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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

168,657 papers · 148 categories

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8152330 · Jun 202619922001200920172026
48 results for Ball Mapper

Mapper and Ball Mapper tools for complex data analysis.

problem Exploring and visualizing high-dimensional data and scalar functions.
method Combining Mapper and Ball Mapper, adding new features for encoding structure and symmetries.
result A new hybrid algorithm, Mapper on Ball Mapper, for comparing high-dimensional data descriptors.

Optimizes cover parameter in Mapper algorithm for better visualization.

problem Tuning the cover parameter in Mapper algorithm to generate a ``nice'' graph.
method Optimizes cover by repeatedly splitting using statistical tests and Gaussian mixture model.
result Algorithm generates covers that retain dataset essence while being faster.

The construction of Mapper has emerged in the last decade as a powerful and effective topological data analysis tool that approximates and generalizes other topological summaries, such as the Reeb graph, the contour tree, split, and joint trees. In this paper, we study the parallel analysis of the construction of Mappe…

2017-12-11abs ↗pdf ↗

Mapper is an unsupervised machine learning algorithm generalising the notion of clustering to obtain a geometric description of a dataset. The procedure splits the data into possibly overlapping bins which are then clustered. The output of the algorithm is a graph where nodes represent clusters and edges represent the …

2019-06-04abs ↗pdf ↗

Mapper-GIN simplifies 3D point cloud classification with lightweight structure.

problem Robust 3D point cloud classification under corruption.
method Mapper algorithm for structural decomposition, GIN for graph classification.
result Mapper-GIN achieves competitive accuracy with minimal parameters.

A new Mapper algorithm optimizes data visualization through automatic parameter tuning.

problem Manual parameter tuning and fixed intervals limit the performance of the standard Mapper algorithm.
method Introduces a soft Mapper framework based on Gaussian mixture models for automatic interval construction and optimization via stochastic gradient descent.
result Demonstrates effectiveness in capturing underlying topological structures and identifying distinct subgroups.

Topological data analysis aims to extract topological quantities from data, which tend to focus on the broader global structure of the data rather than local information. The Mapper method, specifically, generalizes clustering methods to identify significant global mathematical structures, which are out of reach of man…

2019-10-17abs ↗pdf ↗

Reeb spaces, as well as their discretized versions called Mappers, are common descriptors used in Topological Data Analysis, with plenty of applications in various fields of science, such as computational biology and data visualization, among others. The stability and quantification of the rate of convergence of the Ma…

2019-12-23abs ↗pdf ↗

Recent advancements in graph representation learning have led to the emergence of condensed encodings that capture the main properties of a graph. However, even though these abstract representations are powerful for downstream tasks, they are not equally suitable for visualisation purposes. In this work, we merge Mappe…

2020-02-10abs ↗pdf ↗

This paper proposes a new method for learning covers of geometric datasets to improve topological inference and visualization.

problem Improving topological inference and visualization of large-scale geometric datasets.
method Proposes a method for learning topologically-faithful covers of geometric datasets using optimization.
result Simplicial complexes obtained from learned covers outperform standard methods in terms of size and representation of large-scale topology.

We present ShapeVis, a scalable visualization technique for point cloud data inspired from topological data analysis. Our method captures the underlying geometric and topological structure of the data in a compressed graphical representation. Much success has been reported by the data visualization technique Mapper, th…

2020-01-15abs ↗pdf ↗

This study uses TDA to map corporate failure, revealing distinct regions of risk.

problem Understanding and predicting corporate default risk.
method Topological Data Analysis (TDA) applied to Altman's Z-score model.
result Firms do not cluster neatly along default predictors, suggesting complex risk landscapes.

This paper describes a method to construct standard 4-balls from homotopy 4-balls in C2\mathbb{C}^2.

problem The problem is whether every homotopy 4-ball in S4S^4 is standard.
method The approach is to use Stein surfaces and pseudoconvex domains to construct a diffeomorphic domain that is the union of three pseudoconvex domains, ensuring it is a standard 4-ball.
result The construction method ensures that the domain is a standard 4-ball, providing a compelling reimbedding construction for homotopy 4-balls in C2\mathbb{C}^2.

New surface area measures defined for ball-convex bodies, leading to entropy and inequalities.

problem Defining and analyzing surface area measures for ball-convex bodies.
method Introducing LpL_p relative surface areas, proving invariance and inequalities, and using geometric interpretations.
result Established inequalities and a new notion of entropy for ball-convex bodies.

New examples show non-locally-flat PL-disk bounds in rational homology balls but not in integer homology balls.

problem Characterizing knots that bound PL-disks in integer homology balls.
method Involutive Heegaard Floer homology formal properties.
result Found infinitely many manifold-knot pairs (Y, J) where J does not bound a PL-disk in an integer homology ball but does in a rational homology ball.

Study shows no smooth embeddings of rational homology balls into complex projective plane.

problem Embedding rational homology balls into complex projective plane.
method Elementary arguments to prove non-existence of almost complex embeddings.
result No smooth embeddings of rational homology balls into complex projective plane.

New illumination bodies defined for ball-convex shapes, proving convexity and establishing surface area measures.

problem Characterizing properties of ball-convex shapes.
method Introducing illumination bodies and weighted illumination bodies, proving convexity, and establishing surface area measures.
result Illumination bodies are convex and provide surface area measures for ball-convex shapes.

In hyperbolic space HnH^n we set a geodesic ball of radius ρρ. Consider a kk dimensional minimal submanifold passing through the origin of the geodesic ball with boundary lies on the boundary of that geodesic ball. We prove that its area is no less than the totally geodesic kk dimensional submanifold passing through…

2016-12-08abs ↗pdf ↗

The paper characterizes unit balls among Stein spaces with specific groups using Bergman-Einstein metrics.

problem Characterizing unit balls among Stein spaces with specific groups.
method Study of Bergman metric on finite ball quotients and its Kähler-Einstein property.
result The Bergman-Einstein metric exists only on the unit ball itself for finite ball quotients with trivial groups.

Fintushel and Stern showed that the Brieskorn sphere Σ(2,3,7)Σ(2,3,7) bounds a rational homology ball, while its non-trivial Rokhlin invariant obstructs it from bounding an integral homology ball. It is known that their argument can be modified to show that the figure-eight knot is rationally slice, and we use this fact to p…

2017-04-25abs ↗pdf ↗

Study compares eigenvalues on spherically symmetric manifolds to Euclidean balls.

problem Comparing eigenvalues on spherically symmetric manifolds to Euclidean balls.
method Examines Dirichlet Laplace eigenvalues on balls of spherically symmetric manifolds and Euclidean space.
result Eigenvalues on spherically symmetric manifolds are smaller for small radii, but larger for hyperbolic spaces.

We prove the diameter of the intersection of two closed convex balls in a Riemannian manifold eventually decreases continuously as the centers of the balls move apart.

2019-05-17abs ↗pdf ↗

We describe two methods for showing that a vector can not be the f-vector of a homology d-ball. As a consequence, we disprove a conjectured characterization of the f-vectors of balls of dimension five and higher due to Billera and Lee. We also provide a construction of triangulated balls with various f-vectors. We show…

2009-12-10abs ↗pdf ↗