11,944 research outputs found
Preprocessing Solar Images while Preserving their Latent Structure
Telescopes such as the Atmospheric Imaging Assembly aboard the Solar Dynamics
Observatory, a NASA satellite, collect massive streams of high resolution
images of the Sun through multiple wavelength filters. Reconstructing
pixel-by-pixel thermal properties based on these images can be framed as an
ill-posed inverse problem with Poisson noise, but this reconstruction is
computationally expensive and there is disagreement among researchers about
what regularization or prior assumptions are most appropriate. This article
presents an image segmentation framework for preprocessing such images in order
to reduce the data volume while preserving as much thermal information as
possible for later downstream analyses. The resulting segmented images reflect
thermal properties but do not depend on solving the ill-posed inverse problem.
This allows users to avoid the Poisson inverse problem altogether or to tackle
it on each of 10 segments rather than on each of 10 pixels,
reducing computing time by a factor of 10. We employ a parametric
class of dissimilarities that can be expressed as cosine dissimilarity
functions or Hellinger distances between nonlinearly transformed vectors of
multi-passband observations in each pixel. We develop a decision theoretic
framework for choosing the dissimilarity that minimizes the expected loss that
arises when estimating identifiable thermal properties based on segmented
images rather than on a pixel-by-pixel basis. We also examine the efficacy of
different dissimilarities for recovering clusters in the underlying thermal
properties. The expected losses are computed under scientifically motivated
prior distributions. Two simulation studies guide our choices of dissimilarity
function. We illustrate our method by segmenting images of a coronal hole
observed on 26 February 2015
How groups can foster consensus: The case of local cultures
A local culture denotes a commonly shared behaviour within a cluster of
firms. Similar to social norms or conventions, it is an emergent feature
resulting from the firms' interaction in an economic network. To model these
dynamics, we consider a distributed agent population, representing e.g. firms
or individuals. Further, we build on a continuous opinion dynamics model with
bounded confidence (), which assumes that two agents only interact if
differences in their behaviour are less than . Interaction results in
more similarity of behaviour, i.e. convergence towards a common mean. This
framework is extended by two major concepts: (i) The agent's in-group
consisting of acquainted interaction partners is explicitly taken into account.
This leads to an effective agent behaviour reflecting that agents try to
continue to interact with past partners and thus to keep sufficiently close to
them. (ii) The in-group network structure changes over time, as agents can form
new links to other agents with sufficiently close effective behaviour or delete
links to agents no longer close in behaviour. Thus, our model provides a
feedback mechanism between the agents' behaviour and their in-group structure.
Studying its consequences by means of agent-based computer simulations, we find
that for narrow-minded agents (low ) the additional feedback helps to
find consensus more often, whereas for open-minded agents (high )
this does not hold. This counterintuitive result is explained by simulations of
the network evolution
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