arXiv research

Study shows stability of Schrödinger operator spectral data on a manifold.

Paper shows stability of metric reconstruction for orbifolds from spectral data.

SC-Net learns interpretable filters for inverse problems, achieving optimal convergence and super-resolution.

Inverse spectral theory reveals shapes from sound.

Study shows stability of travel time data reconstruction from closed subsets.

Study magnetic potentials on Anosov manifolds using spectral data.

New methods tackle statistical inverse problems with random data.

Many problems in machine learning and statistics can be formulated as (generalized) eigenproblems. In terms of the associated optimization problem, computing linear eigenvectors amounts to finding critical points of a quadratic function subject to quadratic constraints. In this paper we show that a certain class of con…

The paper studies magnetic field effects on surface eigenvalues and spectral properties.

In this paper we consider two inverse problems on a closed connected Riemannian manifold $(M,g)$. The first one is a direct analog of the Gel'fand inverse boundary spectral problem. To formulate it, assume that $M$ is divided by a hypersurface $Σ$ into two components and we know the eigenvalues $λ_j$ of the Laplace ope…

New method for mixed memberships using symmetrized Laplacian inverse matrix.

This paper is devoted to an inverse Steklov problem for a particular class of n-dimensional manifolds having the topology of a hollow sphere and equipped with a warped product metric. We prove that the knowledge of the Steklov spectrum determines uniquely the associated warping function up to a natural invariance.

Paper presents a unique method to recover signals from their bispectrum.

Study finds finitely many non-congruent polygonal domains with same Steklov spectrum.

We consider the geometric inverse problem of determining a closed Riemannian manifold from measurements of the heat kernel in an open subset of the manifold. In this paper we analyze the stability of this problem in the class of $n$-dimensional Riemannian manifolds with bounded diameter and sectional curvature. It is w…

In this work a spectral theory for 2-dimensional, simply periodic, complex-valued solutions u of the sinh-Gordon equation is developed. Spectral data for such solutions are defined (following Hitchin and Bobenko) and the space of spectral data is described by an asymptotic characterization. Using methods of asymptotic …

We study the inverse spectral problem for weighted projective spaces using wave-trace methods. We show that in many cases one can "hear" the weights of a weighted projective space.

We prove inverse spectral results for differential operators on manifolds and orbifolds invariant under a torus action. These inverse spectral results involve the asymptotic equivariant spectrum, which is the spectrum itself together with "very large" weights of the torus action on eigenspaces. More precisely, we show …

Variational Gaussian Processes solve linear inverse problems efficiently.

This note summarizes results that were obtained by the author in his habilitation thesis (arXiv:1607.08792) concerning the development of a spectral theory for simply periodic, 2-dimensional, complex-valued solutions of the sinh-Gordon equation. Spectral data for such solutions are defined for periodic Cauchy data on a…

The paper reformulates regression in infinite dimensions as an inverse problem, showing it's equivalent to compact inverse problems.

Paper tackles estimating initial conditions of spatio-temporal processes from sparse data.

Researchers approximate spectral targets on manifolds with constant negative curvature.

We consider the problem of finding sufficient conditions for a locally Lipschitz mapping between Finsler manifolds to be a global homeomorphism. For this purpose, we develop the notion of Clarke generalized differential in this context and, using this, we obtain a version of the Hadamard integral condition for invertib…

While sparse inverse covariance matrices are very popular for modeling network connectivity, the value of the dense solution is often overlooked. In fact the L2-regularized solution has deep connections to a number of important applications to spectral graph theory, dimensionality reduction, and uncertainty quantificat…

The paper defines surface area for graphs and derives spectral estimates.

We define a pseudo-inverse for line graphs using linear integer programming.

The main results of this paper are an asymptotic expansion in powers of $\hbar$ for the spectral measure $μ_\hbar$ of a semi-classical Toeplitz operator, $Q_\hbar$, and an equivariant version of this result when $Q_\hbar$ admits an $n$-torus as a symmetry group. In addition we discuss some inverse spectral consequences…

In this paper we will extend to non-abelian groups inverse spectral results, proved by us in an earlier paper, for compact abelian groups, i.e. tori. More precisely, Let $\mathsf G$ be a compact Lie group acting isometrically on a compact Riemannian manifold $X$. We will show that for the Schrödinger operator $-\hbar^2…

New method for inferring time series graph from sparse-group log-sum penalty.

Bayesian method estimates Kronecker graphical models from autoregressive processes.

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

WS diffusion models handle anisotropic Gaussian noise better than conventional methods.

We consider a statistical inverse learning problem, where we observe the image of a function $f$ through a linear operator $A$ at i.i.d. random design points $X_i$, superposed with an additive noise. The distribution of the design points is unknown and can be very general. We analyze simultaneously the direct (estimati…

Paper tackles functional linear regression using spectral algorithms with discrete observations.

The paper solves the Steklov spectral inverse problem for conformal metrics.

The Hodge spectra can distinguish orbifolds from manifolds, especially in low dimensions.

Near isospectrality forces full isospectrality for compact quotients of symmetric spaces.

This article is about inverse spectral problems for hyperbolic surfaces and in particular how length spectra relate to the geometry of the underlying surface. A quantitative answer is given to the following: how many questions do you need to ask a length spectrum to determine it completely? In answering this, a quantit…

This article considers inverse problems on closed Riemannian surfaces whose geodesic flow is Anosov. We prove spectral rigidity for any Anosov surface and injectivity of the geodesic ray transform on solenoidal 2-tensors. We also establish surjectivity results for the adjoint of the geodesic ray transform on solenoidal…

Proposes a model for classifying high-dimensional time series with interpretable parameters.

Analyzes how diffusion models learn, revealing a spectral bias in structure mastery.

The wave trace of certain convex domains can be smooth near some points in the length spectrum.

Graph curvature measured by inverse resistance distance.

In this paper, we obtain analytical expression for the distribution of the occupation time in the red (below level $0$) up to an (independent) exponential horizon for spectrally negative Lévy risk processes and refracted spectrally negative Lévy risk processes. This result improves the existing literature in which only…

We revisit the dividend payment problem in the dual model of Avanzi et al. ([2], [1], and [3]). Using the fluctuation theory of spectrally positive Lévy processes, we give a short exposition in which we show the optimality of barrier strategies for all such Lévy processes. Moreover, we characterize the optimal barrier …

This paper provides a full controlled version of algebraic $K$-theory. This includes a rich array of assembly maps; the controlled assembly isomorphism theorem identifying the controlled group with homology; and the stability theorem describing the behavior of the inverse limit as the control parameter goes to 0. There…

Recently, non-stationary spectral kernels have drawn much attention, owing to its powerful feature representation ability in revealing long-range correlations and input-dependent characteristics. However, non-stationary spectral kernels are still shallow models, thus they are deficient to learn both hierarchical featur…