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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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61123184245 · May 202619922001200920172026
48 results for local invertibility

Local invertibility of higher order tensor transforms on compact manifolds.

problem Invertibility of higher order tensor transforms on compact manifolds.
method Local invertibility of transverse and mixed ray transforms of tensors on compact Riemannian manifolds.
result Local invertibility of transverse and mixed ray transforms of tensors for specific dimensions.

Local invertibility of ray transforms on convex manifolds.

problem Invertibility of ray transforms on compact Riemannian manifolds with strictly convex boundary.
method Local invertibility results for transverse and mixed ray transforms of 1 and 1+1 tensors.
result Local invertibility of ray transforms near boundary points, leading to global results.

The paper proves global invertibility for certain local diffeomorphisms and biholomorphisms in higher dimensions.

problem Global invertibility of local diffeomorphisms and biholomorphisms in higher dimensions.
method The approach uses conformal geometry, complex analysis, elliptic PDEs, and topology.
result The main theorem guarantees global invertibility for specific local diffeomorphisms and biholomorphisms in higher dimensions.

Consider a Riemannian manifold in dimension n3n\geq 3 with strictly convex boundary. We prove the local invertibility, up to potential fields, of the geodesic ray transform on tensor fields of rank four near a boundary point. This problem is closely related with elastic \textit{qP}-wave tomography. Under the condition …

2018-10-25abs ↗pdf ↗

Deep neural networks are vulnerable to adversarial attacks and hard to interpret because of their black-box nature. The recently proposed invertible network is able to accurately reconstruct the inputs to a layer from its outputs, thus has the potential to unravel the black-box model. An invertible network classifier c…

2019-09-30abs ↗pdf ↗

The paper studies global invertibility of maps on Finsler manifolds.

problem Global invertibility of locally Lipschitz maps on Finsler manifolds.
method Introduces pseudo-Jacobian and studies its relations with local metric properties of the map.
result Conditions for a map to be globally invertible and covering.

Maps from metrics to Ricci curvature are locally invertible near Einstein manifolds.

problem Understanding the invertibility of maps from metrics to Ricci curvature near Einstein manifolds.
method Analyzing the invertibility of maps involving Ricci curvature, conformal classes, and mean curvature.
result The map is locally invertible near an Einstein manifold with boundary.

Distribution grids are currently challenged by frequent voltage excursions induced by intermittent solar generation. Smart inverters have been advocated as a fast-responding means to regulate voltage and minimize ohmic losses. Since optimal inverter coordination may be computationally challenging and preset local contr…

2018-07-10abs ↗pdf ↗

Study local moduli of Sasaki-Einstein metrics on specific polynomial links.

problem Understanding the local moduli of Sasaki-Einstein metrics on links of invertible polynomials.
method Analyzing Sasaki-Einstein metrics on links of invertible polynomials of cycle type and Thom-Sebastiani sums.
result For polynomials of cycle type, local moduli spaces are zero-dimensional. For Thom-Sebastiani sums, dimensions are positive.

Global invertibility proven for orientation-preserving maps without homeomorphic extension.

problem Global invertibility of orientation-preserving Sobolev maps.
method Avoiding homeomorphic extension, study of strictly orientation-preserving maps.
result Global invertibility can be achieved without homeomorphic extension.

We propose a new way of constructing invertible neural networks by combining simple building blocks with a novel set of composition rules. This leads to a rich set of invertible architectures, including those similar to ResNets. Inversion is achieved with a locally convergent iterative procedure that is parallelizable …

2019-07-18abs ↗pdf ↗

This work tackles exploding inverses in INNs, revealing and mitigating their numerical non-invertibility.

problem Exploding inverses in INNs cause numerical non-invertibility, leading to failures in various tasks.
method Derived bi-Lipschitz properties of INN building blocks, proposed regularizers for local invertibility, and stable INN designs for global invertibility.
result Bi-Lipschitz properties and stable INN designs are crucial for addressing numerical non-invertibility.

We endow the group of invertible Fourier integral operators on an open}manifold with the structure of an ILH Lie group. This is done by establishing such structures for the groups of invertible pseudodifferential operators and contact transformations on an open manifold of bounded geometry, and gluing those together vi…

1999-01-29abs ↗pdf ↗

This paper solves the inversion problem for jump processes using Markovian projections.

problem Calibrating jump-diffusion models with both local and stochastic features.
method Inverting Markovian projections for pure jump processes.
result Constructs calibrated local stochastic intensity (LSI) models for credit risk applications.

A new model decouples global and local image representations without supervision.

problem Learning decoupled global and local image representations without supervision.
method Variational auto-encoding framework with invertible generative flow.
result The model effectively learns decoupled representations of images.

In this paper we study the local magnetic ray transform of symmetric tensor fields up to rank two on a Riemannian manifold of dimension 3\geq 3 with boundary. In particular, we consider the magnetic ray transform of the combinations of tensors of different orders due to the nature of magnetic flows. We show that such …

2016-09-13abs ↗pdf ↗

We prove the local invertibility, up to potential fields, and stability of the geodesic X-ray transform on tensor fields of order 1 and 2 near a strictly convex boundary point, on manifolds with boundary of dimension n>=3. We also present an inversion formula. Under the condition that the manifold can be foliated with …

2014-10-20abs ↗pdf ↗

Characterizes the local diffeomorphism structure of the exponential in the set of skew-symmetric matrices.

problem Characterizing the local diffeomorphism structure of the exponential in the set of skew-symmetric matrices.
method Introduce the diffeomorphic logarithm of special orthogonal matrices and an efficient algorithm.
result The region containing the principal logarithm has a special multiplicity structure.

