190 research outputs found
Dimensional study of the dynamical arrest in a random Lorentz gas
The random Lorentz gas is a minimal model for transport in heterogeneous
media. Upon increasing the obstacle density, it exhibits a growing subdiffusive
transport regime and then a dynamical arrest. Here, we study the dimensional
dependence of the dynamical arrest, which can be mapped onto the void
percolation transition for Poisson-distributed point obstacles. We numerically
determine the arrest in dimensions d=2-6. Comparing the results with standard
mode-coupling theory reveals that the dynamical theory prediction grows
increasingly worse with . In an effort to clarify the origin of this
discrepancy, we relate the dynamical arrest in the RLG to the dynamic glass
transition of the infinite-range Mari-Kurchan model glass former. Through a
mixed static and dynamical analysis, we then extract an improved dimensional
scaling form as well as a geometrical upper bound for the arrest. The results
suggest that understanding the asymptotic behavior of the random Lorentz gas
may be key to surmounting fundamental difficulties with the mode-coupling
theory of glasses.Comment: 9 pages, 6 figure
Model of random packings of different size balls
We develop a model to describe the properties of random assemblies of
polydisperse hard spheres. We show that the key features to describe the system
are (i) the dependence between the free volume of a sphere and the various
coordination numbers between the species, and (ii) the dependence of the
coordination numbers with the concentration of species; quantities that are
calculated analytically. The model predicts the density of random close packing
and random loose packing of polydisperse systems for a given distribution of
ball size and describes packings for any interparticle friction coefficient.
The formalism allows to determine the optimal packing over different
distributions and may help to treat packing problems of non-spherical particles
which are notoriously difficult to solve.Comment: 6 pages, 6 figure
Feature Enhancement Network: A Refined Scene Text Detector
In this paper, we propose a refined scene text detector with a \textit{novel}
Feature Enhancement Network (FEN) for Region Proposal and Text Detection
Refinement. Retrospectively, both region proposal with \textit{only} sliding-window feature and text detection refinement with \textit{single
scale} high level feature are insufficient, especially for smaller scene text.
Therefore, we design a new FEN network with \textit{task-specific},
\textit{low} and \textit{high} level semantic features fusion to improve the
performance of text detection. Besides, since \textit{unitary}
position-sensitive RoI pooling in general object detection is unreasonable for
variable text regions, an \textit{adaptively weighted} position-sensitive RoI
pooling layer is devised for further enhancing the detecting accuracy. To
tackle the \textit{sample-imbalance} problem during the refinement stage, we
also propose an effective \textit{positives mining} strategy for efficiently
training our network. Experiments on ICDAR 2011 and 2013 robust text detection
benchmarks demonstrate that our method can achieve state-of-the-art results,
outperforming all reported methods in terms of F-measure.Comment: 8 pages, 5 figures, 2 tables. This paper is accepted to appear in
AAAI 201
Hopping and the Stokes-Einstein relation breakdown in simple glass formers
One of the most actively debated issues in the study of the glass transition
is whether a mean-field description is a reasonable starting point for
understanding experimental glass formers. Although the mean-field theory of the
glass transition -- like that of other statistical systems -- is exact when the
spatial dimension , the evolution of systems properties
with may not be smooth. Finite-dimensional effects could dramatically
change what happens in physical dimensions, . For standard phase
transitions finite-dimensional effects are typically captured by
renormalization group methods, but for glasses the corrections are much more
subtle and only partially understood. Here, we investigate hopping between
localized cages formed by neighboring particles in a model that allows to
cleanly isolate that effect. By bringing together results from replica theory,
cavity reconstruction, void percolation, and molecular dynamics, we obtain
insights into how hopping induces a breakdown of the Stokes--Einstein relation
and modifies the mean-field scenario in experimental systems. Although hopping
is found to supersede the dynamical glass transition, it nonetheless leaves a
sizable part of the critical regime untouched. By providing a constructive
framework for identifying and quantifying the role of hopping, we thus take an
important step towards describing dynamic facilitation in the framework of the
mean-field theory of glasses.Comment: 27 pages, 13 figures (including supplementary information) - final
version accepted for publication on PNA
Statistical theory of correlations in random packings of hard particles
A random packing of hard particles represents a fundamental model for
granular matter. Despite its importance, analytical modeling of random packings
remains difficult due to the existence of strong correlations which preclude
the development of a simple theory. Here, we take inspiration from liquid
theories for the -particle angular correlation function to develop a
formalism of random packings of hard particles from the bottom-up. A
progressive expansion into a shell of particles converges in the large layer
limit under a Kirkwood-like approximation of higher-order correlations. We
apply the formalism to hard disks and predict the density of two-dimensional
random close packing (RCP), , and random loose
packing (RLP), . Our theory also predicts a phase
diagram and angular correlation functions that are in good agreement with
experimental and numerical data.Comment: 9 pages, 6 figures, to appear in PR
Thermodynamic crossovers in supercritical fluids
Can liquid-like and gas-like states be distinguished beyond the critical
point, where the liquid-gas phase transition no longer exists and
conventionally only a single supercritical fluid phase is defined? Recent
experiments and simulations report strong evidence of dynamical crossovers
above the critical temperature and pressure. Despite using different criteria,
existing theoretical explanations generally consider a single crossover line
separating liquid-like and gas-like states in the supercritical fluid phase. We
argue that such a single-line scenario is inconsistent with the supercritical
behavior of the Ising model, which has two crossover lines due to its symmetry,
violating the universality principle of critical phenomena. To reconcile the
inconsistency, we define two thermodynamic crossover lines in supercritical
fluids as boundaries of liquid-like, indistinguishable and gas-like states.
Near the critical point, the two crossover lines follow critical scalings with
exponents of the Ising universality class, supported by calculations of
theoretical models and analyses of experimental data from the standard
database. The upper line agrees with crossovers independently estimated from
the inelastic X-ray scattering data of supercritical argon, and from the
small-angle neutron scattering data of supercritical carbon dioxide. The lower
line is verified by the equation of states for the compressibility factor. This
work provides a fundamental framework for understanding supercritical physics
in general phase transitions.Comment: 23 pages, 21 figure
EnsNet: Ensconce Text in the Wild
A new method is proposed for removing text from natural images. The challenge
is to first accurately localize text on the stroke-level and then replace it
with a visually plausible background. Unlike previous methods that require
image patches to erase scene text, our method, namely ensconce network
(EnsNet), can operate end-to-end on a single image without any prior knowledge.
The overall structure is an end-to-end trainable FCN-ResNet-18 network with a
conditional generative adversarial network (cGAN). The feature of the former is
first enhanced by a novel lateral connection structure and then refined by four
carefully designed losses: multiscale regression loss and content loss, which
capture the global discrepancy of different level features; texture loss and
total variation loss, which primarily target filling the text region and
preserving the reality of the background. The latter is a novel local-sensitive
GAN, which attentively assesses the local consistency of the text erased
regions. Both qualitative and quantitative sensitivity experiments on synthetic
images and the ICDAR 2013 dataset demonstrate that each component of the EnsNet
is essential to achieve a good performance. Moreover, our EnsNet can
significantly outperform previous state-of-the-art methods in terms of all
metrics. In addition, a qualitative experiment conducted on the SMBNet dataset
further demonstrates that the proposed method can also preform well on general
object (such as pedestrians) removal tasks. EnsNet is extremely fast, which can
preform at 333 fps on an i5-8600 CPU device.Comment: 8 pages, 8 figures, 2 tables, accepted to appear in AAAI 201
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