486 research outputs found
Optimization in random field Ising models by quantum annealing
We investigate the properties of quantum annealing applied to the random
field Ising model in one, two and three dimensions. The decay rate of the
residual energy, defined as the energy excess from the ground state, is find to
be with in the range ,
depending on the strength of the random field. Systems with ``large clusters''
are harder to optimize as measured by . Our numerical results suggest
that in the ordered phase whereas in the paramagnetic phase the
annealing procedure can be tuned so that .Comment: 7 pages (2 columns), 9 figures, published with minor changes, one
reference updated after the publicatio
Elastic lines on splayed columnar defects studied numerically
We investigate by exact optimization method properties of two- and
three-dimensional systems of elastic lines in presence of splayed columnar
disorder. The ground state of many lines is separable both in 2d and 3d leading
to a random walk -like roughening in 2d and ballistic behavior in 3d.
Furthermore, we find that in the case of pure splayed columnar disorder in
contrast to point disorder there is no entanglement transition in 3d.
Entanglement can be triggered by perturbing the pure splay system with point
defects.Comment: 9 pages, 11 figures. Accepted for publication in PR
Maximum likelihood reconstruction for Ising models with asynchronous updates
We describe how the couplings in an asynchronous kinetic Ising model can be
inferred. We consider two cases, one in which we know both the spin history and
the update times and one in which we only know the spin history. For the first
case, we show that one can average over all possible choices of update times to
obtain a learning rule that depends only on spin correlations and can also be
derived from the equations of motion for the correlations. For the second case,
the same rule can be derived within a further decoupling approximation. We
study all methods numerically for fully asymmetric Sherrington-Kirkpatrick
models, varying the data length, system size, temperature, and external field.
Good convergence is observed in accordance with the theoretical expectations
Network inference using asynchronously updated kinetic Ising Model
Network structures are reconstructed from dynamical data by respectively
naive mean field (nMF) and Thouless-Anderson-Palmer (TAP) approximations. For
TAP approximation, we use two methods to reconstruct the network: a) iteration
method; b) casting the inference formula to a set of cubic equations and
solving it directly. We investigate inference of the asymmetric Sherrington-
Kirkpatrick (S-K) model using asynchronous update. The solutions of the sets
cubic equation depend of temperature T in the S-K model, and a critical
temperature Tc is found around 2.1. For T < Tc, the solutions of the cubic
equation sets are composed of 1 real root and two conjugate complex roots while
for T > Tc there are three real roots. The iteration method is convergent only
if the cubic equations have three real solutions. The two methods give same
results when the iteration method is convergent. Compared to nMF, TAP is
somewhat better at low temperatures, but approaches the same performance as
temperature increase. Both methods behave better for longer data length, but
for improvement arises, TAP is well pronounced.Comment: 6 pages, 4 figure
Complex networks created by aggregation
We study aggregation as a mechanism for the creation of complex networks. In
this evolution process vertices merge together, which increases the number of
highly connected hubs. We study a range of complex network architectures
produced by the aggregation. Fat-tailed (in particular, scale-free)
distributions of connections are obtained both for networks with a finite
number of vertices and growing networks. We observe a strong variation of a
network structure with growing density of connections and find the phase
transition of the condensation of edges. Finally, we demonstrate the importance
of structural correlations in these networks.Comment: 12 pages, 13 figure
Estimating informal care inputs associated with EQ-5D for use in economic evaluation.
OBJECTIVES: This paper estimates informal care need using the health of the patient. The results can be used to predict changes in informal care associated with changes in the health of the patient measured using EQ-5D. METHODS: Data was used from a prospective survey of inpatients containing 59,512 complete responses across 44,494 individuals. The number of days a friend or relative has needed to provide care or help with normal activities in the last 6 weeks was estimated using the health of the patient measured by EQ-5D, ICD chapter and other health and sociodemographic data. A variety of different regression models were estimated that are appropriate for the distribution of the informal care dependent variable, which has large spikes at 0 (zero informal care) and 42 days (informal care every day). RESULTS: The preferred model that most accurately predicts the distribution of the data is the zero-inflated negative binomial with variable inflation. The results indicate that improving the health of the patient reduces informal care need. The relationship between ICD chapter and informal care need is not as clear. CONCLUSIONS: The preferred zero-inflated negative binomial with variable inflation model can be used to predict changes in informal care associated with changes in the health of the patient measured using EQ-5D and these results can be applied to existing datasets to inform economic evaluation. Limitations include recall bias and response bias of the informal care data, and restrictions of the dataset to exclude some patient groups
The effect of thresholding on temporal avalanche statistics
We discuss intermittent time series consisting of discrete bursts or
avalanches separated by waiting or silent times. The short time correlations
can be understood to follow from the properties of individual avalanches, while
longer time correlations often present in such signals reflect correlations
between triggerings of different avalanches. As one possible source of the
latter kind of correlations in experimental time series, we consider the effect
of a finite detection threshold, due to e.g. experimental noise that needs to
be removed. To this end, we study a simple toy model of an avalanche, a random
walk returning to the origin or a Brownian bridge, in the presence and absence
of superimposed delta-correlated noise. We discuss the properties after
thresholding of artificial timeseries obtained by mixing toy avalanches and
waiting times from a Poisson process. Most of the resulting scalings for
individual avalanches and the composite timeseries can be understood via random
walk theory, except for the waiting time distributions when strong additional
noise is added. Then, to compare with a more complicated case we study the
Manna sandpile model of self-organized criticality, where some further
complications appear.Comment: 15 pages, 12 figures, submitted to J. Stat. Mech., special issue of
the UPoN2008 conferenc
Measuring functional renormalization group fixed-point functions for pinned manifolds
Exact numerical minimization of interface energies is used to test the
functional renormalization group (FRG) analysis for interfaces pinned by
quenched disorder. The fixed-point function R(u) (the correlator of the
coarse-grained disorder) is computed. In dimensions D=d+1, a linear cusp in
R''(u) is confirmed for random bond (d=1,2,3), random field (d=0,2,3), and
periodic (d=2,3) disorders. The functional shocks that lead to this cusp are
seen. Small, but significant, deviations from 1-loop FRG results are compared
to 2-loop corrections. The cross-correlation for two copies of disorder is
compared with a recent FRG study of chaos.Comment: 4 pages, 4 figure
Fluctuations and scaling in creep deformation
The spatial fluctuations of deformation are studied in creep in the Andrade's
power-law and the logarithmic phases, using paper samples. Measurements by the
Digital Image Correlation technique show that the relative strength of the
strain rate fluctuations increases with time, in both creep regimes. In the
Andrade creep phase characterized by a power law decay of the strain rate
, with , the fluctuations obey
, with . The local
deformation follows a data collapse appropriate for an absorbing
state/depinning transition. Similar behavior is found in a crystal plasticity
model, with a jamming or yielding phase transition
noise and avalanche scaling in plastic deformation
We study the intermittency and noise of dislocation systems undergoing shear
deformation. Simulations of a simple two-dimensional discrete dislocation
dynamics model indicate that the deformation rate exhibits a power spectrum
scaling of the type . The noise exponent is far away from a
Lorentzian, with . This result is directly related to the
way the durations of avalanches of plastic deformation activity scale with
their size.Comment: 6 pages, 5 figures, submitted to Phys. Rev.
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