51,897 research outputs found
Partial ovoids and partial spreads in symplectic and orthogonal polar spaces
We present improved lower bounds on the sizes of small maximal partial ovoids and small maximal partial spreads in the classical symplectic and orthogonal polar spaces, and improved upper bounds on the sizes of large maximal partial ovoids and large maximal partial spreads in the classical symplectic and orthogonal polar spaces. An overview of the status regarding these results is given in tables. The similar results for the hermitian classical polar spaces are presented in [J. De Beule, A. Klein, K. Metsch, L. Storme, Partial ovoids and partial spreads in hermitian polar spaces, Des. Codes Cryptogr. (in press)]
Constant rank-distance sets of hermitian matrices and partial spreads in hermitian polar spaces
In this paper we investigate partial spreads of through the
related notion of partial spread sets of hermitian matrices, and the more
general notion of constant rank-distance sets. We prove a tight upper bound on
the maximum size of a linear constant rank-distance set of hermitian matrices
over finite fields, and as a consequence prove the maximality of extensions of
symplectic semifield spreads as partial spreads of . We prove
upper bounds for constant rank-distance sets for even rank, construct large
examples of these, and construct maximal partial spreads of for a
range of sizes
Spectral geometry with a cut-off: topological and metric aspects
Inspired by regularization in quantum field theory, we study topological and
metric properties of spaces in which a cut-off is introduced. We work in the
framework of noncommutative geometry, and focus on Connes distance associated
to a spectral triple (A, H, D). A high momentum (short distance) cut-off is
implemented by the action of a projection P on the Dirac operator D and/or on
the algebra A. This action induces two new distances. We individuate conditions
making them equivalent to the original distance. We also study the
Gromov-Hausdorff limit of the set of truncated states, first for compact
quantum metric spaces in the sense of Rieffel, then for arbitrary spectral
triples. To this aim, we introduce a notion of "state with finite moment of
order 1" for noncommutative algebras. We then focus on the commutative case,
and show that the cut-off induces a minimal length between points, which is
infinite if P has finite rank. When P is a spectral projection of , we work
out an approximation of points by non-pure states that are at finite distance
from each other. On the circle, such approximations are given by Fejer
probability distributions. Finally we apply the results to Moyal plane and the
fuzzy sphere, obtained as Berezin quantization of the plane and the sphere
respectively.Comment: Reference added. Minor corrections. Published version. 38 pages, 2
figures. Journal of Geometry and Physics 201
A Novel Method for the Absolute Pose Problem with Pairwise Constraints
Absolute pose estimation is a fundamental problem in computer vision, and it
is a typical parameter estimation problem, meaning that efforts to solve it
will always suffer from outlier-contaminated data. Conventionally, for a fixed
dimensionality d and the number of measurements N, a robust estimation problem
cannot be solved faster than O(N^d). Furthermore, it is almost impossible to
remove d from the exponent of the runtime of a globally optimal algorithm.
However, absolute pose estimation is a geometric parameter estimation problem,
and thus has special constraints. In this paper, we consider pairwise
constraints and propose a globally optimal algorithm for solving the absolute
pose estimation problem. The proposed algorithm has a linear complexity in the
number of correspondences at a given outlier ratio. Concretely, we first
decouple the rotation and the translation subproblems by utilizing the pairwise
constraints, and then we solve the rotation subproblem using the
branch-and-bound algorithm. Lastly, we estimate the translation based on the
known rotation by using another branch-and-bound algorithm. The advantages of
our method are demonstrated via thorough testing on both synthetic and
real-world dataComment: 10 pages, 7figure
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