323,595 research outputs found
Random walk driven by the simple exclusion process
We prove a strong law of large numbers and an annealed invariance principle
for a random walk in a one-dimensional dynamic random environment evolving as
the simple exclusion process with jump parameter . First, we establish
that if the asymptotic velocity of the walker is non-zero in the limiting case
"", where the environment gets fully refreshed between each
step of the walker, then, for large enough, the walker still has a
non-zero asymptotic velocity in the same direction. Second, we establish that
if the walker is transient in the limiting case , then, for
small enough but positive, the walker has a non-zero asymptotic
velocity in the direction of the transience. These two limiting velocities can
sometimes be of opposite sign. In all cases, we show that the fluctuations are
normal.Comment: v2 -> v3: Figures and heuristic comments added. Various typos
correcte
Multi-Paradigm Reasoning for Access to Heterogeneous GIS
Accessing and querying geographical data in a uniform way has become easier in recent years. Emerging standards like WFS turn
the web into a geospatial web services enabled place. Mediation
architectures like VirGIS overcome syntactical and semantical heterogeneity
between several distributed sources. On mobile devices,
however, this kind of solution is not suitable, due to limitations,
mostly regarding bandwidth, computation power, and available storage
space. The aim of this paper is to present a solution for providing
powerful reasoning mechanisms accessible from mobile applications
and involving data from several heterogeneous sources.
By adapting contents to time and location, mobile web information
systems can not only increase the value and suitability of the
service itself, but can substantially reduce the amount of data delivered
to users. Because many problems pertain to infrastructures
and transportation in general and to way finding in particular, one
cornerstone of the architecture is higher level reasoning on graph
networks with the Multi-Paradigm Location Language MPLL. A
mediation architecture is used as a âgraph providerâ in order to
transfer the load of computation to the best suited component â
graph construction and transformation for example being heavy on
resources. Reasoning in general can be conducted either near the
âsourceâ or near the end user, depending on the specific use case.
The concepts underlying the proposal described in this paper are
illustrated by a typical and concrete scenario for web applications
Gradient Scan Gibbs Sampler: an efficient algorithm for high-dimensional Gaussian distributions
This paper deals with Gibbs samplers that include high dimensional
conditional Gaussian distributions. It proposes an efficient algorithm that
avoids the high dimensional Gaussian sampling and relies on a random excursion
along a small set of directions. The algorithm is proved to converge, i.e. the
drawn samples are asymptotically distributed according to the target
distribution. Our main motivation is in inverse problems related to general
linear observation models and their solution in a hierarchical Bayesian
framework implemented through sampling algorithms. It finds direct applications
in semi-blind/unsupervised methods as well as in some non-Gaussian methods. The
paper provides an illustration focused on the unsupervised estimation for
super-resolution methods.Comment: 18 page
Coffee-stain growth dynamics on dry and wet surfaces
The drying of a drop containing particles often results in the accumulation
of the particles at the contact line. In this work, we investigate the drying
of an aqueous colloidal drop surrounded by a hydrogel that is also evaporating.
We combine theoretical and experimental studies to understand how the
surrounding vapor concentration affects the particle deposit during the
constant radius evaporation mode. In addition to the common case of evaporation
on an otherwise dry surface, we show that in a configuration where liquid is
evaporating from a flat surface around the drop, the singularity of the
evaporative flux at the contact line is suppressed and the drop evaporation is
homogeneous. For both conditions, we derive the velocity field and we establish
the temporal evolution of the number of particles accumulated at the contact
line. We predict the growth dynamics of the stain and the drying timescales.
Thus, dry and wet conditions are compared with experimental results and we
highlight that only the dynamics is modified by the evaporation conditions, not
the final accumulation at the contact line
Independent Resampling Sequential Monte Carlo Algorithms
Sequential Monte Carlo algorithms, or Particle Filters, are Bayesian
filtering algorithms which propagate in time a discrete and random
approximation of the a posteriori distribution of interest. Such algorithms are
based on Importance Sampling with a bootstrap resampling step which aims at
struggling against weights degeneracy. However, in some situations (informative
measurements, high dimensional model), the resampling step can prove
inefficient. In this paper, we revisit the fundamental resampling mechanism
which leads us back to Rubin's static resampling mechanism. We propose an
alternative rejuvenation scheme in which the resampled particles share the same
marginal distribution as in the classical setup, but are now independent. This
set of independent particles provides a new alternative to compute a moment of
the target distribution and the resulting estimate is analyzed through a CLT.
We next adapt our results to the dynamic case and propose a particle filtering
algorithm based on independent resampling. This algorithm can be seen as a
particular auxiliary particle filter algorithm with a relevant choice of the
first-stage weights and instrumental distributions. Finally we validate our
results via simulations which carefully take into account the computational
budget
Normalized information-based divergences
This paper is devoted to the mathematical study of some divergences based on
the mutual information well-suited to categorical random vectors. These
divergences are generalizations of the "entropy distance" and "information
distance". Their main characteristic is that they combine a complexity term and
the mutual information. We then introduce the notion of (normalized)
information-based divergence, propose several examples and discuss their
mathematical properties in particular in some prediction framework.Comment: 36 page
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