180 research outputs found
Reductions and deviations for stochastic partial differential equations under fast dynamical boundary conditions
In order to understand the impact of random influences at physical boundary
on the evolution of multiscale systems, a stochastic partial differential
equation model under a fast random dynamical boundary condition is
investigated. The noises in the model and in the boundary condition are both
additive. An effective equation is derived and justified by reducing the random
\emph{dynamical} boundary condition to a simpler one. The effective system is
still a stochastic partial differential equation. Furthermore, the quantitative
comparison between the solution of the original stochastic system and the
effective solution is provided by establishing normal deviations and large
deviations principles. Namely, the normal deviations are asymptotically
characterized, while the rate and speed of the large deviations are estimated.Comment: This is a revised version with 29 pages. To appear in Stochastic
Analysis and Applications, 200
Geometric Methods for Stochastic Dynamical Systems
Noisy fluctuations are ubiquitous in complex systems. They play a crucial or
delicate role in the dynamical evolution of gene regulation, signal
transduction, biochemical reactions, among other systems. Therefore, it is
essential to consider the effects of noise on dynamical systems. It has been a
challenging topic to have better understanding of the impact of the noise on
the dynamical behaviors of complex systems.Comment: 8 page
Dynamics of the Thermohaline Circulation Under Uncertainty
The ocean thermohaline circulation under uncertainty is investigated by a
random dynamical systems approach. It is shown that the asymptotic dynamics of
the thermohaline circulation is described by a random attractor and by a system
with finite degrees of freedom.Comment: 15 page
Compactly Generated Shape Index Theory and its Application to a Retarded Nonautonomous Parabolic Equation
We establish the compactly generated shape (H-shape) index theory for local
semiflows on complete metric spaces via more general shape index pairs, and
define the H-shape cohomology index to develop the Morse equations. The main
advantages are that the quotient space is not necessarily metrizable for
the shape index pair and N\sm E need not to be a neighborhood of the
compact invariant set. Moreover, in this new theory, the phase space is not
required to be separable. We apply H-shape index theory to an abstract retarded
nonautonomous parabolic equation to obtain the existence of bounded full
solutions
Mean exit time for stochastic dynamical systems driven by tempered stable L\'evy fluctuations
We use the mean exit time to quantify macroscopic dynamical behaviors of
stochastic dynamical systems driven by tempered L\'evy fluctuations, which are
solutions of nonlocal elliptic equations. Firstly, we construct a new numerical
scheme to compute and solve the mean exit time associated with the one
dimensional stochastic system. Secondly, we extend the analytical and numerical
results to two dimensional case: horizontal-vertical and isotropic case.
Finally, we verify the effectiveness of the presented schemes with numerical
experiments in several examples
Ergodic Dynamics of the Stochastic Swift-Hohenberg System
The Swift-Hohenberg fluid convection system with both local and nonlocal
nonlinearities under the influence of white noise is studied. The objective is
to understand the difference in the dynamical behavior in both local and
nonlocal cases. It is proved that when sufficiently many of its Fourier modes
are forced, the system has a unique invariant measure, or equivalently, the
dynamics is ergodic. Moreover, it is found that the number of modes to be
stochastically excited for ensuring the ergodicity in the local Swift-Hohenberg
system depends {\em only} on the Rayleigh number (i.e., it does not even depend
on the random term itself), while this number for the nonlocal Swift-Hohenberg
system relies additionally on the bound of the kernel in the nonlocal
interaction (integral) term, and on the random term itselfComment: Version: Oct 9, 2003; accepted August 18, 200
Large deviations for slow-fast stochastic partial differential equations
A large deviation principle is derived for stochastic partial differential
equations with slow-fast components. The result shows that the rate function is
exactly that of the averaged equation plus the fluctuating deviation which is a
stochastic partial differential equation with small Gaussian perturbation. This
also confirms the effectiveness of the approximation of the averaged equation
