4,256 research outputs found
Effective transport barriers in nontwist systems
In fluids and plasmas with zonal flow reversed shear, a peculiar kind of transport barrier appears in the shearless region, one that is associated with a proper route of transition to chaos. These barriers have been identified in symplectic nontwist maps that model such zonal flows. We use the so-called standard nontwist map, a paradigmatic example of nontwist systems, to analyze the parameter dependence of the transport through a broken shearless barrier. On varying a proper control parameter, we identify the onset of structures with high stickiness that give rise to an effective barrier near the broken shearless curve. Moreover, we show how these stickiness structures, and the concomitant transport reduction in the shearless region, are determined by a homoclinic tangle of the remaining dominant twin island chains. We use the finite-time rotation number, a recently proposed diagnostic, to identify transport barriers that separate different regions of stickiness. The identified barriers are comparable to those obtained by using finite-time Lyapunov exponents.FAPESPCNPqCAPESMCT/CNEN (Rede Nacional de Fusao)Fundacao AraucariaUS Department of Energy DE-FG05-80ET-53088Physic
Intermingled basins in coupled Lorenz systems
We consider a system of two identical linearly coupled Lorenz oscillators,
presenting synchro- nization of chaotic motion for a specified range of the
coupling strength. We verify the existence of global synchronization and
antisynchronization attractors with intermingled basins of attraction, such
that the basin of one attractor is riddled with holes belonging to the basin of
the other attractor and vice versa. We investigated this phenomenon by
verifying the fulfillment of the mathematical requirements for intermingled
basins, and also obtained scaling laws that characterize quantitatively the
riddling of both basins for this system
Parametric Amplification in the Dynamic Radiation Force of Acoustic Waves in Fluids
We report on parametric amplification in dynamic radiation force produced by
a bichromatic acoustic beam in a fluid. To explain this effect we develop a
theory taking into account the nonlinearity of the fluid. The theory is
validated through an experiment to measure the dynamic radiation force on an
acrylic sphere. Results exhibit an amplification of 66 dB in water and 80 dB in
alcohol as the difference of the frequencies is increased from 10 Hz to 240
kHz
On the quantumness of correlations in nuclear magnetic resonance
Nuclear Magnetic Resonance (NMR) was successfully employed to test several
protocols and ideas in Quantum Information Science. In most of these
implementations the existence of entanglement was ruled out. This fact
introduced concerns and questions about the quantum nature of such bench tests.
In this article we address some issues related to the non-classical aspects of
NMR systems. We discuss some experiments where the quantum aspects of this
system are supported by quantum correlations of separable states. Such
quantumness, beyond the entanglement-separability paradigm, is revealed via a
departure between the quantum and the classical versions of information theory.
In this scenario, the concept of quantum discord seems to play an important
role. We also present an experimental implementation of an analogous of the
single-photon Mach-Zehnder interferometer employing two nuclear spins to encode
the interferometric paths. This experiment illustrate how non-classical
correlations of separable states may be used to simulate quantum dynamics. The
results obtained are completely equivalent to the optical scenario, where
entanglement (between two field modes) may be present
Determinação de limites críticos de densidade de solo e de umidade para o desenvolvimento de cultivares de sorgo, com vistas à identificação de fontes de tolerância a esses estresses.
Statics and dynamics of an Ashkin-Teller neural network with low loading
An Ashkin-Teller neural network, allowing for two types of neurons is
considered in the case of low loading as a function of the strength of the
respective couplings between these neurons. The storage and retrieval of
embedded patterns built from the two types of neurons, with different degrees
of (in)dependence is studied. In particular, thermodynamic properties including
the existence and stability of Mattis states are discussed. Furthermore, the
dynamic behaviour is examined by deriving flow equations for the macroscopic
overlap. It is found that for linked patterns the model shows better retrieval
properties than a corresponding Hopfield model.Comment: 20 pages, 6 figures, Latex with postscript figures in one tar.gz fil
Finite Size Effects in Separable Recurrent Neural Networks
We perform a systematic analytical study of finite size effects in separable
recurrent neural network models with sequential dynamics, away from saturation.
We find two types of finite size effects: thermal fluctuations, and
disorder-induced `frozen' corrections to the mean-field laws. The finite size
effects are described by equations that correspond to a time-dependent
Ornstein-Uhlenbeck process. We show how the theory can be used to understand
and quantify various finite size phenomena in recurrent neural networks, with
and without detailed balance.Comment: 24 pages LaTex, with 4 postscript figures include
The complex channel networks of bone structure
Bone structure in mammals involves a complex network of channels (Havers and
Volkmann channels) required to nourish the bone marrow cells. This work
describes how three-dimensional reconstructions of such systems can be obtained
and represented in terms of complex networks. Three important findings are
reported: (i) the fact that the channel branching density resembles a power law
implies the existence of distribution hubs; (ii) the conditional node degree
density indicates a clear tendency of connection between nodes with degrees 2
and 4; and (iii) the application of the recently introduced concept of
hierarchical clustering coefficient allows the identification of typical scales
of channel redistribution. A series of important biological insights is drawn
and discussedComment: 3 pages, 1 figure, The following article has been submitted to
Applied Physics Letters. If it is published, it will be found online at
http://apl.aip.org
Attractor Modulation and Proliferation in 1+ Dimensional Neural Networks
We extend a recently introduced class of exactly solvable models for
recurrent neural networks with competition between 1D nearest neighbour and
infinite range information processing. We increase the potential for further
frustration and competition in these models, as well as their biological
relevance, by adding next-nearest neighbour couplings, and we allow for
modulation of the attractors so that we can interpolate continuously between
situations with different numbers of stored patterns. Our models are solved by
combining mean field and random field techniques. They exhibit increasingly
complex phase diagrams with novel phases, separated by multiple first- and
second order transitions (dynamical and thermodynamic ones), and, upon
modulating the attractor strengths, non-trivial scenarios of phase diagram
deformation. Our predictions are in excellent agreement with numerical
simulations.Comment: 16 pages, 15 postscript figures, Late
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