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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.

169,341 papers · 148 categories

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265177102 · May 202619922001200920182026
48 results for spectral inaudibility

New technique shows non-orbifold singularities are undetectable by G-invariant spectrum.

problem Detecting non-orbifold singularities using the G-invariant spectrum.
method Generalized Sunada-Pesce-Sutton technique to the G-invariant setting.
result Found an isospectral pair of spaces, one orbifold and one with non-orbifold singularities.

Paper proves naturally reductive property is inaudible for certain manifolds.

problem Proving naturally reductive property is inaudible for specific manifolds.
method Using a pair of non-compact 11-dimensional generalized Heisenberg groups, the paper proves the naturally reductive property is inaudible.
result The naturally reductive property is inaudible for certain manifolds.

Paper shows spectra can't distinguish naturally reductive manifolds.

problem Cannot distinguish naturally reductive manifolds using Laplace-Beltrami spectrum.
method Characterized naturally reductive 2-step nilpotent Lie groups via Ambrose-Singer's structures; constructed isospectral pairs of 9-dimensional nilmanifolds.
result Spectra of Laplace-Beltrami operator can't distinguish naturally reductive manifolds from non-naturally reductive ones.

It is known that the spectrum of the Laplace operator on functions of a closed Riemannian manifold does not determine the integrals of the individual fourth order curvature invariants scal2\operatorname{scal}^2, ric2|\operatorname{ric}|^2, R2|R|^2, which appear as summands in the second heat invariant a2a_2. We study the an…

2015-06-08abs ↗pdf ↗

Improved spectral clustering algorithm for better performance.

problem Improving the performance of spectral clustering algorithms.
method Developed a new performance guarantee under a weaker assumption and evaluated using a different spectral embedding map.
result Better performance guarantee under a weaker assumption and evaluation of a new spectral embedding map.

Developed a spectral theory for sinh-Gordon equation solutions.

problem Solving spectral data for simply periodic solutions of the sinh-Gordon equation.
method Defined spectral data, solved inverse problem, constructed Jacobi variety and Abel map.
result Spectral theory for sinh-Gordon equation solutions is developed and solved.

Paper defines spectral triple and computes functional for nonminimal de Rham-Hodge operator.

problem Computing spectral functions for nonminimal de Rham-Hodge operators.
method Definitions and computations of spectral triple and functional.
result Computed spectral Einstein functional for even-dimensional compact manifolds.

Graph Laplacian spectral convergence rate established for manifold learning.

problem Consistency of spectral clustering algorithms on manifold data.
method Established spectral convergence rate of graph Laplacian for a random sample of a d-dimensional compact submanifold.
result Consistency of spectral clustering algorithms via spectral convergence rate.

The paper explores rigidity theorems for spectral curvature bounds in 3-manifolds.

problem Classical rigidity results in scalar curvature geometry are extended to the spectral setting.
method Warped μμ-bubble method is systematically employed to classify stable weighted minimal hypersurfaces and establish band width estimates.
result Classification theorems and band width estimates for spectral Ricci and scalar curvatures are proven.

The abstract discusses a spectral sequence for Lie algebroids.

problem The abstract tackles the spectral sequence of Lie algebroids.
method The abstract presents a spectral sequence for Lie algebroids, generalizing classical constructions.
result The spectral sequence converges to Lie algebroid cohomology for wide Lie subalgebroids and to formal Lie algebroid cohomology for Lie subalgebroids over proper submanifolds.

New spectral functionals for Dirac operators with inner fluctuations computed.

problem Spectral functionals and Dirac operators with inner fluctuations.
method Extension of spectral functionals for Dirac operators with inner fluctuations.
result Computed spectral Einstein functional for Dirac operator with inner fluctuations on even-dimensional spin manifolds.

The paper derives spectral (0,4)-tensor functionals using the noncommutative residue.

problem Deriving spectral (0,4)-tensor functionals on compact spin manifolds.
method Using four one-forms and the Dirac operator, the noncommutative residue is applied to even-dimensional compact spin manifolds.
result Spectral (0,4)-tensor functionals are extended to a general spectral triple.

