Loughborough University Institutional Repository
https://dspace.lboro.ac.uk:443/dspace-jspui
The DSpace digital repository system captures, stores, indexes, preserves, and distributes digital research material.Tue, 26 Sep 2017 00:02:23 GMT2017-09-26T00:02:23ZMinimum distance estimation of Milky Way model parameters and related inference
https://dspace.lboro.ac.uk/2134/26666
Title: Minimum distance estimation of Milky Way model parameters and related inference
Authors: Banerjee, Sourabh; Basu, Ayanendranath; Bhattacharya, Sourabh; Bose, Smarajit; Chakrabarty, Dalia; Mukherjee, Soumendu S.
Abstract: We propose a method to estimate the location of the Sun in the disk of the Milky Way using a
method based on the Hellinger distance and construct confidence sets on our estimate of the unknown
location using a bootstrap-based method. Assuming the Galactic disk to be two-dimensional, the
sought solar location then reduces to the radial distance separating the Sun from the Galactic center
and the angular separation of the Galactic center to Sun line, from a pre-fixed line on the disk. On
astronomical scales, the unknown solar location is equivalent to the location of us earthlings who
observe the velocities of a sample of stars in the neighborhood of the Sun. This unknown location
is estimated by undertaking pairwise comparisons of the estimated density of the observed set of
velocities of the sampled stars, with the density estimated using synthetic stellar velocity data
sets generated at chosen locations in the Milky Way disk. The synthetic data sets are generated
at a number of locations that we choose from within a constructed grid, at four different base
astrophysical models of the Galaxy. Thus, we work with one observed stellar velocity data and
four distinct sets of simulated data comprising a number of synthetic velocity data vectors, each
generated at a chosen location. For a given base astrophysical model that gives rise to one such
simulated data set, the chosen location within our constructed grid at which the estimated density
of the generated synthetic data best matches the density of the observed data is used as an estimate
for the location at which the observed data was realized. In other words, the chosen location
corresponding to the highest match offers an estimate of the solar coordinates in the Milky Way
disk. The “match” between the pair of estimated densities is parameterized by the affinity measure
based on the familiar Hellinger distance. We perform a novel cross-validation procedure to establish
a desirable “consistency” property of the proposed method.Thu, 01 Jan 2015 00:00:00 GMThttps://dspace.lboro.ac.uk/2134/266662015-01-01T00:00:00ZBayesian estimation of density via multiple sequential inversions of two-dimensional images with application to electron microscopy
https://dspace.lboro.ac.uk/2134/26665
Title: Bayesian estimation of density via multiple sequential inversions of two-dimensional images with application to electron microscopy
Authors: Chakrabarty, Dalia; Gabrielyan, Nare; Rigat, Fabio; Beanland, Richard; Paul, Shashi
Abstract: We present a new Bayesian methodology to learn the unknown material density of a given sample by inverting its two-dimensional images that are taken with a scanning electron microscope. An image results from a sequence of projections of the convolution of the density function with the unknown microscopy correction function that we also learn from the data; thus, learning of the unknowns demands multiple inversions. We invoke a novel design of experiment, involving imaging at multiple values of the parameter that controls the subsurface depth from which information about the density structure is carried, to result in the image. Real-life material density functions are characterized by high-density contrasts and are highly discontinuous, implying that they exhibit correlation structures that do not vary smoothly. In the absence of training data, modeling such correlation structures of real material density functions is not possible. So we discretize the material sample and treat values of the density function at chosen locations inside it as independent and distribution-free parameters. Resolution of the available image dictates the discretization length of the model; three models pertaining to distinct resolution classes (at micrometer to nanometer scale lengths) are developed. We develop priors on the material density, such that these priors adapt to the sparsity inherent in the density function. The likelihood is defined in terms of the distance between the convolution of the unknown functions and the image data. The posterior probability density of the unknowns given the data is expressed using the developed priors on the density and priors on the microscopy correction function as elicited from the microscopy literature. We achieve posterior samples using an adaptive Metropolis-within-Gibbs inference scheme. The method is applied to learn the material density of a three-dimensional sample of a nano-structure, using real image data. Illustrations on simulated image data of alloy samples are also included.Thu, 01 Jan 2015 00:00:00 GMThttps://dspace.lboro.ac.uk/2134/266652015-01-01T00:00:00ZBayesian nonparametric estimation of Milky Way parameters using matrix-variate data in a new Gaussian Process-based method
https://dspace.lboro.ac.uk/2134/26664
Title: Bayesian nonparametric estimation of Milky Way parameters using matrix-variate data in a new Gaussian Process-based method
Authors: Chakrabarty, Dalia; Biswas, Munmun; Bhattacharya, Sourabh
Abstract: In this paper we develop an inverse Bayesian approach to find the value of the unknown model parameter vector that supports the real (or test) data, where the data comprises measurements of a matrix-variate variable. The method is illustrated via the estimation of the unknown Milky Way feature parameter vector, using available test and simulated (training) stellar velocity data matrices. The data is represented as an unknown function of the model parameters, where this high-dimensional function is modelled using a high-dimensional Gaussian Process (GP). The model for this function is trained using available training data and inverted by Bayesian means, to estimate the sought value of the model parameter vector at which the test data is realised. We achieve a closed-form expression for the posterior of the unknown parameter vector and the parameters of the invoked GP, given test and training data. We perform model fitting by comparing the observed data with predictions made at different summaries of the posterior probability of the model parameter vector. As a supplement, we undertake a leave-one-out cross validation of our method.Thu, 01 Jan 2015 00:00:00 GMThttps://dspace.lboro.ac.uk/2134/266642015-01-01T00:00:00ZA Marxist and an Anarchist Walk into the Occupy Movement: internal and external communication practices of radical left groups
https://dspace.lboro.ac.uk/2134/26663
Title: A Marxist and an Anarchist Walk into the Occupy Movement: internal and external communication practices of radical left groups
Authors: Swann, Thomas
Abstract: The uprisings that occurred around the world in 2011 (the Arab Spring,
the Occupy movement and the Spanish Indignados/15M), as well as subsequent
protest movements in Brazil (2013) and Turkey (2013–2014), have
been characterised as social media revolutions due to the use by participants
of online platforms such as Twitter and Facebook (Castells 2012;
Mason 2012). A number of studies, however, have shown that this is
often an inaccurate representation and that traditional forms of communication,
such as face-to-face interaction, together with traditional older
forms of online media (such as e-mail networks, fora, websites), are considered
by participants to be more central to these events than newer
social media (Fuchs 2014a, 85).Wed, 01 Jan 2014 00:00:00 GMThttps://dspace.lboro.ac.uk/2134/266632014-01-01T00:00:00Z