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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.

168,694 papers · 148 categories

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3876113151 · Jun 202019922001200920172026
48 results for hook components

New polynomials link knot homology to Schröder paths.

problem Understanding Khovanov-Rozansky homology of Coxeter knots.
method Introduced generalized Schröder polynomials Sτ(q,t,a)S_τ(q,t,a) and proved their agreement with knot homology.
result Proved Oblomkov-Rasmussen-Shende conjecture for certain knots.

Direct proof of Alexander polynomial scaling for L-shaped representations.

problem Proving scaling property of Alexander polynomials for specific representations.
method Direct use of Reshetikhin-Turaev formalism to compute R-matrices.
result Normalized Alexander polynomial for one-hook representations scales with qRq^{|R|}.

Study spider mechanism configuration spaces using squared distance function.

problem Understand configuration spaces of spider mechanisms.
method Use Morse theory of squared distance function from body to fixed point.
result List and describe critical manifolds of squared distance function as products of polygon spaces.

Researchers establish a connection between knot homology and Lie algebra actions.

problem Understanding the HOMFLY-PT homology of (n,n+1)(n,n+1) torus knots.
method Constructing an explicit isomorphism and computing tautological class actions.
result The tautological class action extends to Hamiltonian vector fields and differentials in spectral sequences.

New methods reveal symmetries in Chern-Simons theory.

problem Understanding symmetries in Chern-Simons theory.
method Introduced a special basis in the center of the universal enveloping algebra to present group factors in arbitrary representations.
result Computed Vassiliev invariants and proved the tug-the-hook symmetry of the colored HOMFLY polynomial.

We discuss the relation between knot polynomials and the KP hierarchy. Mainly, we study the scaling 1-hook property of the coloured Alexander polynomial: ARK(q)=A[1]K(qR)\mathcal{A}^\mathcal{K}_R(q)=\mathcal{A}^\mathcal{K}_{[1]}(q^{\vert R\vert}) for all 1-hook Young diagrams RR. Via the Kontsevich construction, it is reformulated …

2018-05-07abs ↗pdf ↗

Let $ \B^{n+1} \subset \C^{n+1}$ be the unit ball in a complex Euclidean space, and let $ Σ^n = \partial \B^{n+1} = S^{2n+1}$. Let $ f: Σ^n \hook Σ^{N}$ be a local CR immersion.If Nn<2n1 N-n<2n-1, the asymptotic vectors of the second fundamental form of f f at each point form a subspace of the holomorphic tangent space of…

2006-04-18abs ↗pdf ↗

Quasimodular forms were first studied in the context of counting torus coverings. Here we show that a weighted version of these coverings with Siegel-Veech weights also provides quasimodular forms. We apply this to prove conjectures of Eskin and Zorich on the large genus limits of Masur-Veech volumes and of Siegel-Veec…

2016-06-13abs ↗pdf ↗

Novel symmetry found in colored HOMFLY polynomials from superalgebras.

problem Understanding symmetries in colored HOMFLY polynomials.
method Exploring the sl(NM)\mathfrak{sl}(N|M) superalgebra to find a symmetry.
result A symmetry relating polynomials colored by different representations.

We connect two important conjectures in the theory of knot polynomials. The first one is the property Al_R(q) = Al_{[1]}(q^{|R|}) for all single hook Young diagrams R, which is known to hold for all knots. The second conjecture claims that all the mixing matrices U_{i} in the relation {\cal R}_i = U_i{\cal R}_1U_i^{-1}…

2016-10-10abs ↗pdf ↗

Let $ X: M \hook S^5$ be a compact Legendrian surface in pseudoconformal(CR) 5-sphere. We introduce a pseudoconformally invariant Willmore type second order functional $ \W(X)$, and study its critical points called Willmore Legendrian surfaces. The fifth order structure equations show that Willmore dual can be defined …