In this paper we consider the local X-ray transform for general flows. We extend the results on the local and global invertibility of the geodesic ray transform proved by Uhlmann and Vasy \cite{UV} to the X-ray transform for a general flow. The key improvement is that our argument for the ellipticity of the conjugated …

2013-04-25abs ↗pdf ↗

Under a convexity assumption on the boundary we solve a local inverse problem, namely we show that the geodesic X-ray transform can be inverted locally in a stable manner; one even has a reconstruction formula. We also show that under an assumption on the existence of a global foliation by strictly convex hypersurfaces…

2012-10-07abs ↗pdf ↗

For a ring RR, we denote by R[L]R[\mathcal L] the free RR-module spanned by the isotopy classes of singular links in S3\mathbb S^3. Given two invertible elements x,tRx,t \in R, the HOMFLY-PT skein module of singular links in S3\mathbb S^3 (relative to the triple (R,t,x)(R,t,x)) is the quotient of R[L]R[\mathcal L] by local rela…

2012-06-12abs ↗pdf ↗

In this paper, we propose a novel lower dimensional representation of a shape sequence. The proposed dimension reduction is invertible and computationally more efficient in comparison to other related works. Theoretically, the differential geometry tools such as moving frame and parallel transportation are successfully…

2011-07-29abs ↗pdf ↗

Mathematical conditions and practical computations for adversarial robustness measures are established.

problem Existence, uniqueness, and scalability of adversarial robustness measures for AI classifiers.
method Formulated and proven mathematical conditions for existence, uniqueness, and explicit analytical computation of minimal adversarial paths and distances. Practical computation demonstrated on various AI tools and synthetic benchmarks.
result Explicit mathematical conditions and practical computations for adversarial robustness measures are established.

Study on estimating invertible functions with minimax analysis.

problem Minimizing risk of estimating invertible functions on a plane.
method Introduce two types of L2L^2-risks, derive lower and upper rates for minimax values, develop an asymptotically almost everywhere invertible estimator.
result Invertibility does not reduce the complexity of the estimation problem in terms of the rate.

Let MM be a smooth closed spin (resp. oriented and totally non-spin) manifold of dimension n5n\geq 5 with fundamental group ππ. It is stated, e.g. in [RS95], that MM admits a metric of positive scalar curvature (pscm) if its orientation class in kon(Bπ)ko_n(Bπ) (resp. Hn(Bπ;Z)H_n(Bπ;\Z)) lies in the subgroup consisting of elem…

2012-03-20abs ↗pdf ↗

It is widely believed that the success of deep convolutional networks is based on progressively discarding uninformative variability about the input with respect to the problem at hand. This is supported empirically by the difficulty of recovering images from their hidden representations, in most commonly used network …

2018-02-20abs ↗pdf ↗

We consider the problem of developing a method to reconstruct a potential qq from the partial data Dirichlet-to-Neumann map for the Schrödinger equation (Δg+q)u=0(-Δ_g+q)u=0 on a fixed admissible manifold (M,g)(M,g). If the part of the boundary that is inaccessible for measurements satisfies a flatness condition in one directio…

2015-11-10abs ↗pdf ↗

Study on invariant Seifert surfaces for strongly invertible knots, showing large gaps in genus.

problem Understanding gaps in genus between strongly invertible knots and their invariant Seifert surfaces.
method Analysis of invariant Seifert surfaces and proof of genus gaps, with variants of Edmonds' theorem.
result Gap between equivariant genus and usual genus can be arbitrarily large for strongly invertible knots.

We give a detailed microlocal study of X-ray transforms over geodesics-like families of curves with conjugate points of fold type. We show that the normal operator is the sum of a pseudodifferential operator and a Fourier integral operator. We compute the principal symbol of both operators and the canonical relation as…

2010-04-07abs ↗pdf ↗

Study of Dirichlet minimizers on manifolds with boundary and their asymptotic behavior.

problem Understanding the behavior of solutions to the Allen-Cahn equation on manifolds with boundary.
method Analyzing the asymptotic behavior of Dirichlet minimizers, relating Neumann data to boundary geometry, and using invertibility of the linearized Allen-Cahn operator.
result Computed expansions of the solution to high order and established a projection theorem about Allen-Cahn solutions near minimal surfaces.

Algorithm solves covariant exterior derivative equations in small regions.

problem Solving covariant exterior derivative equations in geometric and algorithmic ways.
method Linear homotopy operator of the Poincare lemma, constraints for parallel transport equations.
result Solves covariant constant and related equations in a geometric and algorithmic way.

CF-INNs can approximate any invertible function, resolving a long-standing problem.

problem Whether CF-INNs can approximate any invertible function.
method Demonstrated CF-INNs are universal approximators for invertible functions by showing a convenient criterion.
result CF-INNs are universal approximators for invertible functions.

This work presents a novel approach to train invertible linear layers by adding rank-one perturbations.

problem Training invertible linear layers during optimization with gradient-based methods is challenging.
method Train rank-one perturbations and add them to weight matrices infrequently, keeping track of inverses and determinants.
result Invertible linear layers improve mixing and mode separation in normalizing flows.