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, $L$, compared to the wavelength of the external field, $\lambda$, different transition regimes are observed. For small systems ($L<\lambda$), the transition is associated with the Standard Stochastic Resonance regime, while, for large systems ($L>\lambda$), 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 $(2+1)-$dimensional strip geometries. Across one of the transverse directions, a temperature gradient is applied by thermal baths at fixed temperatures between $T_1$ and $T_2$, where $T_1<T_c^{hom}<T_2$ and $T_c^{hom}=0.69(1)$ 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 $T_c=0.84(2)$ and critical exponents $\nu=1.53(6)$, $\gamma=2.54(11)$, and $\beta=0.26(8)$, 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