Study on irreducibility of Laplacian eigenspaces in homogeneous spaces.
problem Existence of G-invariant Riemannian metrics with irreducible Laplacian eigenspaces. method Analysis of compact homogeneous spaces G/K and their metrics. result Normal metric of rank one symmetric spaces is the only one with irreducible Laplacian eigenspaces.
Study spectral properties of graph Laplacian for manifold data.
problem Understanding spectral properties of graph Laplacian for manifold data.
method Non-asymptotic error bounds on spectral properties of empirical graph Laplacian.
result Eigenvalues and eigenspaces of empirical graph Laplacian are close to Laplace-Beltrami operator of manifold.
Let (E,h) be a holomorphic Hermitian vector bundle over a polarized manifold. We provide a canonical quantization of the Laplacian operator acting on sections of the bundle of Hermitian endomorphisms of E. If E is simple we obtain an approximation of the eigenvalues and eigenspaces of the Laplacian.
Proves spectral simplicity of Hodge Laplacian and curl operator along metric families.
problem Simplicity of Hodge Laplacian and curl operator eigenvalues along metric families.
method Generalized Teytel's method to compute meagre codimension of metrics with specific eigenvalue multiplicities.
result Simplicity of Hodge Laplacian and curl operator is not a meagre codimension 2 property.
The purpose of this paper is to provide a new proof of Bando-Mabuchi's uniqueness theorem of Kähler Einstein metrics on Fano manifolds, based on Chen's weak C^{1,1} geodesic without using any further regularities. Unlike the smooth case, the lack of regularities on the geodesic forbids us to use spectral formula of the…
In this note we prove that toric Kähler metrics on complex projective space which are also U(n)-invariant are determined by their equivariant spectrum i.e. the list of eigenvalues of the Laplacian together with weights of the torus representation on the eigenspaces.
The first eigenvalue of the Laplacian on a unique Hurwitz surface has a sevenfold multiplicity and specific numerical values.
problem Identifying the spectrum of the Laplacian on a specific Hurwitz surface.
method Analytical proof for the multiplicity and numerical identification of the first eigenvalue; numerical identification of the eigenspace representation; determination of Dirichlet domain.
result The first eigenvalue of the Laplacian on the Fricke-Macbeath surface has a sevenfold multiplicity and is contained in the interval [1.23, 1.26].
Lefschetz decompositions for Kähler manifold eigenforms identified.
problem Understanding the spectrum and eigenspaces of Laplacians on Kähler manifolds.
method Analyzing the eigenspaces of the Laplacian Δk on k-forms on a compact Kähler manifold. result The positive part of the spectrum of Δk lies in the spectrum of Δk+1. Given a smooth compact manifold with boundary, we show that the subcomplex of the deformed de Rham complex consisting of eigenspaces of small eigenvalues of the Witten Laplacian is canonically isomorphic to the Thom-Smale complex constructed by Laudenbach. Our proof is based on Bismut-Lebeau's analytic localization tec…
We focus in this work on the estimation of the first k eigenvectors of any graph Laplacian using filtering of Gaussian random signals. We prove that we only need k such signals to be able to exactly recover as many of the smallest eigenvectors, regardless of the number of nodes in the graph. In addition, we address…
In this paper we are concerned with harmonic maps and minimal immersions defined on compact Riemannian manifolds and with values in homogenous strongly harmonic manifolds. We show some results on the Morse index by varying these maps along suitable conformal vector fields. We obtain also that they are global maxima on …
Researchers create surfaces with exceptionally high Steklov eigenvalues.
problem Creating surfaces with first non-zero Steklov eigenvalue of large multiplicity.
method Constructing surfaces with specific isometry groups and gluing them based on Cayley graph structures, then analyzing the eigenspace properties.
result Surfaces with arbitrarily large multiplicity for their first non-zero Steklov eigenvalue are constructed.
Method detects trajectory outliers using Hodge Laplacian embeddings.
problem Detecting outliers in trajectory data on simplicial complexes.
method Flow-embeddings using Hodge 1-Laplacian of simplicial complexes.
result Classifies trajectories based on topological behavior.
For a given minimal Legendrian submanifold L of a Sasaki-Einstein manifold we construct two families of eigenfunctions of the Laplacian of L and we give a lower bound for the dimension of the corresponding eigenspace. Moreover, in the case the lower bound is attained, we prove that L is totally geodesic and a rig…
Laplacian mixture models identify overlapping regions of influence in unlabeled graph and network data in a scalable and computationally efficient way, yielding useful low-dimensional representations. By combining Laplacian eigenspace and finite mixture modeling methods, they provide probabilistic or fuzzy dimensionali…
The paper constructs quantizations for symplectic manifolds with specific Laplacian properties.
problem Quantization of compact symplectic manifolds with higher Landau levels.
method Develops Berezin-Toeplitz quantization using a Bochner Laplacian with specific spectral properties.
result The quantization provides a formal star-product for the lowest Landau level.
