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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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3671107142 · Jun 202619922001200920172026
48 results for Injective flows

Injective flows for star-like manifolds improve variational inference efficiency.

problem Efficiently modeling densities on star-like manifolds with exact Jacobian computation.
method Proposed injective flows for star-like manifolds with exact Jacobian computation.
result Exact Jacobian computation for star-like manifolds reduces computational cost to NFs.

Researchers study injectivity of magnetic and thermostatic nonabelian ray transforms on compact surfaces.

problem Injectivity of magnetic and thermostatic nonabelian ray transforms on compact surfaces.
method Loop group factorization method for nontrapping λλ-geodesic flows and the general linear group of invertible complex matrices.
result General injectivity question of the nonabelian ray transform for simple magnetic flows is settled.

We construct a uniform local bound of curvature operator from local bounds of Ricci curvature and injectivity radius among all nn-dimensional Ricci flows. Thus new compactness theorems for the Ricci flow and Ricci solitons are derived. In particular, we show that every Ricci flow with RicK|Ric|\leq K must satisfy $|Rm|\…

2016-02-05abs ↗pdf ↗

A new method improves generative models by learning lower-dimensional representations.

problem Normalizing flows cannot learn lower-dimensional representations of data.
method Noisy injective flows (NIF) that map latent space to a learnable manifold in high-dimensional data space using injective transformations and an additive noise model.
result Simple application of NIF to existing flow architectures significantly improves sample quality and yields separable data embeddings.

Optimal control problem for firm cash flow with dividend and capital injection strategies.

problem Maximizing dividends while managing capital injections in a firm's cash flow.
method Proved two optimal strategies: mean-reverting dividends with capital injections or no injections until ruin.
result Optimal strategies are dichotomous: either mean-reverting dividends with injections or no injections.

Invertible flow-based generative models are an effective method for learning to generate samples, while allowing for tractable likelihood computation and inference. However, the invertibility requirement restricts models to have the same latent dimensionality as the inputs. This imposes significant architectural, memor…

2020-02-20abs ↗pdf ↗

For Anosov flows preserving a smooth measure on a closed manifold M\mathcal{M}, we define a natural self-adjoint operator ΠΠ which maps into the space of invariant distributions in u<0Hu(M)\cap_{u<0} H^{u}(\mathcal{M}) and whose kernel is made of coboundaries in s>0Hs(M)\cup_{s>0} H^{s}(\mathcal{M}). We describe relations to Liv…

2014-08-20abs ↗pdf ↗

This paper proposes a deep neural network approach for predicting multiphase flow in heterogeneous domains with high computational efficiency. The deep neural network model is able to handle permeability heterogeneity in high dimensional systems, and can learn the interplay of viscous, gravity, and capillary forces fro…

2019-10-21abs ↗pdf ↗

Latent Noise Injection improves synthetic data generation for privacy and statistical alignment.

problem Slow convergence of generative models in high-dimensional settings.
method Latent Noise Injection using Masked Autoregressive Flows (MAF).
result Synthetic data closely reflects the underlying distribution, especially in high-dimensional settings.

Utilizing a splitting of geometric flows on surfaces introduced by Buzano and Rupflin, we present a general scheme to prove blow up criteria for such geometric flows. A vital ingredient is a new compactness theorem for families of metrics on surfaces with a uniform bound on their volumes, square integrals of their curv…

2018-03-15abs ↗pdf ↗

Estimates network structure from node potentials and edge flows under Gaussian injection statistics.

problem Estimating network structure from node potentials and edge flows under Gaussian injection statistics.
method Proposes an 1\ell_{1}-regularized maximum likelihood estimator for high-dimensional network structure estimation.
result Establishes sufficient conditions for exact sparsity recovery of network structure with high probability.

Uniform bounds on SO(2)imesSO(3)SO(2) imes SO(3)-invariant Ricci solitons on S4\mathbb{S}^4.

problem Bounding SO(2)imesSO(3)SO(2) imes SO(3)-invariant Ricci solitons on S4\mathbb{S}^4.
method Established uniform constant C\mathcal{C} for bounded curvature, volume, and injectivity radius.
result Strong evidence suggests that only round SO(2)imesSO(3)SO(2) imes SO(3)-invariant Ricci solitons on S4\mathbb{S}^4 exist.

A fundamental tool in the analysis of Ricci flow is a compactness result of Hamilton in the spirit of the work of Cheeger, Gromov and others. Roughly speaking it allows one to take a sequence of Ricci flows with uniformly bounded curvature and uniformly controlled injectivity radius, and extract a subsequence that conv…

2011-10-17abs ↗pdf ↗

In recent years, there has seen much interest and increased research activities on Perelman's paper. Section one and two of this paper aim to establish Perelman's local non-collapsing result for the Ricci flow. This will provide a positive lower bound on the injectivity radius for the Ricci flow under blow-up analysis.…

2010-04-11abs ↗pdf ↗

This paper analyzes deep and wide transformer training dynamics.

problem Understanding the training dynamics of infinitely deep and wide transformers.
method Develops a mean-field framework for gradient-based training of transformers, controlling a neural PDE.
result Establishes a rigorous foundation for gradient-based transformer training, proving convergence to global minima.

Avoids noncompact hypersurfaces from touching in evolving flows.

problem Preventing noncompact hypersurfaces from touching in evolving flows.
method Analyzes mean curvature flow and weak set flows in Euclidean and Riemannian spaces.
result Proves that noncompact hypersurfaces remain disjoint in evolving flows.

