282 research outputs found
Persistent Homology analysis of Phase Transitions
Persistent homology analysis, a recently developed computational method in
algebraic topology, is applied to the study of the phase transitions undergone
by the so-called XY-mean field model and by the phi^4 lattice model,
respectively. For both models the relationship between phase transitions and
the topological properties of certain submanifolds of configuration space are
exactly known. It turns out that these a-priori known facts are clearly
retrieved by persistent homology analysis of dynamically sampled submanifolds
of configuration space.Comment: 10 pages; 10 figure
On the apparent failure of the topological theory of phase transitions
The topological theory of phase transitions has its strong point in two
theorems proving that, for a wide class of physical systems, phase transitions
necessarily stem from topological changes of some submanifolds of configuration
space. It has been recently argued that the lattice -model
provides a counterexample that falsifies this theory. It is here shown that
this is not the case: the phase transition of this model stems from an
asymptotic () change of topology of the energy level sets, in spite
of the absence of critical points of the potential in correspondence of the
transition energy.Comment: 5 pages, 4 figure
Towards Persistence-Based Reconstruction in Euclidean Spaces
Manifold reconstruction has been extensively studied for the last decade or
so, especially in two and three dimensions. Recently, significant improvements
were made in higher dimensions, leading to new methods to reconstruct large
classes of compact subsets of Euclidean space . However, the complexities
of these methods scale up exponentially with d, which makes them impractical in
medium or high dimensions, even for handling low-dimensional submanifolds. In
this paper, we introduce a novel approach that stands in-between classical
reconstruction and topological estimation, and whose complexity scales up with
the intrinsic dimension of the data. Specifically, when the data points are
sufficiently densely sampled from a smooth -submanifold of , our
method retrieves the homology of the submanifold in time at most ,
where is the size of the input and is a constant depending solely on
. It can also provably well handle a wide range of compact subsets of
, though with worse complexities. Along the way to proving the
correctness of our algorithm, we obtain new results on \v{C}ech, Rips, and
witness complex filtrations in Euclidean spaces
Estimating Multidimensional Persistent Homology through a Finite Sampling
An exact computation of the persistent Betti numbers of a submanifold of
a Euclidean space is possible only in a theoretical setting. In practical
situations, only a finite sample of is available. We show that, under
suitable density conditions, it is possible to estimate the multidimensional
persistent Betti numbers of from the ones of a union of balls centered on
the sample points; this even yields the exact value in restricted areas of the
domain.
Using these inequalities we improve a previous lower bound for the natural
pseudodistance to assess dissimilarity between the shapes of two objects from a
sampling of them.
Similar inequalities are proved for the multidimensional persistent Betti
numbers of the ball union and the one of a combinatorial description of it
Dimension Detection with Local Homology
Detecting the dimension of a hidden manifold from a point sample has become
an important problem in the current data-driven era. Indeed, estimating the
shape dimension is often the first step in studying the processes or phenomena
associated to the data. Among the many dimension detection algorithms proposed
in various fields, a few can provide theoretical guarantee on the correctness
of the estimated dimension. However, the correctness usually requires certain
regularity of the input: the input points are either uniformly randomly sampled
in a statistical setting, or they form the so-called
-sample which can be neither too dense nor too sparse.
Here, we propose a purely topological technique to detect dimensions. Our
algorithm is provably correct and works under a more relaxed sampling
condition: we do not require uniformity, and we also allow Hausdorff noise. Our
approach detects dimension by determining local homology. The computation of
this topological structure is much less sensitive to the local distribution of
points, which leads to the relaxation of the sampling conditions. Furthermore,
by leveraging various developments in computational topology, we show that this
local homology at a point can be computed \emph{exactly} for manifolds
using Vietoris-Rips complexes whose vertices are confined within a local
neighborhood of . We implement our algorithm and demonstrate the accuracy
and robustness of our method using both synthetic and real data sets
On the Reconstruction of Geodesic Subspaces of
We consider the topological and geometric reconstruction of a geodesic
subspace of both from the \v{C}ech and Vietoris-Rips filtrations
on a finite, Hausdorff-close, Euclidean sample. Our reconstruction technique
leverages the intrinsic length metric induced by the geodesics on the subspace.
We consider the distortion and convexity radius as our sampling parameters for
a successful reconstruction. For a geodesic subspace with finite distortion and
positive convexity radius, we guarantee a correct computation of its homotopy
and homology groups from the sample. For geodesic subspaces of ,
we also devise an algorithm to output a homotopy equivalent geometric complex
that has a very small Hausdorff distance to the unknown shape of interest
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