238 research outputs found
Ergodic properties of quasi-Markovian generalized Langevin equations with configuration dependent noise and non-conservative force
We discuss the ergodic properties of quasi-Markovian stochastic differential
equations, providing general conditions that ensure existence and uniqueness of
a smooth invariant distribution and exponential convergence of the evolution
operator in suitably weighted spaces, which implies the validity
of central limit theorem for the respective solution processes. The main new
result is an ergodicity condition for the generalized Langevin equation with
configuration-dependent noise and (non-)conservative force
GRIDDS - A Gait Recognition Image and Depth Dataset
Several approaches based on human gait have been proposed in the literature, either for medical research reasons, smart surveillance, human-machine interaction, or other purposes, whose validation highly depends on the access to common input data through available datasets, enabling a coherent performance comparison. The advent of depth sensors leveraged the emergence of novel approaches and, consequently, the usage of new datasets. In this work we present the GRIDDS - A Gait Recognition Image and Depth Dataset, a new and publicly available gait depth-based dataset that can be used mostly for person and gender recognition purposes. (c) Springer Nature Switzerland AG 2019
Dimension reduction for systems with slow relaxation
We develop reduced, stochastic models for high dimensional, dissipative
dynamical systems that relax very slowly to equilibrium and can encode long
term memory. We present a variety of empirical and first principles approaches
for model reduction, and build a mathematical framework for analyzing the
reduced models. We introduce the notions of universal and asymptotic filters to
characterize `optimal' model reductions for sloppy linear models. We illustrate
our methods by applying them to the practically important problem of modeling
evaporation in oil spills.Comment: 48 Pages, 13 figures. Paper dedicated to the memory of Leo Kadanof
Clustering of serotypes in a longitudinal study of Streptococcus pneumoniae carriage in three day care centres
<p>Abstract</p> <p>Background</p> <p><it>Streptococcus pneumoniae </it>(pneumococcus) causes a wide range of clinical manifestations that together constitute a major burden of disease worldwide. The main route of pneumococcal transmission is through asymptomatic colonisation of the nasopharynx. Studies of transmission are currently of general interest because of the impact of the new conjugate-polysaccharide vaccines on nasopharyngeal colonisation (carriage). Here we report the first longitudinal study of pneumococcal carriage that records serotype specific exposure to pneumococci simultaneously within the two most important mixing groups, families and day care facilities.</p> <p>Methods</p> <p>We followed attendees (N = 59) with their family members (N = 117) and the employees (N = 37) in three Finnish day care centres for 9 months with monthly sampling of nasopharyngeal carriage. Pneumococci were cultured, identified and serotyped by standard methods.</p> <p>Results</p> <p>Children in day care constitute a core group of pneumococcal carriage: of the 36 acquisitions of carriage with documented exposure to homologous pneumococci, the attendee had been exposed in her/his day care centre in 35 cases and in the family in 9 cases. Day care children introduce pneumococci to the family: 66% of acquisitions of a new serotype in a family were associated with simultaneous or previous carriage of the same type in the child attending day care. Consequently, pneumococcal transmission was found to take place as micro-epidemics driven by the day care centres. Each of the three day care centres was dominated by a serotype of its own, accounting for 100% of the isolates of that serotype among all samples from the day care attendees.</p> <p>Conclusion</p> <p>The transmission of pneumococci is more intense within than across clusters defined by day care facilities. The ensuing micro-epidemic behaviour enhances pneumococcal transmission.</p
An optimization principle for deriving nonequilibrium statistical models of Hamiltonian dynamics
A general method for deriving closed reduced models of Hamiltonian dynamical
systems is developed using techniques from optimization and statistical
estimation. As in standard projection operator methods, a set of resolved
variables is selected to capture the slow, macroscopic behavior of the system,
and the family of quasi-equilibrium probability densities on phase space
corresponding to these resolved variables is employed as a statistical model.
The macroscopic dynamics of the mean resolved variables is determined by
optimizing over paths of these probability densities. Specifically, a cost
function is introduced that quantifies the lack-of-fit of such paths to the
underlying microscopic dynamics; it is an ensemble-averaged, squared-norm of
the residual that results from submitting a path of trial densities to the
Liouville equation. The evolution of the macrostate is estimated by minimizing
the time integral of the cost function. The value function for this
optimization satisfies the associated Hamilton-Jacobi equation, and it
determines the optimal relation between the statistical parameters and the
irreversible fluxes of the resolved variables, thereby closing the reduced
dynamics. The resulting equations for the macroscopic variables have the
generic form of governing equations for nonequilibrium thermodynamics, and they
furnish a rational extension of the classical equations of linear irreversible
thermodynamics beyond the near-equilibrium regime. In particular, the value
function is a thermodynamic potential that extends the classical dissipation
function and supplies the nonlinear relation between thermodynamics forces and
fluxes
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