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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.

168,742 papers · 148 categories

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371013 · Mar 202519922001200920172026
48 results for geometry-topology interplay

We provide a systematic approach to twisting differential KO-theory leading to a construction of the corresponding twisted differential Atiyah-Hirzebruch spectral sequence (AHSS). We relate and contrast the degree two and the degree one twists, whose description involves appropriate local systems. Along the way, we pro…

2019-05-22abs ↗pdf ↗

ICLR 2021 challenge in computational geometry and topology attracted 16 teams.

problem Designing and evaluating computational methods in differential geometry and topology.
method Designing and hosting an open-source competition with repositories Geomstats and Giotto-TDA.
result 16 teams participated in the challenge, showcasing innovative contributions to computational geometry and topology.

In this introductory paper we study nearly Frobenius algebras which are generalizations of the concept of a Frobenius algebra which appear naturally in topology: nearly Frobenius algebras have no traces (co-units). We survey the most basic foundational results and some of the applications they encounter in geometry, to…

2019-07-11abs ↗pdf ↗

Arboricity of manifolds is explored, with specific results for 2D surfaces.

problem Understanding the arboricity of different types of manifolds.
method Analyzing discrete 2-spheres, other 2D surfaces, and d-manifolds of higher dimensions.
result Arboricity of 2D surfaces is 3 or 4, and for higher dimensions, it can be arbitrarily large.

This is the content of a talk given by the author at the 2009 Lehigh University Geometry/Topology Conference. Using the definition of connection given by Dieudonné, the Sasaki metric on the tangent bundle to a Riemannian manifold is expressed in a natural way. Also, the following property is established. The induced me…

2009-06-05abs ↗pdf ↗

We study geometry, topology and deformation spaces of noncompact complex hyperbolic manifolds (geometrically finite, with variable negative curvature), whose properties make them surprisingly different from real hyperbolic manifolds with constant negative curvature. This study uses an interaction between Kähler geometr…

1997-01-26abs ↗pdf ↗

Hodge theory is a beautiful synthesis of geometry, topology, and analysis, which has been developed in the setting of Riemannian manifolds. On the other hand, spaces of images, which are important in the mathematical foundations of vision and pattern recognition, do not fit this framework. This motivates us to develop …

2009-12-01abs ↗pdf ↗

Learning to cooperate is crucially important in multi-agent environments. The key is to understand the mutual interplay between agents. However, multi-agent environments are highly dynamic, where agents keep moving and their neighbors change quickly. This makes it hard to learn abstract representations of mutual interp…

2018-10-22abs ↗pdf ↗

In the 1980s Alano Ancona developed a profound potential theory on Gromov hyperbolic manifolds of bounded geometry. Since then, such hyperbolic spaces have become basic in geometry, topology and group theory. In this paper we make Ancona's original work, addressed to a rather advanced audience, approachable for a wider…

2018-05-06abs ↗pdf ↗

We introduce a new homological machine for the study of secondary geometric invariants. The objects, called spark complexes, occur in many areas of mathematics. The theory is applied here to establish the equivalence of a large family of spark complexes which appear naturally in geometry, topology and physics. These co…

2003-06-11abs ↗pdf ↗

Graph Interplay (GIP) improves GSSL performance by enhancing graph-level communications.

problem Improving graph self-supervised learning performance without labeled data.
method Graph Interplay (GIP) introduces random inter-graph edges within standard batches to enhance GSSL methods.
result GIP significantly outperforms existing GSSL methods across multiple benchmarks.

The paper examines the geometry and topology of Sasaki-Ricci solitons, proving they are either connected at infinity or compact.

problem Understanding the geometry and topology of Sasaki-Ricci solitons.
method Analyzing the properties of complete gradient shrinking Sasaki-Ricci solitons, proving connectedness at infinity and compactness under certain curvature conditions.
result Proves that Sasaki-Ricci solitons are either connected at infinity or compact, generalizing results from previous studies.

Deep neural networks perform well on local tasks but struggle with global tasks.

problem Understanding the limitations of overparameterized deep neural networks in learning global functions.
method Introduced kk-local and kk-global functions to study the interplay between depth and function locality.
result Depth is beneficial for learning local functions but detrimental to learning global functions.

This survey summarizes the results discussed in a talk at "Bielefeld Geometry & Topology Days" held at Bielefeld University in July 2015. We are interested in quantitative and qualitative properties of Bott-Chern cohomology. We announce new results obtained in [D. Angella, N. Tardini, Quantitative and qualitative cohom…

2015-07-25abs ↗pdf ↗

Various problems of geometry, topology and dynamical systems on surfaces as well as some questions concerning one-dimensional dynamical systems lead to the study of closed surfaces endowed with a flat metric with several cone-type singularities. Such flat surfaces are naturally organized into families which appear to b…

2006-09-14abs ↗pdf ↗

Quantum crypto-economics models price risks in blockchain technology.

problem Quantum technology's potential to undermine blockchain security.
method Building financial models to price quantum risk in blockchain scenarios.
result Quantum crypto-economics models can assess and price quantum risks in blockchain.

The study analyzes neural network predictions of knot invariants and finds that braid representations work best.

problem Understanding and predicting knot invariants using neural networks.
method Investigated different knot representations and invariants, proposed a cosine similarity score.
result Braid representations are best for predicting knot invariants, and some invariants are easier to learn than others.

This work explores how overparametrization and priors affect Bayesian neural network posteriors.

problem Symmetries, non-identifiabilities, and weight-space priors fragment and inflate BNN posteriors.
method We study the interplay between overparametrization and priors in BNN posteriors, deriving key phenomena and validating through experiments.
result Overparametrization induces structured, prior-aligned weight posterior distributions.