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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.

168,742 papers · 148 categories

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48 results for vanishing moments

Uniformly K-stable toric varieties are asymptotically Chow stable if their Futaki-Ono invariant vanishes.

problem Determining asymptotic Chow stability of uniformly K-stable toric varieties.
method Detailed study of triangulations of moment polytope neighborhoods and analysis of Futaki-Ono invariant.
result Every uniformly K-stable polarized smooth toric variety with vanishing Futaki-Ono invariant is asymptotically Chow polystable.

Samplets and multiwavelets constructed from scattered data converge to specific densities in the limit.

problem Constructing data-adapted multiresolution analyses and multiwavelets with flexible vanishing moments.
method Probabilistic framework for samplet construction; convergence to multiwavelets with broken polynomial densities.
result Samplet construction converges to multiwavelets in the infinite data limit.

We extend the notion of multi-moment map to geometries defined by closed forms of arbitrary degree. We give fundamental existence and uniqueness results and discuss a number of essential examples, including geometries related to special holonomy. For forms of degree four, multi-moment maps are guaranteed to exist and a…

2011-10-29abs ↗pdf ↗

The paper examines linking numbers in grid models and finds polynomial moments.

problem Analyzing linking numbers in grid models.
method Examined linking numbers as a random variable on isotopy classes of 2-component links, computed moments and limits.
result The uuth moment of the linking number is a polynomial in the grid size with degree dud\leq u, and all odd moments vanish.

Maxout networks study gradients and propose initialization strategies.

problem Complexity in input-output Jacobian distribution complicates stable parameter initialization.
method Obtained bounds on moments of gradients and formulated initialization strategies.
result Parameter initialization strategies improve training of deep maxout networks.

Study of hyperkähler reduction on abelian varieties and toric manifolds.

problem Understanding hyperkähler reduction on specific manifolds.
method Lifts canonical Kähler reduction to hyperkähler, studies on abelian varieties and toric manifolds.
result Obtains decoupling result, variational characterisation, relation to KK-stability, and proves existence and uniqueness under suitable assumptions.

The gluing technique is used to construct hypersurfaces in Euclidean space having approximately constant prescribed mean curvature. These surfaces are perturbations of unions of finitely many spheres of the same radius assembled end-to-end along a line segment. The condition on the existence of these hypersurfaces is t…

2009-02-20abs ↗pdf ↗

Heterotic backgrounds described using generalised geometry, preserving minimal supersymmetry.

problem Characterizing heterotic backgrounds preserving minimal supersymmetry in four dimensions.
method Using generalised geometry, characterizing backgrounds by an SU(3)imesSpin(6+n)SU(3) imes Spin(6+n) structure and an involutive subbundle of the generalised tangent bundle.
result The analysis of infinitesimal deformations reproduces known cohomologies of massless moduli.

The study proves the finiteness of moments for Gaussian field zeros and critical points.

problem Finiteness of moments for Gaussian field zeros and critical points.
method Definition and study of multijets, construction of p-multijet bundles.
result Linear statistics of Gaussian field zeros have finite p-th moments for p ≥ 1.

A near-symplectic structure on a 4-manifold is a closed 2-form that is symplectic away from the 1-dimensional submanifold along which it vanishes and that satisfies a certain transversality condition along this vanishing locus. We investigate near-symplectic 4-manifolds equipped with singular Lagrangian torus fibration…

2006-09-27abs ↗pdf ↗

In the paper [Probab. Theory Relat. Fields, 100 (1994) 417-428] Xue-Mei Li has shown that the moment stability of an SDE is closely connected with the topology of the underlying manifold. In particular, she gave sufficient condition on SDE on a manifold MM under which the fundamental group π1M=0π_1 M=0. We prove that in …

2011-10-31abs ↗pdf ↗

Study on estimating rank-one tensors in noisy data with heavy tails.

problem Estimating rank-one spiked tensors in the presence of heavy tailed errors.
method Analysis of spectral norm of random tensors with iid entries.
result Signal strength requirements for optimal estimation are similar for heavy tailed and Gaussian noise, but vanish for noise with finite fourth moment.

Study of string theory flux compactifications using generalized geometry.

problem Characterizing generic Minkowski flux compactifications in string theory.
method Using E7(7)×R^+ generalized geometry, analyze involutive subbundles and moment maps.
result Counted massless scalar moduli of GMPT solutions using generalised geometry cohomology.

