM flocks.Comment: 26 pages, 11 figures. To appear in J. Stat. Phy
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