6,411 research outputs found
Optimal Location of Sources in Transportation Networks
We consider the problem of optimizing the locations of source nodes in
transportation networks. A reduction of the fraction of surplus nodes induces a
glassy transition. In contrast to most constraint satisfaction problems
involving discrete variables, our problem involves continuous variables which
lead to cavity fields in the form of functions. The one-step replica symmetry
breaking (1RSB) solution involves solving a stable distribution of functionals,
which is in general infeasible. In this paper, we obtain small closed sets of
functional cavity fields and demonstrate how functional recursions are
converted to simple recursions of probabilities, which make the 1RSB solution
feasible. The physical results in the replica symmetric (RS) and the 1RSB
frameworks are thus derived and the stability of the RS and 1RSB solutions are
examined.Comment: 38 pages, 18 figure
Novel applications of machine learning in astronomy and beyond
The field of astronomy is currently experiencing a period of unprecedented expansion, predominantly brought about by the vast amounts of data being produced by the latest telescopes and surveys. New methods will be required to have any hope of being able to analyse the data collected, the most widespread of which is machine learning. Machine learning has evolved rapidly over the past decade in an attempt to match the rate of increasing data, and aided by advancements in computer hardware, analyses that would have been impossible in the past are now common place on astronomersā laptops. However, despite machine learning becoming a favourite tool for many, there is often little consideration for which algorithms are best suited for the job.
In this thesis, machine learning is implemented in a variety of different problems ranging from Solar System science and searching for Trans-Neptunian Objects (TNOs), to the cosmological problem of obtaining accurate photometric redshift (photo-z) estimations for distant galaxies. In chapter 2 I implement many different machine learning classifiers to aid the Dark Energy Surveyās search for TNOs, comparing the classifiers to find the most suitable, and demonstrating how machine learning can provide significant increases in efficiency. In chapter 3 I implement machine learning algorithms to provide photo-z estimations for a million galaxies, using the method as an example for how it is possible to benchmark machine learning algorithms to provide information about the scalibility of different methods. In chapter 4 I expand upon the benchmarking of methods developed for obtaining photo-z estimates, applying them instead to deep learning algorithms which directly use image data, before discussing future work and concluding in chapter 5
Space, Time and Color in Hadron Production Via e+e- -> Z0 and e+e- -> W+W-
The time-evolution of jets in hadronic e+e- events at LEP is investigated in
both position- and momentum-space, with emphasis on effects due to color flow
and particle correlations. We address dynamical aspects of the four
simultanously-evolving, cross-talking parton cascades that appear in the
reaction e+e- -> gamma/Z0 -> W+W- -> q1 q~2 q3 q~4, and compare with the
familiar two-parton cascades in e+e- -> Z0 -> q1 q~2. We use a QCD statistical
transport approach, in which the multiparticle final state is treated as an
evolving mixture of partons and hadrons, whose proportions are controlled by
their local space-time geography via standard perturbative QCD parton shower
evolution and a phenomenological model for non-perturbative parton-cluster
formation followed by cluster decays into hadrons. Our numerical simulations
exhibit a characteristic `inside-outside' evolution simultanously in position
and momentum space. We compare three different model treatments of color flow,
and find large effects due to cluster formation by the combination of partons
from different W parents. In particular, we find in our preferred model a shift
of several hundred MeV in the apparent mass of the W, which is considerably
larger than in previous model calculations. This suggests that the
determination of the W mass at LEP2 may turn out to be a sensitive probe of
spatial correlations and hadronization dynamics.Comment: 52 pages, latex, 18 figures as uu-encoded postscript fil
Semantic Mapping of Road Scenes
The problem of understanding road scenes has been on the fore-front in the computer vision community
for the last couple of years. This enables autonomous systems to navigate and understand
the surroundings in which it operates. It involves reconstructing the scene and estimating the objects
present in it, such as āvehiclesā, āroadā, āpavementsā and ābuildingsā. This thesis focusses on these
aspects and proposes solutions to address them.
First, we propose a solution to generate a dense semantic map from multiple street-level images.
This map can be imagined as the birdās eye view of the region with associated semantic labels for
tenās of kilometres of street level data. We generate the overhead semantic view from street level
images. This is in contrast to existing approaches using satellite/overhead imagery for classification
of urban region, allowing us to produce a detailed semantic map for a large scale urban area. Then
we describe a method to perform large scale dense 3D reconstruction of road scenes with associated
semantic labels. Our method fuses the depth-maps in an online fashion, generated from the
stereo pairs across time into a global 3D volume, in order to accommodate arbitrarily long image
sequences. The object class labels estimated from the street level stereo image sequence are used to
annotate the reconstructed volume. Then we exploit the scene structure in object class labelling by
performing inference over the meshed representation of the scene. By performing labelling over the
mesh we solve two issues: Firstly, images often have redundant information with multiple images
describing the same scene. Solving these images separately is slow, where our method is approximately
a magnitude faster in the inference stage compared to normal inference in the image domain.
Secondly, often multiple images, even though they describe the same scene result in inconsistent
labelling. By solving a single mesh, we remove the inconsistency of labelling across the images.
Also our mesh based labelling takes into account of the object layout in the scene, which is often
ambiguous in the image domain, thereby increasing the accuracy of object labelling. Finally, we perform
labelling and structure computation through a hierarchical robust PN Markov Random Field
defined on voxels and super-voxels given by an octree. This allows us to infer the 3D structure and
the object-class labels in a principled manner, through bounded approximate minimisation of a well
defined and studied energy functional. In this thesis, we also introduce two object labelled datasets
created from real world data. The 15 kilometre Yotta Labelled dataset consists of 8,000 images per
camera view of the roadways of the United Kingdom with a subset of them annotated with object
class labels and the second dataset is comprised of ground truth object labels for the publicly available
KITTI dataset. Both the datasets are available publicly and we hope will be helpful to the vision
research community
Digital Image Access & Retrieval
The 33th Annual Clinic on Library Applications of Data Processing, held at the University of Illinois at Urbana-Champaign in March of 1996, addressed the theme of "Digital Image Access & Retrieval." The papers from this conference cover a wide range of topics concerning digital imaging technology for visual resource collections. Papers covered three general areas: (1) systems, planning, and implementation; (2) automatic and semi-automatic indexing; and (3) preservation with the bulk of the conference focusing on indexing and retrieval.published or submitted for publicatio
Measurment of spatial orientation using a biologically plausible gradient model
A Thesis submitted for the degree of Doctor of Philosophy
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