100,886 research outputs found
On the pseudolinear crossing number
A drawing of a graph is {\em pseudolinear} if there is a pseudoline
arrangement such that each pseudoline contains exactly one edge of the drawing.
The {\em pseudolinear crossing number} of a graph is the minimum number of
pairwise crossings of edges in a pseudolinear drawing of . We establish
several facts on the pseudolinear crossing number, including its computational
complexity and its relationship to the usual crossing number and to the
rectilinear crossing number. This investigation was motivated by open questions
and issues raised by Marcus Schaefer in his comprehensive survey of the many
variants of the crossing number of a graph.Comment: 12 page
From Proximity to Utility: A Voronoi Partition of Pareto Optima
We present an extension of Voronoi diagrams where when considering which site
a client is going to use, in addition to the site distances, other site
attributes are also considered (for example, prices or weights). A cell in this
diagram is then the locus of all clients that consider the same set of sites to
be relevant. In particular, the precise site a client might use from this
candidate set depends on parameters that might change between usages, and the
candidate set lists all of the relevant sites. The resulting diagram is
significantly more expressive than Voronoi diagrams, but naturally has the
drawback that its complexity, even in the plane, might be quite high.
Nevertheless, we show that if the attributes of the sites are drawn from the
same distribution (note that the locations are fixed), then the expected
complexity of the candidate diagram is near linear.
To this end, we derive several new technical results, which are of
independent interest. In particular, we provide a high-probability,
asymptotically optimal bound on the number of Pareto optima points in a point
set uniformly sampled from the -dimensional hypercube. To do so we revisit
the classical backward analysis technique, both simplifying and improving
relevant results in order to achieve the high-probability bounds
Geometric Crossing-Minimization - A Scalable Randomized Approach
We consider the minimization of edge-crossings in geometric drawings of graphs G=(V, E), i.e., in drawings where each edge is depicted as a line segment. The respective decision problem is NP-hard [Daniel Bienstock, 1991]. Crossing-minimization, in general, is a popular theoretical research topic; see Vrt\u27o [Imrich Vrt\u27o, 2014]. In contrast to theory and the topological setting, the geometric setting did not receive a lot of attention in practice. Prior work [Marcel Radermacher et al., 2018] is limited to the crossing-minimization in geometric graphs with less than 200 edges. The described heuristics base on the primitive operation of moving a single vertex v to its crossing-minimal position, i.e., the position in R^2 that minimizes the number of crossings on edges incident to v.
In this paper, we introduce a technique to speed-up the computation by a factor of 20. This is necessary but not sufficient to cope with graphs with a few thousand edges. In order to handle larger graphs, we drop the condition that each vertex v has to be moved to its crossing-minimal position and compute a position that is only optimal with respect to a small random subset of the edges. In our theoretical contribution, we consider drawings that contain for each edge uv in E and each position p in R^2 for v o(|E|) crossings. In this case, we prove that with a random subset of the edges of size Theta(k log k) the co-crossing number of a degree-k vertex v, i.e., the number of edge pairs uv in E, e in E that do not cross, can be approximated by an arbitrary but fixed factor delta with high probability. In our experimental evaluation, we show that the randomized approach reduces the number of crossings in graphs with up to 13 000 edges considerably. The evaluation suggests that depending on the degree-distribution different strategies result in the fewest number of crossings
PrAGMATiC: a Probabilistic and Generative Model of Areas Tiling the Cortex
Much of the human cortex seems to be organized into topographic cortical
maps. Yet few quantitative methods exist for characterizing these maps. To
address this issue we developed a modeling framework that can reveal
group-level cortical maps based on neuroimaging data. PrAGMATiC, a
probabilistic and generative model of areas tiling the cortex, is a
hierarchical Bayesian generative model of cortical maps. This model assumes
that the cortical map in each individual subject is a sample from a single
underlying probability distribution. Learning the parameters of this
distribution reveals the properties of a cortical map that are common across a
group of subjects while avoiding the potentially lossy step of co-registering
each subject into a group anatomical space. In this report we give a
mathematical description of PrAGMATiC, describe approximations that make it
practical to use, show preliminary results from its application to a real
dataset, and describe a number of possible future extensions
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