1,334,142 research outputs found
Modeling Barkhausen Noise in Magnetic Glasses with Dipole-Dipole Interactions
Long-ranged dipole-dipole interactions in magnetic glasses give rise to
magnetic domains having labyrinthine patterns. Barkhausen Noise is then
expected to result from the movement of domain boundaries which is supposed to
be modeled by the motion of elastic membranes with random pinning. We propose
an atomistic model of such magnetic glasses in which we measure the Barkhausen
Noise which indeed results from the movement of domain boundaries. Nevertheless
the statistics of the Barkhausen Noise is found in striking disagreement with
the expectations in the literature. In fact we find exponential statistics
without any power law, stressing the fact that Barkhausen Noise can belong to
very different universality classes. In this glassy system the essence of the
phenomenon is the ability of spin-carrying particles to move and minimize the
energy without any spin flip. A theory is offered in excellent agreement with
the measured data without any free parameter.Comment: 5 Pages, 5 Figures, Submitted to EP
Control and Characterization of Individual Grains and Grain Boundaries in Graphene Grown by Chemical Vapor Deposition
The strong interest in graphene has motivated the scalable production of high
quality graphene and graphene devices. Since large-scale graphene films
synthesized to date are typically polycrystalline, it is important to
characterize and control grain boundaries, generally believed to degrade
graphene quality. Here we study single-crystal graphene grains synthesized by
ambient CVD on polycrystalline Cu, and show how individual boundaries between
coalescing grains affect graphene's electronic properties. The graphene grains
show no definite epitaxial relationship with the Cu substrate, and can cross Cu
grain boundaries. The edges of these grains are found to be predominantly
parallel to zigzag directions. We show that grain boundaries give a significant
Raman "D" peak, impede electrical transport, and induce prominent weak
localization indicative of intervalley scattering in graphene. Finally, we
demonstrate an approach using pre-patterned growth seeds to control graphene
nucleation, opening a route towards scalable fabrication of single-crystal
graphene devices without grain boundaries.Comment: New version with additional data. Accepted by Nature Material
Dynamic Modeling and Simulation of a Real World Billiard
Gravitational billiards provide an experimentally accessible arena for
testing formulations of nonlinear dynamics. We present a mathematical model
that captures the essential dynamics required for describing the motion of a
realistic billiard for arbitrary boundaries. Simulations of the model are
applied to parabolic, wedge and hyperbolic billiards that are driven
sinusoidally. Direct comparisons are made between the model's predictions and
previously published experimental data. It is shown that the data can be
successfully modeled with a simple set of parameters without an assumption of
exotic energy dependence.Comment: 10 pages, 3 figure
Hierarchical Object Parsing from Structured Noisy Point Clouds
Object parsing and segmentation from point clouds are challenging tasks
because the relevant data is available only as thin structures along object
boundaries or other features, and is corrupted by large amounts of noise. To
handle this kind of data, flexible shape models are desired that can accurately
follow the object boundaries. Popular models such as Active Shape and Active
Appearance models lack the necessary flexibility for this task, while recent
approaches such as the Recursive Compositional Models make model
simplifications in order to obtain computational guarantees. This paper
investigates a hierarchical Bayesian model of shape and appearance in a
generative setting. The input data is explained by an object parsing layer,
which is a deformation of a hidden PCA shape model with Gaussian prior. The
paper also introduces a novel efficient inference algorithm that uses informed
data-driven proposals to initialize local searches for the hidden variables.
Applied to the problem of object parsing from structured point clouds such as
edge detection images, the proposed approach obtains state of the art parsing
errors on two standard datasets without using any intensity information.Comment: 13 pages, 16 figure
Kernel Density Estimation with Linked Boundary Conditions
Kernel density estimation on a finite interval poses an outstanding challenge
because of the well-recognized bias at the boundaries of the interval.
Motivated by an application in cancer research, we consider a boundary
constraint linking the values of the unknown target density function at the
boundaries. We provide a kernel density estimator (KDE) that successfully
incorporates this linked boundary condition, leading to a non-self-adjoint
diffusion process and expansions in non-separable generalized eigenfunctions.
The solution is rigorously analyzed through an integral representation given by
the unified transform (or Fokas method). The new KDE possesses many desirable
properties, such as consistency, asymptotically negligible bias at the
boundaries, and an increased rate of approximation, as measured by the AMISE.
We apply our method to the motivating example in biology and provide numerical
experiments with synthetic data, including comparisons with state-of-the-art
KDEs (which currently cannot handle linked boundary constraints). Results
suggest that the new method is fast and accurate. Furthermore, we demonstrate
how to build statistical estimators of the boundary conditions satisfied by the
target function without apriori knowledge. Our analysis can also be extended to
more general boundary conditions that may be encountered in applications
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