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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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122244365487 · May 202619922001200920172026
48 results for structural simplicity

The paper establishes conditions for Riemannian connections and semi-simplicity of Lie algebras using spray structures.

problem Conditions for Riemannian connections and semi-simplicity of Lie algebras.
method Using almost product structures and spray, the paper provides necessary and sufficient conditions for these properties.
result Equivalence of semi-simplicity of Lie algebras to derived ideal coincidence, interiority of derivations, and adjoint representation semi-simplicity.

When Daan Krammer and Stephen Bigelow independently proved that braid groups are linear, they used the Lawrence-Krammer-Bigelow representation for generic values of its variables q and t. The t variable is closely connected to the traditional Garside structure of the braid group and plays a major role in Krammer's alge…

2014-11-04abs ↗pdf ↗

Neural nets learn simple distributions first, then more complex ones.

problem Understanding how neural networks generalize from simple to complex functions.
method Stochastic gradient descent training, synthetic data, CIFAR10, ImageNet pre-training.
result Neural networks initially use lower-order statistics, then higher-order ones.

The paper determines the structure of Kakimizu complexes for genus one hyperbolic knots.

problem Understanding the structure of Kakimizu complexes for genus one hyperbolic knots.
method Analyzing the simplicial complex of minimal genus Seifert surfaces in the exterior of the knots.
result The Kakimizu complex for genus one hyperbolic knots consists of a single dd-simplex for d=0,4d=0,4 and otherwise of at most two dd-simplices which intersect in a common (d1)(d-1)-face.

Algorithms that decompose a manifold into simple pieces reveal the geometric and topological structure of the manifold, showing how complicated structures are constructed from simple building blocks. This note describes a way to algorithmically construct a trisection, which describes a 44-dimensional manifold as a uni…

2017-11-07abs ↗pdf ↗

Framework reduces simplicity bias in NNs, improving OOD generalization and robustness.

problem Simplicity bias in deep learning models leads to biased predictions and poor OOD generalization.
method Proposes a framework that regularizes conditional mutual information to encourage use of diverse features.
result Demonstrates effectiveness in various settings, enhancing OOD generalization and robustness.

Low rank matrix recovery problems appear widely in statistics, combinatorics, and imaging. One celebrated method for solving these problems is to formulate and solve a semidefinite program (SDP). It is often known that the exact solution to the SDP with perfect data recovers the solution to the original low rank matrix…

2020-02-25abs ↗pdf ↗

New framework shows CC^*-simplicity for groups without certain subalgebras.

problem Characterizing CC^*-simplicity of groups.
method Introducing confined subalgebras and Uniformly Recurrent States.
result A countable discrete group is CC^*-simple if it has no non-trivial amenable confined subalgebras.

We study a natural intrinsic definition of geometric simplices in Riemannian manifolds of arbitrary dimension nn, and exploit these simplices to obtain criteria for triangulating compact Riemannian manifolds. These geometric simplices are defined using Karcher means. Given a finite set of vertices in a convex set on t…

2014-06-14abs ↗pdf ↗

Research reveals simplicity bias in random logistic map, impacting data analysis and forecasting.

problem Simplicity bias in dynamical systems and its impact on data analysis and prediction.
method Examined the logistic map and random logistic map, focusing on simplicity bias and noise effects.
result Simplicity bias is observable in the random logistic map, persisting even with small noise levels.

The Apollonius theorem is generalized for m-simplices, with applications in geometry and optimization.

problem Generalizing the Apollonius theorem for m-simplices.
method Direct generalization of the theorem to m-simplices in n-dimensional space.
result Applications in geometry and optimization, including minimal surface enclosures, simplex thickness, and root-finding methods.

In this paper, we show that Gromov-Thurston's principle works for hyperbolic 3-manifolds of infinite volume and with finitely generated fundamental group. As an application, we have a new proof of Ending Lamination Theorem. Our proof essentially relays only on Maximum Volume Law for hyperbolic 3-simplices.

2019-06-03abs ↗pdf ↗

We study prismatics sets analogously to simplical sets except that realization involves prisms, i.e., products of simplices rather than just simplices. Particular examples are the prismatic subdivision of a simplicial set S and the prismatic star of S. Both have the same homotopy type as S and in particular the latter …

2008-07-31abs ↗pdf ↗

We generalize the very well known boundary operator of the ordinary singular homology theory, defined in many books about algebraic topology. We describe a variant of this ordinary simplicial boundary operator where the usual boundary (n-1)-simplices of each n-simplex are replaced by combinations of internal (n-1)- sim…

2011-09-09abs ↗pdf ↗

The paper explores how simplicity leads to better out-of-distribution generalization in models.

problem Understanding the theoretical principles behind out-of-distribution (OOD) generalization in modern models.
method Examining diffusion models in image generation to analyze compositional generalization abilities and develop a theoretical framework for simplicity-based OOD generalization.
result The true, generalizable model corresponds to the simplest among consistent models, and this simplicity can be quantified and used to establish sample complexity guarantees.

