0000000000297268
AUTHOR
Frédéric Chazal
Topology guaranteeing manifold reconstruction using distance function to noisy data
Given a smooth compact codimension one submanifold S of Rk and a compact approximation K of S, we prove that it is possible to reconstruct S and to approximate the medial axis of S with topological guarantees using unions of balls centered on K. We consider two notions of noisy-approximation that generalize sampling conditions introduced by Amenta & al. and Dey & al. Our results are based upon critical point theory for distance functions. For the two approximation conditions, we prove that the connected components of the boundary of unions of balls centered on K are isotopic to S. Our results allow to consider balls of different radii. For the first approximation condition, we also prove th…
The “λ-medial axis”
Medial axis is known to be unstable for nonsmooth objects. For an open set O, we define the weak feature size, wfs, minimum distance between Oc and the critical points of the function distance to Oc. We introduce the "lambda-medial axis" Mλ of O, a subset of the medial axis of O which captures the homotopy type of O when λ < wfs. We show that, at least for some "regular" values of λ, Mλ remains stable under Hausdorff distance perturbations of Oc.
Molecular shape analysis based upon the morse-smale complex and the connolly function
Docking is the process by which two or several molecules form a complex. Docking involves the geometry of the molecular surfaces, as well as chemical and energetical considerations. In the mid-eighties, Connolly proposed a docking algorithm matching surface knobs with surface depressions. Knobs and depressions refer to the extrema of the Connolly function, which is defined as follows. Given a surface M bounding a three-dimensional domain X, and a sphere S centered at a point p of M, the Connolly function is equal to the solid angle of the portion of S containing within X.We recast the notions of knobs and depressions in the framework of Morse theory for functions defined over two-dimensiona…
Convergence Rates for Persistence Diagram Estimation in Topological Data Analysis
International audience; Computational topology has recently seen an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a statistical approach. We show that the use of persistent homology can be naturally considered in general statistical frameworks and that persistence diagrams can be used as statistics with interesting convergence properties. Some numerical experiments are performed in various contexts to illustrate our results.
Volumes transverses aux feuilletages d'efinissables dans des structures o-minimales
Let Fλ be a family of codimension p foliations defined on a family Mλ of manifolds and let Xλ be a family of compact subsets of Mλ. Suppose that Fλ, Mλ and Xλ are definable in an o-minimal structure and that all leaves of Fλ are closed. Given a definable family Ωλ of differential p-forms satisfaying iZ Ωλ = 0 forany vector field Z tangent to Fλ, we prove that there exists a constant A > 0 such that the integral of on any transversal of Fλ intersecting each leaf in at most one point is bounded by A. We apply this result to prove that p-volumes of transverse sections of Fλ are uniformly bounded.
Une propriété des solutions non spiralantes d’équations différentielles analytiques du plan
Resume On montre que le contact entre deux courbes integrales singulieres et non spiralantes des deux equations de Pfaff analytiques du plan est au plus exponentiellement petit.
Optimal rates of convergence for persistence diagrams in Topological Data Analysis
Computational topology has recently known an important development toward data analysis, giving birth to the field of topological data analysis. Topological persistence, or persistent homology, appears as a fundamental tool in this field. In this paper, we study topological persistence in general metric spaces, with a statistical approach. We show that the use of persistent homology can be naturally considered in general statistical frameworks and persistence diagrams can be used as statistics with interesting convergence properties. Some numerical experiments are performed in various contexts to illustrate our results.
Statistical properties of general Markov dynamical sources: applications to information theory
In \textitDynamical sources in information theory: fundamental intervals and word prefixes, B. Vallée studies statistical properties of words generated by dynamical sources. This is done using generalized Ruelle operators. The aim of this article is to generalize sources for which the results hold. First, we avoid the use of Grotendieck theory and Fredholm determinants, this allows dynamical sources that cannot be extended to a complex disk or that are not analytic. Second, we consider Markov sources: the language generated by the source over an alphabet \textbfM is not necessarily \textbfM^*.