Computation of cohomology operations of finite simplicial complexes

We propose a method for calculating cohomology operations on finite simplicial complexes. Of course, there exist well–known methods for computing (co)homology groups, for example, the “reduction algorithm” consisting in reducing the matrices corresponding to the differential in each dimension to the Smith normal form, from which one can read off the (co)homology groups of the complex [Mun84], or the “incremental algorithm” for computing Betti numbers [DE93]. Nevertheless, little is known about general methods for computing cohomology operations. For any finite simplicial complex K, we give a procedure including the computation of some primary and secondary cohomology operations. This method is based on the transcription of the reduction algorithm mentioned above, in terms of a special type of algebraic homotopy equivalences, called contractions [McL75], of the (co)chain complex of K to a “minimal” (co)chain complex M(K). More concretely, whenever the ground ring is a field or the (co)homology of K is free, then M(K) is isomorphic to the (co)homology of K. Combining this contraction with the combinatorial formulae for Steenrod reduced pth powers at cochain level developed in [GR99] and [Gon00], these operations at cohomology level can be computed. Finally, a method for calculating Adem secondary cohomology operations Φq : Ker(Sq2Hq (K)) → Hq+3(K)/Sq2Hq (K) is showed

Computing the Component-Labeling and the Adjacency Tree of a Binary Digital Image in Near Logarithmic-Time

Connected component labeling (CCL) of binary images is one of the fundamental operations in real time applications. The adjacency tree (AdjT) of the connected components offers a region-based representation where each node represents a region which is surrounded by another region of the opposite color. In this paper, a fully parallel algorithm for computing the CCL and AdjT of a binary digital image is described and implemented, without the need of using any geometric information. The time complexity order for an image of m × n pixels under the assumption that a processing element exists for each pixel is near O(log(m+ n)). Results for a multicore processor show a very good scalability until the so-called memory bandwidth bottleneck is reached. The inherent parallelism of our approach points to the direction that even better results will be obtained in other less classical computing architectures.Ministerio de Economía y Competitividad MTM2016-81030-PMinisterio de Economía y Competitividad TEC2012-37868-C04-0

Homological spanning forest framework for 2D image analysis

A 2D topology-based digital image processing framework is presented here. This framework consists of the computation of a flexible geometric graph-based structure, starting from a raster representation of a digital image I. This structure is called Homological Spanning Forest (HSF for short), and it is built on a cell complex associated to I. The HSF framework allows an efficient and accurate topological analysis of regions of interest (ROIs) by using a four-level architecture. By topological analysis, we mean not only the computation of Euler characteristic, genus or Betti numbers, but also advanced computational algebraic topological information derived from homological classification of cycles. An initial HSF representation can be modified to obtain a different one, in which ROIs are almost isolated and ready to be topologically analyzed. The HSF framework is susceptible of being parallelized and generalized to higher dimensions

Cell AT-models for digital volumes

In [4], given a binary 26-adjacency voxel-based digital volume V, the homological information (that related to n-dimensional holes: connected components, ”tunnels” and cavities) is extracted from a linear map (called homology gradient vector field) acting on a polyhedral cell complex P(V) homologically equivalent to V. We develop here an alternative way for constructing P(V) based on homological algebra arguments as well as a new more efficient algorithm for computing a homology gradient vector field based on the contractibility of the maximal cells of P(V)

Towards optimality in discrete Morse Theory through chain homotopies

Once a discrete Morse function has been defined on a finite cell complex, information about its homology can be deduced from its critical elements. The main objective of this paper is to define optimal discrete gradient vector fields on general finite cell complexes, where optimality entails having the least number of critical elements. Our approach is to consider this problem as a homology computation question for chain complexes endowed with extra algebraic nilpotent operator

Homological optimality in Discrete Morse Theory through chain homotopies

Morse theory is a fundamental tool for analyzing the geometry and topology of smooth manifolds. This tool was translated by Forman to discrete structures such as cell complexes, by using discrete Morse functions or equivalently gradient vector fields. Once a discrete gradient vector field has been defined on a finite cell complex, information about its homology can be directly deduced from it. In this paper we introduce the foundations of a homology-based heuristic for finding optimal discrete gradient vector fields on a general finite cell complex K. The method is based on a computational homological algebra representation (called homological spanning forest or HSF, for short) that is an useful framework to design fast and efficient algorithms for computing advanced algebraic-topological information (classification of cycles, cohomology algebra, homology A(∞)-coalgebra, cohomology operations, homotopy groups, …). Our approach is to consider the optimality problem as a homology computation process for a chain complex endowed with an extra chain homotopy operator

An example in combinatorial cohomology

Steenrod cohomology operations are algebraic tools for distinguishing non–homeomorphic topological spaces. In this paper, starting off from the general method developed in [4] for Steenrod squares and Steenrod reduced powers, we present an explicit combinatorial formulation for the Steenrod reduced power Pp 1 : Hq(X;Fp) ! Hqp−1(X;Fp), at cocycle level, where p is an odd prime, q a non–negative integer, X a simplicial set and Fp the finite field with p elements. We design an algorithm for computing Pp 1 on the cohomology of the classifying space of Zp and we generalize this process to any simplicial set at cohomology level

Estudio de una ecuación del calor semilineal en dominios no-cilíndricos

En esta comunicación presentaremos resultados de existencia y unicidad de soluciones que verifican una igualdad de energía para una ecuación semilineal en dominios no-cilíndricos (cf. [P. E. Kloeden, P. Marín-Rubio and J. Real, Pullback attractors for a semilinear heat equation in a non-cylindrical domain, J. Differential Equations 244 (2008), 2062–2090]) basándonos en algunas ideas de [M. L. Bernardi, G. A. Pozzi and G. Savaré, Variational equations of Schroedinger-type in noncylindrical domains, J. Differential Equations 171 (2001), 63–87. S. Bonaccorsi and G. Guatteri, A variational approach to evolution problems with variable domains, J. Differential Equations 175 (2001), 51–70]. En la prueba se usa un método de penalización para un problema más regular y paso al límite. Tras ello, y con hipótesis adicionales, se consiguen estimaciones uniformes que permiten estudiar el comportamiento asintótico del problema.Ministerio de Educación y Ciencia (MEC). Españ