10,674 research outputs found
Minimum d-dimensional arrangement with fixed points
In the Minimum -Dimensional Arrangement Problem (d-dimAP) we are given a
graph with edge weights, and the goal is to find a 1-1 map of the vertices into
(for some fixed dimension ) minimizing the total
weighted stretch of the edges. This problem arises in VLSI placement and chip
design.
Motivated by these applications, we consider a generalization of d-dimAP,
where the positions of some of the vertices (pins) is fixed and specified as
part of the input. We are asked to extend this partial map to a map of all the
vertices, again minimizing the weighted stretch of edges. This generalization,
which we refer to as d-dimAP+, arises naturally in these application domains
(since it can capture blocked-off parts of the board, or the requirement of
power-carrying pins to be in certain locations, etc.). Perhaps surprisingly,
very little is known about this problem from an approximation viewpoint.
For dimension , we obtain an -approximation
algorithm, based on a strengthening of the spreading-metric LP for 2-dimAP. The
integrality gap for this LP is shown to be . We also show that
it is NP-hard to approximate 2-dimAP+ within a factor better than
\Omega(k^{1/4-\eps}). We also consider a (conceptually harder, but
practically even more interesting) variant of 2-dimAP+, where the target space
is the grid , instead of
the entire integer lattice . For this problem, we obtain a -approximation using the same LP relaxation. We complement
this upper bound by showing an integrality gap of , and an
\Omega(k^{1/2-\eps})-inapproximability result.
Our results naturally extend to the case of arbitrary fixed target dimension
Community-aware network sparsification
Network sparsification aims to reduce the number of edges of a network while maintaining its structural properties; such properties include shortest paths, cuts, spectral measures, or network modularity. Sparsification has multiple applications, such as, speeding up graph-mining algorithms, graph visualization, as well as identifying the important network edges.
In this paper we consider a novel formulation of the network-sparsification problem. In addition to the network, we also consider as input a set of communities. The goal is to sparsify the network so as to preserve the network structure with respect to the given communities. We introduce two variants of the community-aware sparsification problem, leading to sparsifiers that satisfy different connectedness community properties. From the technical point of view, we prove hardness results and devise effective approximation algorithms. Our experimental results on a large collection of datasets demonstrate the effectiveness of our algorithms.https://epubs.siam.org/doi/10.1137/1.9781611974973.48Accepted manuscrip
The SDSS Galaxy Angular Two-Point Correlation Function
We present the galaxy two-point angular correlation function for galaxies
selected from the seventh data release of the Sloan Digital Sky Survey. The
galaxy sample was selected with -band apparent magnitudes between 17 and 21;
and we measure the correlation function for the full sample as well as for the
four magnitude ranges: 17-18, 18-19, 19-20, and 20-21. We update the flag
criteria to select a clean galaxy catalog and detail specific tests that we
perform to characterize systematic effects, including the effects of seeing,
Galactic extinction, and the overall survey uniformity. Notably, we find that
optimally we can use observed regions with seeing < 1\farcs5, and -band
extinction < 0.13 magnitudes, smaller than previously published results.
