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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.

168,695 papers · 148 categories

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95191286381 · Jun 202019922001200920172026
48 results for second inner variation

Study bounds the index of minimal submanifolds using energy measures and Yang-Mills-Higgs equations.

problem Bounding the index of codimension 2 minimal submanifolds.
method Second inner variation of energy, convergence of energy measures, and stress-energy tensors.
result Bound the Morse index of the submanifold by the index of critical points.

Study on combustion theory solutions, proving nondegeneracy and stability in limit.

problem One-phase singular perturbation problem in combustion theory.
method Introduce density condition to preserve nondegeneracy, classify stable solutions.
result Global stable solutions have flat level sets in dimensions ≤ 4.

The paper studies knot quandles and their cohomology, proving infinite dimensionality results.

problem Understanding the cohomology of knot quandles and its implications in knot theory.
method Analyzing quandles and their inner automorphism groups, proving conditions for infinite dimensionality.
result The second bounded cohomology of knot quandles is infinite dimensional, detecting the unknot.

Study on renormalized volume of minimal submanifolds in Poincare-Einstein manifolds.

problem Analyzing minimal submanifolds in Poincare-Einstein manifolds.
method Deriving formulae for first and second variations of renormalized volume, proving asymptotic descriptions, and deriving inner-product relationships.
result Existence of asymptotic description and L2L^2-inner-product relationship for specific cases.

This research improves multimodal systems by adding a second objective and regularisation methods.

problem Improving performance of multimodal systems with multiple objectives and regularisation.
method Introduces a second objective over multimodal fusion using variational inference and regularisation methods.
result Demonstrates potential for multiple objectives and probabilistic methods to lower variance and improve generalisation.

We point out that the Homfly polynomial (that is to say, Ocneanu's trace functional) contains two polynomial-valued inner products on the Hecke algebra representation of Artin's braid group. These bear a close connection to the Morton-Franks-Williams inequality. In these structures, the sets of positive, respectively n…

2009-06-30abs ↗pdf ↗

A new algorithm tackles bilevel optimization with multiple inner minima.

problem Challenges in bilevel optimization with multiple inner minima.
method Reformulated as constrained optimization, solved via primal-dual bilevel optimization (PDBO) algorithm.
result First non-asymptotic convergence guarantee for bilevel optimization with multiple inner minima.

Paper proposes a new method to optimize feature coordinates for better image classification.

problem Improving feature extraction for better machine learning classification.
method Mutual-energy inner product optimization method.
result The method enhances low-frequency features and suppresses high-frequency noise, leading to better classification results.

We discuss the asymptotic lower bound on the inner radius of nodal domains that arise from Laplacian eigenfunctions φλ φ_λ on a closed Riemannian manifold (M,g) (M,g) . First, in the real-analytic case we present an improvement of the currently best known bounds, due to Mangoubi (\cite{Man1}). Furthermore, using recent re…

2016-07-13abs ↗pdf ↗

Inner product-based convolution has been a central component of convolutional neural networks (CNNs) and the key to learning visual representations. Inspired by the observation that CNN-learned features are naturally decoupled with the norm of features corresponding to the intra-class variation and the angle correspond…

2018-04-22abs ↗pdf ↗

Semi-Implicit Variational Inference (SIVI) is improved with SIVI-SM using score matching.

problem Intractable densities in variational distributions hinder SIVI training.
method SIVI-SM uses score matching to handle intractable densities in a minimax formulation.
result SIVI-SM outperforms ELBO-based SIVI methods in Bayesian inference tasks.

We introduce the variational graph auto-encoder (VGAE), a framework for unsupervised learning on graph-structured data based on the variational auto-encoder (VAE). This model makes use of latent variables and is capable of learning interpretable latent representations for undirected graphs. We demonstrate this model us…

2016-11-21abs ↗pdf ↗

Improved UIVI method shows better performance than state-of-the-art SIVI methods.

problem Estimating the likelihood of samples from complex distributions in high dimensions.
method Replaced the inner MCMC loop of UIVI with importance sampling and learned the optimal proposal distribution.
result The refined UIVI approach demonstrates superior performance or parity with state-of-the-art methods.

In Riemann geometry, the relations among two transversal submanifolds and global manifold are discussed. By replacing the normal vector of a submanifold with the tangent vector of another submanifold, the metric tensors, Christoffel symbols and curvature tensors of the three manifolds are linked together. When the inne…

1999-01-27abs ↗pdf ↗

We derive the first and second variation formula for the Green's function pole's value of Paneitz operator on the standard three sphere. In particular it is shown that the first variation vanishes and the second variation is nonpositively definite. Moreover, the second variation vanishes only at the direction of confor…

2015-04-08abs ↗pdf ↗

Black-box variational inference tries to approximate a complex target distribution though a gradient-based optimization of the parameters of a simpler distribution. Provable convergence guarantees require structural properties of the objective. This paper shows that for location-scale family approximations, if the targ…

2019-01-24abs ↗pdf ↗

We show a very simple and general total second variation formula for Perelman's W\mathcal{W}-functional at arbitrary points in the space of Riemannian metrics. Moreover we perform a study of the properties of the variations of Kähler structures. We deduce a quite simple and general total second variation formula for P…

2012-01-04abs ↗pdf ↗

SOAR improves deep networks' robustness against adversarial examples.

problem Improving deep neural networks' robustness against adversarial examples.
method Formulated adversarial robustness problem under robust optimization framework, approximated loss function using second-order Taylor series expansion.
result SOAR significantly improves robustness of networks against adversarial perturbations.