Study infinitesimal deformations of Killing spinors on nearly parallel G2-manifolds.
problem Infinitesimal deformations of Killing spinors on nearly parallel G2-manifolds.
method Examined using the correspondence between nearly parallel G2-structures and Killing spinors.
result Identified that the space of Rarita-Schwinger fields coincides with a subspace of the eigenspace of the Laplacian.
In this paper, we study the Lagrangian F-stability and Hamiltonian F-stability of Lagrangian self-shrinkers. We prove a characterization theorem for the Hamiltonian F-stability of n-dimensional complete Lagrangian self-shrinkers without boundary, with polynomial volume growth and with the second fundamental form sati…
In some other context, the question was raised how many nearly Kähler structures exist on the sphere §6 equipped with the standard Riemannian metric. In this short note, we prove that, up to isometry, there exists only one. This is a consequence of the description of the eigenspace to the eigenvalue λ=12 of the L…
New eigenvalue bounds for 3-Sasaki metrics improve previous estimates.
problem Estimating eigenvalues for 3-Sasaki metrics.
method Improved Lichnerowicz-Obata type estimates for scalar sub-Laplacian.
result Lower bounds for the first non-zero eigenvalue of 3-Sasaki metrics.
We prove effective uniformization for nearly round 2-spheres and investigate their stability.
problem Proving effective uniformization for nearly round 2-spheres and their stability.
method Utilizing an identity related to the third-order differential of the conformal factor, and an isometric embedding of a round sphere into Euclidean space using an orthogonal basis of the first eigenspace of the Laplacian operator.
result We provide a simplified proof of effective uniformization and its stability.
Efficiently approximates eigenspaces for symmetric and general matrices.
problem Fast computation of eigenspaces for large matrices.
method Factor eigenspaces into fundamental components using transformations, solve minimization problems, and iteratively update.
result Improved computational efficiency for eigenspace approximation.
Study Witten deformation on noncompact manifolds with bounded geometry.
problem Cohomology of Witten deformation on noncompact manifolds.
method Witten deformation, Agmon estimate, Witten's instanton complex.
result Cohomology of Witten deformation is isomorphic to Thom-Smale and relative cohomology.
Consider the sum of the first N eigenspaces for the Laplacian on a Riemannian manifold. A basis for this space determines a map to Euclidean space and for N sufficiently large the map is an embedding. In analogy with a fruitful idea of Kähler geometry, we define (Riemannian) Bergman metrics of degree N to be thos…
This paper tackles distributed estimation of the top-L eigenspace in PCA for large data sets.
problem Challenges in estimating the top-L eigenspace in principal component analysis for large data sets.
method Proposes a novel multi-round algorithm using shift-and-invert preconditioning and convex optimization.
result Achieves a fast convergence rate and covers the targeted top-L eigenspace without explicit eigengap assumption.
The paper calculates dimensions of higher Landau levels on compact manifolds.
problem Understanding Landau levels on compact manifolds in the large magnetic field limit.
method Computing dimensions as Riemann-Roch numbers, studying Toeplitz algebras, and proving isomorphisms.
result Each Landau level is isomorphic to a quantization twisted by an auxiliary bundle.
Clustering of data sets is a standard problem in many areas of science and engineering. The method of spectral clustering is based on embedding the data set using a kernel function, and using the top eigenvectors of the normalized Laplacian to recover the connected components. We study the performance of spectral clust…
In this note we explore a connection between finite covers of surfaces and the Teichmüller polynomial of a fibered face of a hyperbolic 3--manifold. We consider the action of a homological pseudo-Anosov homeomorphism ψ on the homology groups of a class of finite abelian covers of a surface Σg,n. Eigenspaces of t…
Our work connects parameter magnitudes and Hessian eigenspaces in deep neural nets.
problem Understanding the relationship between parameter magnitudes and Hessian curvature in deep learning models.
method Developed a matrix-free algorithm based on sketched SVDs to measure similarity between parameter masks and Hessian eigenspaces.
result Top Hessian eigenvectors tend to be concentrated around larger parameters, indicating a connection between parameter magnitudes and loss curvature.
New neural architectures invariant to sign flips and basis symmetries for graph representation learning.
problem Learning invariant graph representations from eigenvectors.
method SignNet and BasisNet neural architectures that are invariant to sign flips and basis symmetries.
result Proven to be universal, approximating any continuous function of eigenvectors with desired invariances.
New algorithm estimates eigenspace with faulty nodes, matching performance of existing methods.
problem Estimating eigenspace in distributed systems with node failures.
method Develops an eigenspace estimation algorithm for distributed environments with arbitrary node failures.
result Matches performance of existing non-robust estimator up to an additive error.