Furthermore, we confirm that the uniformity of the SDSS photometry is minimally
affected by the stripe geometry. We find that, overall, the two-point angular
correlation function can be described by a power law, with , over the range
0\fdg005--10\degr. We also find similar relationships for the four
magnitude subsamples, but the amplitude within the same angular interval for
the four subsamples is found to decrease with fainter magnitudes, in agreement
with previous results. We find that the systematic signals are well below the
galaxy angular correlation function for angles less than approximately
5\degr, which limits the modeling of galaxy angular correlations on larger
scales. Finally, we present our custom, highly parallelized two-point
correlation code that we used in this analysis.Comment: 22 pages, 17 figures, accepted by MNRA
Are galaxy distributions scale invariant? A perspective from dynamical systems theory
Unless there is evidence for fractal scaling with a single exponent over
distances .1 <= r <= 100 h^-1 Mpc then the widely accepted notion of scale
invariance of the correlation integral for .1 <= r <= 10 h^-1 Mpc must be
questioned. The attempt to extract a scaling exponent \nu from the correlation
integral n(r) by plotting log(n(r)) vs. log(r) is unreliable unless the
underlying point set is approximately monofractal. The extraction of a spectrum
of generalized dimensions \nu_q from a plot of the correlation integral
generating function G_n(q) by a similar procedure is probably an indication
that G_n(q) does not scale at all. We explain these assertions after defining
the term multifractal, mutually--inconsistent definitions having been confused
together in the cosmology literature. Part of this confusion is traced to a
misleading speculation made earlier in the dynamical systems theory literature,
while other errors follow from confusing together entirely different
definitions of ``multifractal'' from two different schools of thought. Most
important are serious errors in data analysis that follow from taking for
granted a largest term approximation that is inevitably advertised in the
literature on both fractals and dynamical systems theory.Comment: 39 pages, Latex with 17 eps-files, using epsf.sty and a4wide.sty
(included) <[email protected]
Data complexity measured by principal graphs
How to measure the complexity of a finite set of vectors embedded in a
multidimensional space? This is a non-trivial question which can be approached
in many different ways. Here we suggest a set of data complexity measures using
universal approximators, principal cubic complexes. Principal cubic complexes
generalise the notion of principal manifolds for datasets with non-trivial
topologies. The type of the principal cubic complex is determined by its
dimension and a grammar of elementary graph transformations. The simplest
grammar produces principal trees.
We introduce three natural types of data complexity: 1) geometric (deviation
of the data's approximator from some "idealized" configuration, such as
deviation from harmonicity); 2) structural (how many elements of a principal
graph are needed to approximate the data), and 3) construction complexity (how
many applications of elementary graph transformations are needed to construct
the principal object starting from the simplest one).
We compute these measures for several simulated and real-life data
distributions and show them in the "accuracy-complexity" plots, helping to
optimize the accuracy/complexity ratio. We discuss various issues connected
with measuring data complexity. Software for computing data complexity measures
from principal cubic complexes is provided as well.Comment: Computers and Mathematics with Applications, in pres
Multivariate Approaches to Classification in Extragalactic Astronomy
Clustering objects into synthetic groups is a natural activity of any
science. Astrophysics is not an exception and is now facing a deluge of data.
For galaxies, the one-century old Hubble classification and the Hubble tuning
fork are still largely in use, together with numerous mono-or bivariate
classifications most often made by eye. However, a classification must be
driven by the data, and sophisticated multivariate statistical tools are used
more and more often. In this paper we review these different approaches in
order to situate them in the general context of unsupervised and supervised
learning. We insist on the astrophysical outcomes of these studies to show that
multivariate analyses provide an obvious path toward a renewal of our
classification of galaxies and are invaluable tools to investigate the physics
and evolution of galaxies.Comment: Open Access paper.
http://www.frontiersin.org/milky\_way\_and\_galaxies/10.3389/fspas.2015.00003/abstract\>.
\<10.3389/fspas.2015.00003 \&g
A New Quartet Tree Heuristic for Hierarchical Clustering
We consider the problem of constructing an an optimal-weight tree from the
3*(n choose 4) weighted quartet topologies on n objects, where optimality means
that the summed weight of the embedded quartet topologiesis optimal (so it can
be the case that the optimal tree embeds all quartets as non-optimal
topologies). We present a heuristic for reconstructing the optimal-weight tree,
and a canonical manner to derive the quartet-topology weights from a given
distance matrix. The method repeatedly transforms a bifurcating tree, with all
objects involved as leaves, achieving a monotonic approximation to the exact
single globally optimal tree. This contrasts to other heuristic search methods
from biological phylogeny, like DNAML or quartet puzzling, which, repeatedly,
incrementally construct a solution from a random order of objects, and
subsequently add agreement values.Comment: 22 pages, 14 figure
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