plus the fluctuating deviation to the slow-fast stochastic partial differential
equations.Comment: 30 page
Most Probable Evolution Trajectories in a Genetic Regulatory System Excited by Stable L\'evy Noise
We study the most probable trajectories of the concentration evolution for
the transcription factor activator in a genetic regulation system, with
non-Gaussian stable L\'evy noise in the synthesis reaction rate taking into
account. We calculate the most probable trajectory by spatially maximizing the
probability density of the system path, i.e., the solution of the associated
nonlocal Fokker-Planck equation. We especially examine those most probable
trajectories from low concentration state to high concentration state (i.e.,
the likely transcription regime) for certain parameters, in order to gain
insights into the transcription processes and the tipping time for the
transcription likely to occur. This enables us: (i) to visualize the progress
of concentration evolution (i.e., observe whether the system enters the
transcription regime within a given time period); (ii) to predict or avoid
certain transcriptions via selecting specific noise parameters in particular
regions in the parameter space. Moreover, we have found some peculiar or
counter-intuitive phenomena in this gene model system, including (a) a smaller
noise intensity may trigger the transcription process, while a larger noise
intensity can not, under the same asymmetric L\'evy noise. This phenomenon does
not occur in the case of symmetric L\'evy noise; (b) the symmetric L\'evy
motion always induces transition to high concentration, but certain asymmetric
L\'evy motions do not trigger the switch to transcription. These findings
provide insights for further experimental research, in order to achieve or to
avoid specific gene transcriptions, with possible relevance for medical
advances
Joint Background Reconstruction and Foreground Segmentation via A Two-stage Convolutional Neural Network
Foreground segmentation in video sequences is a classic topic in computer
vision. Due to the lack of semantic and prior knowledge, it is difficult for
existing methods to deal with sophisticated scenes well. Therefore, in this
paper, we propose an end-to-end two-stage deep convolutional neural network
(CNN) framework for foreground segmentation in video sequences. In the first
stage, a convolutional encoder-decoder sub-network is employed to reconstruct
the background images and encode rich prior knowledge of background scenes. In
the second stage, the reconstructed background and current frame are input into
a multi-channel fully-convolutional sub-network (MCFCN) for accurate foreground
segmentation. In the two-stage CNN, the reconstruction loss and segmentation
loss are jointly optimized. The background images and foreground objects are
output simultaneously in an end-to-end way. Moreover, by incorporating the
prior semantic knowledge of foreground and background in the pre-training
process, our method could restrain the background noise and keep the integrity
of foreground objects at the same time. Experiments on CDNet 2014 show that our
method outperforms the state-of-the-art by 4.9%.Comment: ICME 201
On the Relations of Correlation Filter Based Trackers and Struck
In recent years, two types of trackers, namely correlation filter based
tracker (CF tracker) and structured output tracker (Struck), have exhibited the
state-of-the-art performance. However, there seems to be lack of analytic work
on their relations in the computer vision community. In this paper, we
investigate two state-of-the-art CF trackers, i.e., spatial regularization
discriminative correlation filter (SRDCF) and correlation filter with limited
boundaries (CFLB), and Struck, and reveal their relations. Specifically, after
extending the CFLB to its multiple channel version we prove the relation
between SRDCF and CFLB on the condition that the spatial regularization factor
of SRDCF is replaced by the masking matrix of CFLB. We also prove the
asymptotical approximate relation between SRDCF and Struck on the conditions
that the spatial regularization factor of SRDCF is replaced by an indicator
function of object bounding box, the weights of SRDCF in its loss item are
replaced by those of Struck, the linear kernel is employed by Struck, and the
search region tends to infinity. Extensive experiments on public benchmarks
OTB50 and OTB100 are conducted to verify our theoretical results. Moreover, we
explain how detailed differences among SRDCF, CFLB, and Struck would give rise
to slightly different performances on visual sequence
- …