We show how to approximate large graphs with smaller ones using spectral properties.
problem How coarsening affects the spectrum of a graph.
method Conditions for the closeness of principal eigenvalues and eigenspaces of coarsened and original graph Laplacian matrices.
result Coarse eigenvectors can be used for spectral clustering without refinement.
For even dimensional conformal manifolds several new conformally invariant objects were found recently: invariant differential complexes related to, but distinct from, the de Rham complex (these are elliptic in the case of Riemannian signature); the cohomology spaces of these; conformally stable form spaces that we may…
FedPower improves eigenspace estimation privacy in federated learning.
problem Privacy breaches and communication challenges in federated eigenspace estimation.
method FedPower uses a power method with local power iterations and global aggregation, weighted by OPT, and adds Gaussian noise for privacy.
result FedPower provides convergence bounds and demonstrates effectiveness in experiments.
A method to analyze neural network performance by measuring layer saturation.
problem Understanding which layers contribute to network performance.
method Layer saturation method: restricts layer output to eigenspace of variance matrix.
result Layer saturation indicates which layers contribute to network performance.
New measure helps identify better word embedding compression methods.
problem Challenges in evaluating compressed word embeddings for downstream tasks.
method Proposed eigenspace overlap score and developed generalization bounds.
result Eigenspace overlap score correlates with better downstream performance.
For the spherical Laplacian on the sphere and for the Dirichlet Laplacian in the square}, Antonie Stern claimed in her PhD thesis (1924) the existence of an infinite sequence of eigenvalues whose corresponding eigenspaces contain an eigenfunction with exactly two nodal domains. These results were given complete proofs …
Physics-informed GP regression solves eigenvalue problems by identifying non-trivial eigenspaces.
problem Solving eigenvalue problems of linear operators with trivial solutions.
method Constructing a transfer function-type indicator using physics-informed Gaussian Process posterior.
result The posterior covariance is non-trivial only for eigenvalues of the operator, indicating non-trivial eigenspaces.
Adaptive PCA algorithm for real-time data analysis.
problem Real-time computation of eigenspace for time-varying data.
method Online adaptive PCA algorithm with one-step update rule considering second order correlations.
result The algorithm provides an excellent approximation to the original eigenspace computed using standard PCA in batch mode.
Under mean radius of curvature flow, a closed convex surface in Euclidean space is known to expand exponentially to infinity. In the 3-dimensional case we prove that the oriented normals to the flowing surface converge to the oriented normals of a round sphere whose centre is determined by the initial surface. To prove…
This work analyzes the role of data augmentation in self-supervised learning using RKHS approximation and regression.
problem Limited theoretical understanding of the role of data augmentation in self-supervised learning.
method Geometric characterization of the target function given by augmentation, proving generalization bounds.
result Two generalization bounds are derived, one free of model complexity, the other specific to near-optimal encoders.
Paper proposes an online distributed PCA algorithm for faster computation.
problem Efficiently estimating principal eigenspaces in distributed systems.
method Online distributed algorithm for principal eigenspace recovery.
result Algorithm demonstrates faster computation with comparable accuracy.
SOEM clusters time series data with improved accuracy.
problem Clustering non-aligned time series data.
method Generalizes SOFM to matrix input using approximate joint diagonalisation of covariance structures.
result SOEM produces valid topological clustering of time series data.
Paper identifies key function spaces for ReLU networks based on Fisher information.
problem Understanding the structure of Fisher information matrices in ReLU networks.
method Spectral decomposition of Fisher information matrices, focusing on the first three eigenspaces.
result The first three eigenspaces account for 97.7% of the trace of the Fisher information matrix, corresponding to spherical harmonic functions of order ≤2.
Paper constructs Thom-Smale complex using instantons from Morse functions.
problem Constructing Thom-Smale complex for Morse functions.
method Analytic instanton construction using eigenspaces of mapping cone Laplacian.
result Instanton complex is cochain isomorphic to Thom-Smale complex.
New methods compare Steklov eigenspaces of free boundary minimal surfaces in balls.
problem Comparing Steklov eigenspaces of free boundary minimal surfaces.
method Developed new methods to compare span of coordinate functions with Steklov eigenspace.
result Proved congruence of free boundary minimal annuli in 3D unit ball.
If G is a compact Lie group endowed with a left invariant metric g, then G acts via pullback by isometries on each eigenspace of the associated Laplace operator Δg. We establish algebraic criteria for the existence of left invariant metrics g on G such that each eigenspace of Δg, regarded as the real ve…
Geometric Arbitrage Theory reformulates a generic asset model possibly allowing for arbitrage by packaging all assets and their forwards dynamics into a stochastic principal fibre bundle, with a connection whose parallel transport encodes discounting and portfolio rebalancing, and whose curvature measures, in this geom…