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Specialising finite domain programs with polyhedra
A procedure is described for tightening domain constraints of finite domain logic programs by applying a static analysis based on convex polyhedra. Individual finite domain constraints are over-approximated by polyhedra to describe the solution space over ninteger variables as an n dimensional polyhedron. This polyhedron is then approximated, using projection, as an n dimensional bounding box that can be used to specialise and improve the domain constraints. The analysis can be implemented straightforwardly and an empirical evaluation of the specialisation technique is given
Designing postgraduate pedagogies: connecting internal and external leaders
Learning is the new resource driving the knowledge economy. Now everyone is expected to make themselves available to learn : un-learn : re-learn. Much has been written about new modes of learning, as well the new technologies that promise to deliver information 24/7. Paradoxically, however, in the field of educational sociology there has been little systematic theorisation of the pedagogies designed to facilitate learning in the knowledge economy. Nor have there been systematic efforts to connect macro economic, technological and social changes to state official policies and institutional pedagogic practices. The Bernsteinian theoretical corpus models the power and control relations generating pedagogic discourses, practices and identities from the macro level of policy formation to the micro level of pedagogic interactions. It is therefore useful in examining the new pedagogies designed to generate the learning resources of the knowledge economy. In this paper, we draw on and extend Bernstein's theory of pedagogic discourse and identities to analyse the design and implementation of a postgraduate unit in educational research. This unit aimed to be: rigorous in disciplinary knowledge, technologically innovative, cost efficient; and responsive to diverse student needs and market contingencies
Exploring the Morphology of RAVE Stellar Spectra
The RAdial Velocity Experiment (RAVE) is a medium resolution R~7500
spectroscopic survey of the Milky Way which already obtained over half a
million stellar spectra. They present a randomly selected magnitude-limited
sample, so it is important to use a reliable and automated classification
scheme which identifies normal single stars and discovers different types of
peculiar stars. To this end we present a morphological classification of
350,000 RAVE survey stellar spectra using locally linear embedding, a
dimensionality reduction method which enables representing the complex spectral
morphology in a low dimensional projected space while still preserving the
properties of the local neighborhoods of spectra. We find that the majority of
all spectra in the database ~90-95% belong to normal single stars, but there is
also a significant population of several types of peculiars. Among them the
most populated groups are those of various types of spectroscopic binary and
chromospherically active stars. Both of them include several thousands of
spectra. Particularly the latter group offers significant further investigation
opportunities since activity of stars is a known proxy of stellar ages.
Applying the same classification procedure to the sample of normal single stars
alone shows that the shape of the projected manifold in two dimensional space
correlates with stellar temperature, surface gravity and metallicity.Comment: 28 pages, 11 figures, accepted for publication in ApJ
Pycortex: an interactive surface visualizer for fMRI.
Surface visualizations of fMRI provide a comprehensive view of cortical activity. However, surface visualizations are difficult to generate and most common visualization techniques rely on unnecessary interpolation which limits the fidelity of the resulting maps. Furthermore, it is difficult to understand the relationship between flattened cortical surfaces and the underlying 3D anatomy using tools available currently. To address these problems we have developed pycortex, a Python toolbox for interactive surface mapping and visualization. Pycortex exploits the power of modern graphics cards to sample volumetric data on a per-pixel basis, allowing dense and accurate mapping of the voxel grid across the surface. Anatomical and functional information can be projected onto the cortical surface. The surface can be inflated and flattened interactively, aiding interpretation of the correspondence between the anatomical surface and the flattened cortical sheet. The output of pycortex can be viewed using WebGL, a technology compatible with modern web browsers. This allows complex fMRI surface maps to be distributed broadly online without requiring installation of complex software
Uncovering collective listening habits and music genres in bipartite networks
In this paper, we analyze web-downloaded data on people sharing their music
library, that we use as their individual musical signatures (IMS). The system
is represented by a bipartite network, nodes being the music groups and the
listeners. Music groups audience size behaves like a power law, but the
individual music library size is an exponential with deviations at small
values. In order to extract structures from the network, we focus on
correlation matrices, that we filter by removing the least correlated links.
This percolation idea-based method reveals the emergence of social communities
and music genres, that are visualised by a branching representation. Evidence
of collective listening habits that do not fit the neat usual genres defined by
the music industry indicates an alternative way of classifying listeners/music
groups. The structure of the network is also studied by a more refined method,
based upon a random walk exploration of its properties. Finally, a personal
identification - community imitation model (PICI) for growing bipartite
networks is outlined, following Potts ingredients. Simulation results do
reproduce quite well the empirical data.Comment: submitted to PR
Extending a serial 3D two-phase CFD code to parallel execution over MPI by using the PETSc library for domain decomposition
To leverage the last two decades' transition in High-Performance Computing
(HPC) towards clusters of compute nodes bound together with fast interconnects,
a modern scalable CFD code must be able to efficiently distribute work amongst
several nodes using the Message Passing Interface (MPI). MPI can enable very
large simulations running on very large clusters, but it is necessary that the
bulk of the CFD code be written with MPI in mind, an obstacle to parallelizing
an existing serial code.
In this work we present the results of extending an existing two-phase 3D
Navier-Stokes solver, which was completely serial, to a parallel execution
model using MPI. The 3D Navier-Stokes equations for two immiscible
incompressible fluids are solved by the continuum surface force method, while
the location of the interface is determined by the level-set method.
We employ the Portable Extensible Toolkit for Scientific Computing (PETSc)
for domain decomposition (DD) in a framework where only a fraction of the code
needs to be altered. We study the strong and weak scaling of the resulting
code. Cases are studied that are relevant to the fundamental understanding of
oil/water separation in electrocoalescers.Comment: 8 pages, 6 figures, final version for to the CFD 2014 conferenc
Contemporary continuum QCD approaches to excited hadrons
Amongst the bound states produced by the strong interaction, radially excited
meson and nucleon states offer an important phenomenological window into the
long-range behavior of the coupling constant in Quantum Chromodynamics. We here
report on some technical details related to the computation of the bound
state's eigenvalue spectrum in the framework of Bethe-Salpeter and Faddeev
equations.Comment: Proceedings of the 21st International Conference on Few-body Problems
in Physics to be published in EPJ Web of Conference
Hallucinating optimal high-dimensional subspaces
Linear subspace representations of appearance variation are pervasive in
computer vision. This paper addresses the problem of robustly matching such
subspaces (computing the similarity between them) when they are used to
describe the scope of variations within sets of images of different (possibly
greatly so) scales. A naive solution of projecting the low-scale subspace into
the high-scale image space is described first and subsequently shown to be
inadequate, especially at large scale discrepancies. A successful approach is
proposed instead. It consists of (i) an interpolated projection of the
low-scale subspace into the high-scale space, which is followed by (ii) a
rotation of this initial estimate within the bounds of the imposed
``downsampling constraint''. The optimal rotation is found in the closed-form
which best aligns the high-scale reconstruction of the low-scale subspace with
the reference it is compared to. The method is evaluated on the problem of
matching sets of (i) face appearances under varying illumination and (ii)
object appearances under varying viewpoint, using two large data sets. In
comparison to the naive matching, the proposed algorithm is shown to greatly
increase the separation of between-class and within-class similarities, as well
as produce far more meaningful modes of common appearance on which the match
score is based.Comment: Pattern Recognition, 201
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