We show that three-dimensional homogeneous Ricci flow solutions that admit finite-volume quotients have long-time limits given by expanding solitons. We show that the same is true for a large class of four-dimensional homogeneous solutions. We give an extension of Hamilton's compactness theorem that does not assume a l…

2005-09-27abs ↗pdf ↗

Assume (M,g,Ω) is a closed, oriented Riemannian surface equipped with an Anosov magnetic flow. We establish certain results on the surjectivity of the adjoint of the magnetic ray transform, and use these to prove the injectivity of the magnetic ray transform on sums of tensors of degree at most two. In the final sectio…

2012-08-29abs ↗pdf ↗

In this paper we will give a simple proof of a modification of a result on pseudolocality for the Ricci flow by P.Lu without using the pseudolocality theorem 10.1 of Perelman [P1]. We also obtain an extension of a result of Hamilton on the compactness of a sequence of complete pointed Riemannian manifolds $\{(M_k,g_k(t…

2009-08-06abs ↗pdf ↗

We prove a completely new integral criterion for the existence and completeness of the wave operators W±(Δh,Δg,Ig,h)W_{\pm}(-Δ_h,-Δ_g, I_{g,h}) corresponding to the (unique self-adjoint realizations of) the Laplace-Beltrami operators Δj-Δ_j, j=1,2j=1,2, that are induced by two quasi-isometric complete Riemannian metrics gg and hh o…

2017-09-05abs ↗pdf ↗

We construct a Wasserstein gradient flow of the maximum mean discrepancy (MMD) and study its convergence properties. The MMD is an integral probability metric defined for a reproducing kernel Hilbert space (RKHS), and serves as a metric on probability measures for a sufficiently rich RKHS. We obtain conditions for conv…

2019-06-11abs ↗pdf ↗

We prove the short-time existence of Ricci flows on complete manifolds with scalar curvature bounded below uniformly, Ricci curvature bounded below by a negative quadratic function, and with almost Euclidean isoperimetric inequality holds locally. In particular, this result applies to manifolds with both Ricci curvatur…

2016-10-06abs ↗pdf ↗

Let (M3,g0)(M^3,g_0) be a complete noncompact Riemannian 3-manifold with nonnegative Ricci curvature and with injectivity radius bounded away from zero. Suppose that the scalar curvature R(x)0R(x)\to 0 as xx\to \infty. Then the Ricci flow with initial data (M3,g0)(M^3,g_0) has a long time solution. This extends a recent result of …

2008-07-01abs ↗pdf ↗

Optimal portfolio tracking with dynamic capital injection into a ratcheting benchmark.

problem Optimizing a portfolio's performance by dynamically adding capital to a non-decreasing benchmark.
method Formulated as an unconstrained control problem with a running maximum cost, transformed into an auxiliary problem with a nonlinear HJB equation, solved using probabilistic representation and stochastic flow analysis.
result Established the existence of a unique classical solution to the HJB equation, providing feedback optimal portfolio strategies.

We show that an eternal solution to a complete, locally conformally flat Yamabe flow, tg=Rg\frac{\partial}{\partial t} g = -Rg, with uniformly bounded scalar curvature and positive Ricci curvature at t=0t = 0, where the scalar curvature assumes its maximum is a gradient steady soliton. As an application of that, we study t…

2007-05-24abs ↗pdf ↗

A new gradient flow for MMD with closed-form implementation.

problem Existing gradient flows either lack tractable numerical implementation or require strong assumptions.
method Introduces a (de)-regularized Maximum Mean Discrepancy (DrMMD) and its gradient flow.
result Guarantees near-global convergence for a broad class of targets in both continuous and discrete time.

Wave operators and spectral stability for Dirac operators under Ricci flow.

problem Stability of the absolutely continuous spectrum of Dirac operators under Ricci flow.
method Proving existence and completeness of wave operators for Dirac operators and their squares under Ricci flow.
result Criterion for spectral stability of Dirac operators and their squares under Ricci flow without injectivity radius assumptions.

This work extends the randomized shortest paths (RSP) model by investigating the net flow RSP and adding capacity constraints on edge flows. The standard RSP is a model of movement, or spread, through a network interpolating between a random-walk and a shortest-path behavior [30, 42, 49]. The framework assumes a unit f…

2019-10-04abs ↗pdf ↗

Improved physics-integrated generative models with noise robustness and fidelity.

problem Enhancing generative models to produce outputs that comply with physical laws and improve generalization.
method Integrating variational autoencoder with planar normalizing flow and attention mechanisms to learn latent posterior distributions and mitigate noise.
result Significant improvement in reconstruction quality and robustness against noise.

We present a monotonic expression for the Ricci flow, valid in all dimensions and without curvature assumptions. It is interpreted as an entropy for a certain canonical ensemble. Several geometric applications are given. In particular, (1) Ricci flow, considered on the space of riemannian metrics modulo diffeomorphism …

2002-11-11abs ↗pdf ↗

A new method for learning manifolds efficiently using canonical basis functions.

problem Learning manifolds in high-dimensional data with efficient and distinct latent dimensions.
method Proposes a novel optimization objective to enforce a transformation matrix with a few prominent and non-degenerate basis functions.
result Demonstrates that minimizing the off-diagonal manifold metric elements 1\ell_1-norm results in a more efficient latent space representation.