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1. Coordinates Adapted to Vector Fields: Canonical Coordinates

   Stovall, Betsy; Street, Brian (December 2018, Geometric and functional analysis)

   Given a finite collection of C1 vector fields on aC2 manifold which span the tangent space at every point, we consider the question of when there is locally a coordinate system in which these vector fields have a higher level of smoothness. For example, when is there a coordinate system in which the vector fields are smooth, or real analytic, or have Zygmund regularity of some finite order? We address this question in a quantitative way, which strengthens and generalizes previous works on the quantitative theory of sub-Riemannian (aka Carnot–Carathéodory) geometry due to Nagel, Stein, and Wainger, Tao and Wright, the second author, and others. Furthermore, we provide a diffeomorphism invariant version of these theories. This is the first part in a three part series of papers. In this paper, we study a particular coordinate system adapted to a collection of vector fields (sometimes called canonical coordinates) and present results related to the above questions which are not quite sharp; these results form the backbone of the series. The methods of this paper are based on techniques from ODEs. In the second paper, we use additional methods from PDEs to obtain the sharp results. In the third paper, we prove results concerning real analyticity and use methods from ODEs. 

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2. Toroidal Coordinates: Decorrelating Circular Coordinates with Lattice Reduction

   https://doi.org/10.4230/LIPIcs.SoCG.2023.57  

   Scoccola, Luis; Gakhar, Hitesh; Bush, Johnathan; Schonsheck, Nikolas; Rask, Tatum; Zhou, Ling; Perea, Jose A. (January 2023, Leibniz international proceedings in informatics)

   Chambers, Erin W.; Gudmundsson, Joachim (Ed.)

   The circular coordinates algorithm of de Silva, Morozov, and Vejdemo-Johansson takes as input a dataset together with a cohomology class representing a 1-dimensional hole in the data; the output is a map from the data into the circle that captures this hole, and that is of minimum energy in a suitable sense. However, when applied to several cohomology classes, the output circle-valued maps can be "geometrically correlated" even if the chosen cohomology classes are linearly independent. It is shown in the original work that less correlated maps can be obtained with suitable integer linear combinations of the cohomology classes, with the linear combinations being chosen by inspection. In this paper, we identify a formal notion of geometric correlation between circle-valued maps which, in the Riemannian manifold case, corresponds to the Dirichlet form, a bilinear form derived from the Dirichlet energy. We describe a systematic procedure for constructing low energy torus-valued maps on data, starting from a set of linearly independent cohomology classes. We showcase our procedure with computational examples. Our main algorithm is based on the Lenstra-Lenstra-Lovász algorithm from computational number theory. 

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3. Variational Barycentric Coordinates

   Dodik, Ana; Stein, Oded; Sitzmann, Vincent; Solomon, Justin (December 2023, ACM Transactions on Graphics)

   We propose a variational technique to optimize for generalized barycentric coordinates that offers additional control compared to existing models. Prior work represents barycentric coordinates using meshes or closed-form formulae, limiting the choice of objective function. In contrast, we directly parameterize the continuous function mapping any coordinate in a polytope’s interior to its barycentric coordinates using a neural field. Enabled by our theoretical characterization of barycentric coordinates, we construct neural fields parameterizing valid coordinates. We demonstrate flexibility using various objective functions, validate our algorithm, and present several applications. 

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4. Variational Barycentric Coordinates

   Dodik, Ana; Stein, Oded; Sitzmann, Vincent; Solomon, Justin (December 2023, ACM Transactions on Graphics)

   We propose a variational technique to optimize for generalized barycentric coordinates that offers additional control compared to existing models. Prior work represents barycentric coordinates using meshes or closed-form formulae, limiting the choice of objective function. In contrast, we directly parameterize the continuous function mapping any coordinate in a polytope’s interior to its barycentric coordinates using a neural field. Enabled by our theoretical characterization of barycentric coordinates, we construct neural fields parameterizing valid coordinates. We demonstrate flexibility using various objective functions, validate our algorithm, and present several applications. 

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5. Manifold Coordinates with Physical Meaning

   Samson J. Koelle; Hanyu Zhang; Marina Meila; Yu-Chia Chen (June 2022, Journal of machine learning research)

   Manifold embedding algorithms map high-dimensional data down to coordinates in a much lower-dimensional space. One of the aims of dimension reduction is to find intrinsic coordinates that describe the data manifold. The coordinates returned by the embedding algorithm are abstract, and finding their physical or domain-related meaning is not formalized and often left to domain experts. This paper studies the problem of recovering the meaning of the new low-dimensional representation in an automatic, principled fashion. We propose a method to explain embedding coordinates of a manifold as non-linear compositions of functions from a user-defined dictionary. We show that this problem can be set up as a sparse linear Group Lasso recovery problem, find sufficient recovery conditions, and demonstrate its effectiveness on data 

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