Introduces spectral-domain Wasserstein distance and Gelbrich bound for elliptical processes.

problem Estimating distances and bounds for elliptical stochastic processes.
method Defines spectral-domain W2\mathcal{W}_2 Wasserstein distance and Gelbrich bound.
result Develops new spectral-domain bounds for non-elliptical processes.

Paper introduces a new multilinear functional for spectral triples and computes its properties.

problem Computing properties of spectral triples and their associated Hodge operators.
method Introduces a new multilinear functional for spectral triples and computes its properties using noncommutative residue and perturbed de-Rham Hodge operators.
result Recover two forms, torsion of the linear connection, and four forms by the noncommutative residue and perturbed de-Rham Hodge Dirac triple.

Spectral Inference Networks learn eigenfunctions from data using optimization.

problem Learning eigenfunctions of linear operators from data.
method Spectral Inference Networks generalize Slow Feature Analysis to generic symmetric operators and use stochastic optimization.
result Spectral Inference Networks accurately recover eigenfunctions and discover interpretable representations from video data.

Defines and computes a generalized spectral action for Lorentz warped products.

problem Computing spectral actions for Lorentz warped products.
method Defines and computes the bimetric spectral Einstein-Hilbert action for Lorentz warped products.
result Derives a Kastler-Kalau-Walze type theorem for Lorentz warped products.

Proves new inequality linking spectral numbers of Lagrangians and their reductions.

problem Understanding spectral properties of Lagrangian submanifolds.
method Develops inverse reduction inequalities for spectral numbers.
result Proof of inequality between spectral numbers of Lagrangian and its reductions.

Study constant mean curvature tori in R^3 using spectral data and Whitham deformations.

problem Parameterize spectral data of constant mean curvature tori in R^3.
method Use Whitham deformations, blowups, and spectral data analysis.
result Prove the Wente family is parameterized by the bisector of the right angle.

Constructs equivariant spectral flow for Dirac-type operators on manifolds.

problem Calculating spectral flow for Dirac-type operators on manifolds with group actions.
method Equivariant spectral flow construction for paths of Dirac-type operators on manifolds.
result Relates delocalised η-invariants and ρ-invariants for different positive scalar curvature metrics.

Bayesian framework integrates spectral deconvolution with expert reasoning for robust peak estimation.

problem Challenges in extracting meaningful peaks from noisy or complex spectra.
method Bayesian spectral deconvolution coupled with a physical-property regression layer.
result Recovery of weak peaks in poly(lactic acid) IR spectra related to degradation rates.

Study on spectral sequence of Iwasawa manifold and its deformations.

problem Properties of Frölicher spectral sequence on Iwasawa manifold and its deformations.
method Determination of successive pages of the Frölicher spectral sequence.
result New examples and counterexamples on spectral sequence properties.

Spectral deconfounding improves machine learning models by reducing hidden confounding effects.

problem Machine learning models can be misled by hidden confounders, leading to unreliable predictions.
method Develops a nonlinear spectral deconfounding framework for gradient boosting that modifies boosting dynamics to slow down in confounding-aligned directions.
result Spectrally deconfounded boosting improves estimation of the target function under hidden confounding and is more scalable.

This paper improves spectral clustering for large datasets using the Nystrom method.

problem Spectral clustering's scalability issues with large datasets.
method A principled spectral clustering algorithm exploiting Nystrom approximation's spectral properties.
result Improved spectral clustering efficiency and accuracy compared to existing methods.

Dual regularized graph Laplacian improves spectral clustering for community detection.

problem Detecting clusters in networks with improved spectral clustering methods.
method Proposes dual regularized graph Laplacian for three spectral clustering approaches.
result Theoretical analysis shows DRSC and DRSLIM yield stable consistent community detection.

New method uses Chebyshev expansions to compute unbiased stochastic gradients for spectral functions.

problem Computing gradients of spectral functions is expensive and challenging.
method Combining randomized trace estimators with Chebyshev expansions for unbiased stochastic gradients.
result Developed methods for optimizing objectives involving spectral-sums with fast and stable convergence.

The paper introduces a trilinear functional to recover torsion in spectral triples.

problem Recovering torsion in noncommutative spectral triples.
method Introduces a trilinear functional for spectral triples and demonstrates its application to recover torsion.
result The trilinear functional recovers the torsion of the linear connection in canonical spectral triples.