In this paper we show that any good toric contact manifold has well defined cylindrical contact homology and describe how it can be combinatorially computed from the associated moment cone. As an application we compute the cylindrical contact homology of a particularly nice family of examples that appear in the work of…

2010-05-20abs ↗pdf ↗

A tractable pseudo-metric for non-parametric distributions via SPD geometry.

problem Computing distances between non-parametric probability distributions is intractable.
method Two-stage framework: projection onto parametric family, embedding into SPD matrices.
result Closed-form pseudo-metric for two-sample hypothesis testing.

The paper examines how the angle between inputs in ReLU networks decreases with depth, impacting training.

problem Depth degeneracy in neural networks, leading to constant function behavior on initialization.
method Combinatorial expansions and Monte Carlo experiments to analyze the angle between inputs in ReLU networks of increasing depth.
result The angle between inputs in ReLU networks decreases exponentially with depth, leading to constant function behavior on initialization.

New method approximates MMD using pseudo-differential operators and singular values.

problem Approximating MMD with pseudo-differential operators and singular values.
method Corresponding pseudo-differential operators to Mercer kernels, approximating p(x,y)p({\mathbf x}, {\mathbf y}) with its first rr singular values.
result The new MMD distance measures the difference of two distributions with respect to rr^\ast local moments, where rr^\ast depends on singular values decay rate.

Extends probabilistic approach for Kahler-Einstein metrics on Fano manifolds.

problem Constructing Kahler-Einstein metrics on log Fano manifolds with non-discrete automorphism groups.
method Introduces Gibbs polystability and uses moment map constraint to break symmetry.
result Gibbs polystability conjectured to be equivalent to existence of Kahler-Einstein metric.

In this paper we show that, for a sub-Laplacian ΔΔ on a 33-dimensional manifold MM, no point interaction centered at a point q0Mq_0\in M exists. When MM is complete w.r.t. the associated sub-Riemannian structure, this means that ΔΔ acting on C0(M{q0})C^\infty_0(M\setminus\{q_0\}) is essentially self-adjoint. A particular …

2019-02-14abs ↗pdf ↗

For a GJR-GARCH specification with a generic innovation distribution we derive analytic expressions for the first four conditional moments of the forward and aggregated returns and variances. Moment for the most commonly used GARCH models are stated as special cases. We also the limits of these moments as the time hori…

2018-08-29abs ↗pdf ↗

This paper identifies and bounds ICE central moments using PO marginal central moments.

problem Identifying and characterizing treatment effect heterogeneity.
method Using only marginal central moments of potential outcomes, the paper identifies and bounds central moments of individual causal effects.
result Identification and bounding of central moments of ICE using marginal moments of POs.

We tackle causal inference under conditional moment restrictions using importance weighting.

problem Challenges in causal inference under conditional moment restrictions, especially in high-dimensional settings.
method Transform conditional moment restrictions to unconditional moment restrictions through importance weighting.
result Successfully estimate nonparametric functions defined under conditional moment restrictions.

A new method for estimating causal parameters from observables reduces the need for finite moment conditions.

problem Estimating causal parameters from observational data with unknown or infinite moment conditions.
method Variational Method of Moments (VMM) for a general class of estimators, including kernel and neural net-based methods.
result VMM estimators are consistent, asymptotically normal, and semiparametrically efficient.

Introduces generalized moment maps for almost Hermitian settings.

problem Extending classical moment map theory to almost Hermitian settings.
method Introduces momentumly closed forms and proves a variant of the Darboux-Weinstein theorem.
result Establishes convexity property and constructs reduction space for generalized moment maps.

Proposes Moment Exchange to use moments in image recognition models, improving generalization.

problem Discarding moments in image recognition models reduces stability and training time.
method Moment Exchange: replaces moments of learned features with another image's moments and interpolates labels.
result Improves generalization of recognition models across multiple datasets.

A new method of moments estimator goes beyond data reweighting.

problem Estimation of moment restrictions and conditional moment restrictions.
method Kernel Method of Moments (KMM) based on maximum mean discrepancy.
result KMM achieves competitive performance on conditional moment restriction tasks.

Moment Pooling reduces latent space dimensions in machine learning models.

problem High-dimensional latent spaces in machine learning models are hard to interpret.
method Moment Pooling extends Deep Sets networks to arbitrary multivariate moments.
result Latent dimensions as small as 1 can achieve similar performance to higher dimensions.