The study reveals simplicity bias in neural networks leading to better compositional mappings.

problem Understanding when and how to encourage neural networks to learn compositional mappings.
method Examined compositional mappings through coding length and gradient descent dynamics.
result Neural networks tend to learn the simplest bijections, explaining their good generalization.

Study on simplicity of Lie skew braces, proving new results for compact cases.

problem Simplicity of Lie skew braces, focusing on compact connected cases.
method Reviewing correspondence, investigating ideals and rigidity, proving main result for compact Lie skew braces.
result Compact connected simple Lie skew braces are either trivial or have simple underlying Lie groups.

Two-layer networks favor simple features, especially in complex datasets.

problem Simplicity bias in neural networks over-reliing on simple features.
method Characterization of two-layer neural networks with small weights and gradient flow.
result Features learned in middle training stages are more useful for out-of-distribution transfer.

The study finds non-simple isotopy classes of links in 3-manifolds, including Legendrian and pseudo-Legendrian examples.

problem Characterizing isotopy classes of links in 3-manifolds, especially in contact structures.
method Developed theory of links transverse to a nowhere-zero vector field, constructing examples in both Legendrian and pseudo-Legendrian settings.
result Non-simple isotopy classes of links exist, including Legendrian and pseudo-Legendrian examples.

We give several new criteria to judge whether a simple convex polytope in a Euclidean space is combinatorially equivalent to a product of simplices. These criteria are mixtures of combinatorial, geometrical and topological conditions that are inspired by the ideas from toric topology.

2016-09-19abs ↗pdf ↗

New criteria distinguish cause from effect in data, overcoming statistical limitations.

problem Determining causal direction from statistical dependence alone.
method Intuitive criteria based on simplicity of prediction, tested on synthetic data.
result Criteria accurately distinguish cause from effect in various scenarios.

The paper proves eigenvalues are simple for specific operators on bundles.

problem Eigenvalue simplicity for connection Laplacian and GG-simplicity on bundles.
method Analyzes connections on vector bundles and principal bundles, proving eigenvalue simplicity for a residual set of connections.
result Eigenvalues of the connection Laplacian and Laplace-Beltrami operator are simple for specified conditions.

Everyone knows that the Euler characteristic of a combinatorial manifold is given by the alternating sum of its numbers of simplices. It is shown that there are other linear combinations of the numbers of simplices which are combinatorial invariants, but that all such invariants are multiples of the Euler characteristi…

2002-01-18abs ↗pdf ↗

Proves simplicity of Lyapunov exponents for specific Anosov flows.

problem Proving all Lyapunov exponents have multiplicity 1 for certain Anosov flows.
method Perturbative results for flows, modification of eigenvalues, Markov partition, and simplicity criterion.
result In a C1C^1-open and CkC^k-dense set of Anosov flows, all Lyapunov exponents have multiplicity 1.

The paper explores simple and relatively simple transformation groups and their universal coverings.

problem Understanding the structure of universal coverings of transformation groups.
method Study of relatively simple groups and generalization of Tsuboi's metric space.
result Tsuboi's metric space of Ham~(M,ω)\widetilde{\mathrm{Ham}}(M, ω) is not quasi-isometric to the half line.

We propose a new embedding method which is particularly well-suited for settings where the sample size greatly exceeds the ambient dimension. Our technique consists of partitioning the space into simplices and then embedding the data points into features corresponding to the simplices' barycentric coordinates. We then …

2020-02-05abs ↗pdf ↗

New datasets reveal neural networks can rely on simple features, leading to poor generalization.

problem Neural networks' reliance on simple features can lead to poor generalization and robustness.
method Designing datasets with varying levels of simplicity and incorporating non-robustness.
result Neural networks can exclusively rely on the simplest feature, leading to poor performance on complex data.

New findings on lightconvex boundaries in Finslerian spacetimes.

problem Understanding causal structures in Finslerian spacetimes.
method General results for indefinite Finslerian manifolds with boundary, and equivalence among boundary lightconvexity, causally simple interior, and Hausdorff space of cone geodesics.
result Equivalence among boundary lightconvexity, causally simple interior, and Hausdorff space of cone geodesics in globally hyperbolic spacetimes.