3,222 research outputs found
A method for dense packing discovery
The problem of packing a system of particles as densely as possible is
foundational in the field of discrete geometry and is a powerful model in the
material and biological sciences. As packing problems retreat from the reach of
solution by analytic constructions, the importance of an efficient numerical
method for conducting \textit{de novo} (from-scratch) searches for dense
packings becomes crucial. In this paper, we use the \textit{divide and concur}
framework to develop a general search method for the solution of periodic
constraint problems, and we apply it to the discovery of dense periodic
packings. An important feature of the method is the integration of the unit
cell parameters with the other packing variables in the definition of the
configuration space. The method we present led to improvements in the
densest-known tetrahedron packing which are reported in [arXiv:0910.5226].
Here, we use the method to reproduce the densest known lattice sphere packings
and the best known lattice kissing arrangements in up to 14 and 11 dimensions
respectively (the first such numerical evidence for their optimality in some of
these dimensions). For non-spherical particles, we report a new dense packing
of regular four-dimensional simplices with density
and with a similar structure to the densest known tetrahedron packing.Comment: 15 pages, 5 figure
Parametric Alignment of Drosophila Genomes
The classic algorithms of Needleman--Wunsch and Smith--Waterman find a
maximum a posteriori probability alignment for a pair hidden Markov model
(PHMM). In order to process large genomes that have undergone complex genome
rearrangements, almost all existing whole genome alignment methods apply fast
heuristics to divide genomes into small pieces which are suitable for
Needleman--Wunsch alignment. In these alignment methods, it is standard
practice to fix the parameters and to produce a single alignment for subsequent
analysis by biologists.
Our main result is the construction of a whole genome parametric alignment of
Drosophila melanogaster and Drosophila pseudoobscura. Parametric alignment
resolves the issue of robustness to changes in parameters by finding all
optimal alignments for all possible parameters in a PHMM. Our alignment draws
on existing heuristics for dividing whole genomes into small pieces for
alignment, and it relies on advances we have made in computing convex polytopes
that allow us to parametrically align non-coding regions using biologically
realistic models. We demonstrate the utility of our parametric alignment for
biological inference by showing that cis-regulatory elements are more conserved
between Drosophila melanogaster and Drosophila pseudoobscura than previously
thought. We also show how whole genome parametric alignment can be used to
quantitatively assess the dependence of branch length estimates on alignment
parameters.
The alignment polytopes, software, and supplementary material can be
downloaded at http://bio.math.berkeley.edu/parametric/.Comment: 19 pages, 3 figure
Assessing the robustness of parsimonious predictions for gene neighborhoods from reconciled phylogenies
The availability of a large number of assembled genomes opens the way to
study the evolution of syntenic character within a phylogenetic context. The
DeCo algorithm, recently introduced by B{\'e}rard et al. allows the computation
of parsimonious evolutionary scenarios for gene adjacencies, from pairs of
reconciled gene trees. Following the approach pioneered by Sturmfels and
Pachter, we describe how to modify the DeCo dynamic programming algorithm to
identify classes of cost schemes that generates similar parsimonious
evolutionary scenarios for gene adjacencies, as well as the robustness to
changes to the cost scheme of evolutionary events of the presence or absence of
specific ancestral gene adjacencies. We apply our method to six thousands
mammalian gene families, and show that computing the robustness to changes to
cost schemes provides new and interesting insights on the evolution of gene
adjacencies and the DeCo model.Comment: Accepted, to appear in ISBRA - 11th International Symposium on
Bioinformatics Research and Applications - 2015, Jun 2015, Norfolk, Virginia,
United State
Secret Sharing Schemes with a large number of players from Toric Varieties
A general theory for constructing linear secret sharing schemes over a finite
field \Fq from toric varieties is introduced. The number of players can be as
large as for . We present general methods for obtaining
the reconstruction and privacy thresholds as well as conditions for
multiplication on the associated secret sharing schemes.
In particular we apply the method on certain toric surfaces. The main results
are ideal linear secret sharing schemes where the number of players can be as
large as . We determine bounds for the reconstruction and privacy
thresholds and conditions for strong multiplication using the cohomology and
the intersection theory on toric surfaces.Comment: 15 pages, 4 figures. arXiv admin note: text overlap with
arXiv:1203.454
Polynomial-Sized Topological Approximations Using The Permutahedron
Classical methods to model topological properties of point clouds, such as
the Vietoris-Rips complex, suffer from the combinatorial explosion of complex
sizes. We propose a novel technique to approximate a multi-scale filtration of
the Rips complex with improved bounds for size: precisely, for points in
, we obtain a -approximation with at most simplices of dimension or lower. In conjunction with dimension
reduction techniques, our approach yields a -approximation of size for Rips filtrations on arbitrary metric
spaces. This result stems from high-dimensional lattice geometry and exploits
properties of the permutahedral lattice, a well-studied structure in discrete
geometry.
Building on the same geometric concept, we also present a lower bound result
on the size of an approximate filtration: we construct a point set for which
every -approximation of the \v{C}ech filtration has to contain
features, provided that for .Comment: 24 pages, 1 figur
Experimental study of energy-minimizing point configurations on spheres
In this paper we report on massive computer experiments aimed at finding
spherical point configurations that minimize potential energy. We present
experimental evidence for two new universal optima (consisting of 40 points in
10 dimensions and 64 points in 14 dimensions), as well as evidence that there
are no others with at most 64 points. We also describe several other new
polytopes, and we present new geometrical descriptions of some of the known
universal optima.Comment: 41 pages, 12 figures, to appear in Experimental Mathematic
Smoothing Method for Approximate Extensive-Form Perfect Equilibrium
Nash equilibrium is a popular solution concept for solving
imperfect-information games in practice. However, it has a major drawback: it
does not preclude suboptimal play in branches of the game tree that are not
reached in equilibrium. Equilibrium refinements can mend this issue, but have
experienced little practical adoption. This is largely due to a lack of
scalable algorithms.
Sparse iterative methods, in particular first-order methods, are known to be
among the most effective algorithms for computing Nash equilibria in
large-scale two-player zero-sum extensive-form games. In this paper, we
provide, to our knowledge, the first extension of these methods to equilibrium
refinements. We develop a smoothing approach for behavioral perturbations of
the convex polytope that encompasses the strategy spaces of players in an
extensive-form game. This enables one to compute an approximate variant of
extensive-form perfect equilibria. Experiments show that our smoothing approach
leads to solutions with dramatically stronger strategies at information sets
that are reached with low probability in approximate Nash equilibria, while
retaining the overall convergence rate associated with fast algorithms for Nash
equilibrium. This has benefits both in approximate equilibrium finding (such
approximation is necessary in practice in large games) where some probabilities
are low while possibly heading toward zero in the limit, and exact equilibrium
computation where the low probabilities are actually zero.Comment: Published at IJCAI 1
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