8,873 research outputs found
The 3D Spin Geometry of the Quantum Two-Sphere
We study a three-dimensional differential calculus on the standard Podles
quantum two-sphere S^2_q, coming from the Woronowicz 4D+ differential calculus
on the quantum group SU_q(2). We use a frame bundle approach to give an
explicit description of the space of forms on S^2_q and its associated spin
geometry in terms of a natural spectral triple over S^2_q. We equip this
spectral triple with a real structure for which the commutant property and the
first order condition are satisfied up to infinitesimals of arbitrary order.Comment: v2: 25 pages; minor change
Playing with parameters: structural parameterization in graphs
When considering a graph problem from a parameterized point of view, the
parameter chosen is often the size of an optimal solution of this problem (the
"standard" parameter). A natural subject for investigation is what happens when
we parameterize such a problem by various other parameters, some of which may
be the values of optimal solutions to different problems. Such research is
known as parameterized ecology. In this paper, we investigate seven natural
vertex problems, along with their respective parameters: the size of a maximum
independent set, the size of a minimum vertex cover, the size of a maximum
clique, the chromatic number, the size of a minimum dominating set, the size of
a minimum independent dominating set and the size of a minimum feedback vertex
set. We study the parameterized complexity of each of these problems with
respect to the standard parameter of the others.Comment: 17 page
Twisted Hochschild Homology of Quantum Hyperplanes
We calculate the Hochschild dimension of quantum hyperplanes using the
twisted Hochschild homology.Comment: 12 pages, LaTe
Colouring Diamond-free Graphs
The Colouring problem is that of deciding, given a graph G and an integer k, whether G admits a (proper) k-colouring. For all graphs H up to five vertices, we classify the computational complexity of Colouring for (diamond,H)-free graphs. Our proof is based on combining known results together with proving that the clique-width is bounded for (diamond,P_1+2P_2)-free graphs. Our technique for handling this case is to reduce the graph under consideration to a k-partite graph that has a very specific decomposition. As a by-product of this general technique we are also able to prove boundedness of clique-width for four other new classes of (H_1,H_2)-free graphs. As such, our work also continues a recent systematic study into the (un)boundedness of clique-width of (H_1,H_2)-free graphs, and our five new classes of bounded clique-width reduce the number of open cases from 13 to 8
Structural solutions to maximum independent set and related problems
In this thesis, we study some fundamental problems in algorithmic graph theory. Most
natural problems in this area are hard from a computational point of view. However,
many applications demand that we do solve such problems, even if they are intractable.
There are a number of methods in which we can try to do this:
1) We may use an approximation algorithm if we do not necessarily require the best
possible solution to a problem.
2) Heuristics can be applied and work well enough to be useful for many applications.
3) We can construct randomised algorithms for which the probability of failure is very
small.
4) We may parameterize the problem in some way which limits its complexity.
In other cases, we may also have some information about the structure of the
instances of the problem we are trying to solve. If we are lucky, we may and that we
can exploit this extra structure to find efficient ways to solve our problem. The question
which arises is - How far must we restrict the structure of our graph to be able to solve
our problem efficiently?
In this thesis we study a number of problems, such as Maximum Indepen-
dent Set, Maximum Induced Matching, Stable-II, Efficient Edge Domina-
tion, Vertex Colouring and Dynamic Edge-Choosability. We try to solve problems
on various hereditary classes of graphs and analyse the complexity of the resulting
problem, both from a classical and parameterized point of view
Deep Learning and Statistical Models for Time-Critical Pedestrian Behaviour Prediction
The time it takes for a classifier to make an accurate prediction can be
crucial in many behaviour recognition problems. For example, an autonomous
vehicle should detect hazardous pedestrian behaviour early enough for it to
take appropriate measures. In this context, we compare the switching linear
dynamical system (SLDS) and a three-layered bi-directional long short-term
memory (LSTM) neural network, which are applied to infer pedestrian behaviour
from motion tracks. We show that, though the neural network model achieves an
accuracy of 80%, it requires long sequences to achieve this (100 samples or
more). The SLDS, has a lower accuracy of 74%, but it achieves this result with
short sequences (10 samples). To our knowledge, such a comparison on sequence
length has not been considered in the literature before. The results provide a
key intuition of the suitability of the models in time-critical problems
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