1,067 research outputs found
A fast Bayesian approach to discrete object detection in astronomical datasets - PowellSnakes I
A new fast Bayesian approach is introduced for the detection of discrete
objects immersed in a diffuse background. This new method, called PowellSnakes,
speeds up traditional Bayesian techniques by: i) replacing the standard form of
the likelihood for the parameters characterizing the discrete objects by an
alternative exact form that is much quicker to evaluate; ii) using a
simultaneous multiple minimization code based on Powell's direction set
algorithm to locate rapidly the local maxima in the posterior; and iii)
deciding whether each located posterior peak corresponds to a real object by
performing a Bayesian model selection using an approximate evidence value based
on a local Gaussian approximation to the peak. The construction of this
Gaussian approximation also provides the covariance matrix of the uncertainties
in the derived parameter values for the object in question. This new approach
provides a speed up in performance by a factor of `hundreds' as compared to
existing Bayesian source extraction methods that use MCMC to explore the
parameter space, such as that presented by Hobson & McLachlan. We illustrate
the capabilities of the method by applying to some simplified toy models.
Furthermore PowellSnakes has the advantage of consistently defining the
threshold for acceptance/rejection based on priors which cannot be said of the
frequentist methods. We present here the first implementation of this technique
(Version-I). Further improvements to this implementation are currently under
investigation and will be published shortly. The application of the method to
realistic simulated Planck observations will be presented in a forthcoming
publication.Comment: 30 pages, 15 figures, revised version with minor changes, accepted
for publication in MNRA
Smoothing and filtering with a class of outer measures
Filtering and smoothing with a generalised representation of uncertainty is
considered. Here, uncertainty is represented using a class of outer measures.
It is shown how this representation of uncertainty can be propagated using
outer-measure-type versions of Markov kernels and generalised Bayesian-like
update equations. This leads to a system of generalised smoothing and filtering
equations where integrals are replaced by supremums and probability density
functions are replaced by positive functions with supremum equal to one.
Interestingly, these equations retain most of the structure found in the
classical Bayesian filtering framework. It is additionally shown that the
Kalman filter recursion can be recovered from weaker assumptions on the
available information on the corresponding hidden Markov model
Quantifying Noise Limitations of Neural Network Segmentations in High-Resolution Transmission Electron Microscopy
Motivated by the need for low electron dose transmission electron microscopy
imaging, we report the optimal frame dose (i.e. ) range for object
detection and segmentation tasks with neural networks. The MSD-net architecture
shows promising abilities over the industry standard U-net architecture in
generalising to frame doses below the range included in the training set, for
both simulated and experimental images. It also presents a heightened ability
to learn from lower dose images. The MSD-net displays mild visibility of a Au
nanoparticle at 20-30 , and converges at 200 where a
full segmentation of the nanoparticle is achieved. Between 30 and 200
object detection applications are still possible. This work also
highlights the importance of modelling the modulation transfer function when
training with simulated images for applications on images acquired with
scintillator based detectors such as the Gatan Oneview camera. A parametric
form of the modulation transfer function is applied with varying ranges of
parameters, and the effects on low electron dose segmentation is presented.Comment: Revised version: Numerous clarifications and improvement
Reduced-Complexity Algorithms for Indoor Map-Aware Localization Systems
The knowledge of environmental maps (i.e., map-awareness) can appreciably improve the accuracy of optimal methods for position estimation in indoor scenarios. This improvement, however, is achieved at the price of a significant complexity increase with respect to the case of map-unawareness, specially for large maps. This is mainly due to the fact that optimal map-aware estimation
algorithms require integrating highly nonlinear functions or solving nonlinear and nonconvex constrained optimization problems. In this paper, various techniques for reducing the complexity of such estimators are developed. In particular, two novel strategies for restricting the search domain of map-aware position estimators are developed and the exploitation of state-of-the-art numerical
integration and optimization methods is investigated; this leads to the development of a new family of suboptimal map-aware localization algorithms. Our numerical and experimental results evidence that the accuracy of these algorithms is very close to that offered by their optimal counterparts, despite their significantly lower computational complexity
Path integrals, particular kinds, and strange things
This paper describes a path integral formulation of the free energy
principle. The ensuing account expresses the paths or trajectories that a
particle takes as it evolves over time. The main results are a method or
principle of least action that can be used to emulate the behaviour of
particles in open exchange with their external milieu. Particles are defined by
a particular partition, in which internal states are individuated from external
states by active and sensory blanket states. The variational principle at hand
allows one to interpret internal dynamics - of certain kinds of particles - as
inferring external states that are hidden behind blanket states. We consider
different kinds of particles, and to what extent they can be imbued with an
elementary form of inference or sentience. Specifically, we consider the
distinction between dissipative and conservative particles, inert and active
particles and, finally, ordinary and strange particles. Strange particles (look
as if they) infer their own actions, endowing them with apparent autonomy or
agency. In short - of the kinds of particles afforded by a particular partition
- strange kinds may be apt for describing sentient behaviour.Comment: 31 pages (excluding references), 6 figure
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