New methods model gamma-ray data to better understand Galactic emissions.

problem Uncertain diffuse Galactic gamma-ray emissions bias data interpretation.
method Gaussian processes and variational inference for flexible modeling.
result More robust interpretation of gamma-ray sky, especially dark matter signals.

Author presents the second variational formula for statistical biharmonic maps.

problem Developing a formula for statistical biharmonic maps.
method Introduced the second variational formula for the statistical bi-energy functional.
result The second variational formula can be represented using Hessian curvature in Hessian manifolds.

Improved Kalman filtering with hierarchical variational approach.

problem Inconsistent process covariance estimation and slow convergence speed in traditional variational Kalman filtering.
method Introducing a surrogate variable for process-noise-free state, reformulating CAVI, and sliding-window hyperparameter estimation.
result Enhanced convergence speed and superior estimation accuracy compared to existing methods.

Paper derives second variational formula for statistical manifold mappings.

problem Variational formulas for mappings between statistical manifolds.
method Develops second variational formula for harmonic mappings, defines stability, index, and nullity.
result Shows weakly stability for harmonic mappings into statistical manifolds of non-positive curvature.

Study variational problems for integral invariants of maps between pseudo-Riemannian manifolds.

problem Understanding variational properties of integral invariants defined from the second fundamental form.
method Derive first variational formulae for integral invariants of degree two, show Euler-Lagrange equation for Chern-Federer energy, and provide examples of submanifolds.
result The Euler-Lagrange equation of the Chern-Federer energy functional reduces to a second order PDE.

A method to robustly federate learning with non-i.i.d. data and Byzantine workers.

problem Byzantine workers sending malicious messages in federated learning with non-i.i.d. data.
method Resampling strategy to reduce inner and outer variation, stochastic average gradient, robust geometric median aggregation.
result The method reaches a neighborhood of the optimal solution at a linear convergence rate and learning error depends on the number of Byzantine workers.

Improved diffusion sampling for inverse problems with faster and more robust inference.

problem High computational cost and lack of robustness in diffusion posterior sampling.
method Amortized variational inference with explicit likelihood guidance.
result Improved trade-off between inference speed and robustness to unseen degradations.

The paper calculates variations of Einstein-Hilbert action on CR manifolds.

problem Variation of the Einstein-Hilbert action in pseudohermitian geometry.
method Computed first and second variations on CR manifolds, characterized critical points as pseudo-Einstein structures, and analyzed second variation on standard spheres.
result In three dimensions, the second variation of the Einstein-Hilbert action on CR structures differs from the Riemannian case due to embeddability.

We show that on a surface locally every affine torsion-free connection is projectively equivalent to a Weyl connection. First, this is done using exterior differential system theory. Second, this is done by showing that the solutions of the relevant PDE are in one-to-one correspondence with the sections of the `twistor…

2009-10-14abs ↗pdf ↗

The paper is mainly devoted to systematic developments and applications of geometric aspects of second-order variational analysis that are revolved around the concept of parabolic regularity of sets. This concept has been known in variational analysis for more than two decades while being largely underinvestigated. We …

2019-08-31abs ↗pdf ↗

In this paper we provide a detailed proof of the second variation formula, essentially due to Richard Hamilton, Tom Ilmanen and the first author, for Perelman's νν-entropy. In particular, we correct an error in the stability operator stated in Theorem 6.3 of [2]. Moreover, we obtain a necessary condition for linearly …

2010-08-04abs ↗pdf ↗

Inverted file and asymmetric distance computation (IVFADC) have been successfully applied to approximate nearest neighbor search and subsequently maximum inner product search. In such a framework, vector quantization is used for coarse partitioning while product quantization is used for quantizing residuals. In the ori…

2019-03-25abs ↗pdf ↗

PVI improves SIVI by directly optimizing ELBO without parametric assumptions.

problem Intractable variational densities in SIVI methods.
method Particle Variational Inference (PVI) using empirical measures to approximate optimal mixing distributions.
result PVI directly optimizes the ELBO and performs favorably compared to other SIVI methods.

Schwarzian derivative connects to Euler-Lagrange equations in variational calculus.

problem Understanding the relationship between the Schwarzian derivative and variational equations.
method Analyzing the Schwarzian derivative as a first integral and Euler-Lagrange operator for specific variations.
result The Schwarzian derivative is both a first integral and the Euler-Lagrange operator for a certain class of variations.

A surface M is called p-minimal if one of the coordinate functions is p-harmonic in the inner metric. We show that in the twodimensional case the Gaussian map of such surfaces is quasiconformal. In the case when the surface is a tube we study the geometrical structure of such surfaces. In particularly, we establish the…

2009-03-01abs ↗pdf ↗