2007-07-03abs ↗pdf ↗

From analysis of a big variety of different knots we conclude that at q which is an root of unity, q^{2m}=1, HOMFLY polynomials in symmetric representations [r] satisfy recursion identity: H_{r+m} = H_r H_m for any A, which is a generalization of the property H_r = (H_1)^r for special polynomials at q=1. We conjecture …

2015-05-22abs ↗pdf ↗

The paper computes group factors and properties of Wilson loops in Chern-Simons theory.

problem Computing group factors and properties of Wilson loops in Chern-Simons theory.
method Developed a method for computing group factors of the perturbative series expansion of Wilson loops.
result Provided a combinatorial description of group factors with clear dependence on rank and representation.

HZ transform applied to knot polynomials reveals hyperbolic knot structures.

problem Understanding the structure of knot polynomials and their factorisability.
method Applying the Harer-Zagier transform to knot polynomials and character expansions.
result Construction of an infinite family of hyperbolic knots and proof of factorisability in the 3-strand case.

Independent component analysis (ICA) decomposes multivariate data into mutually independent components (ICs). The ICA model is subject to a constraint that at most one of these components is Gaussian, which is required for model identifiability. Linear non-Gaussian component analysis (LNGCA) generalizes the ICA model t…

2017-12-23abs ↗pdf ↗

In links with two components there are three different types of crossings: self-crossings in the first component, self crossings in the second component, and crossings between components. In this paper we examine the minimum number of crossing changes needed to unlink without changing the crossings between components. …

2019-06-29abs ↗pdf ↗

A fast method estimates Gaussian mixture components without iterative fitting.

problem Estimating the number of components in high-dimensional Gaussian mixtures.
method Center data, compute singular values, and count above a threshold.
result The estimator consistently recovers the true number of components under mild separation condition.

In this paper the exact linear relation between the leading eigenvectors of the modularity matrix and the singular vectors of an uncentered data matrix is developed. Based on this analysis the concept of a modularity component is defined, and its properties are developed. It is shown that modularity component analysis …

2015-10-19abs ↗pdf ↗

New simulations advise caution in choosing principal components for multivariate functional data.

problem Inaccurate selection of principal components in multivariate functional data.
method Extensive simulations investigating the reliability of percentage of variance explained thresholds.
result Conventional threshold methods may fail to accurately explain overall variance in multivariate functional data.

Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer across models. We hypothesize that adversarial weakness is composed of three sources of bias: architecture, dataset, and random initialization.…

2018-12-04abs ↗pdf ↗

The object of this paper is to study GL(2,R) orbit closures in hyperelliptic components of strata of abelian differentials. The main result is that all higher rank affine invariant submanifolds in hyperelliptic components are branched covering constructions, i.e. every translation surface in the affine invariant subman…

2015-08-21abs ↗pdf ↗

FMM fails to accurately determine the number of components even with consistent posterior.

problem Determining the number of subpopulations in a data set using FMM.
method Analysis of FMM component-count posterior under model misspecification.
result FMM component-count posterior diverges under model misspecification, contrary to intuition.

msPCA solves sparse PCA for multiple components efficiently.

problem Sparse principal component analysis with multiple components.
method Alternating maximization algorithm for sparse loading vectors, with orthogonality or zero correlation constraints.
result Achieves high variance explained with sparse components and controlled feasibility violations.

Principal component regression (PCR) is a two-stage procedure that selects some principal components and then constructs a regression model regarding them as new explanatory variables. Note that the principal components are obtained from only explanatory variables and not considered with the response variable. To addre…

2014-02-26abs ↗pdf ↗

Bayesian approach learns nonparametric mixture components from heterogeneous data.

problem Realistic modeling of heterogeneous data populations with nonparametric mixture components.
method Bayesian nonparametric modeling using Dirichlet process mixture priors.
result Posterior contraction rates for component densities are nearly polynomial, improving over deconvolution methods.

System learns to combine multiple model components for personalized text generation.

problem Adapting and biasing language models for personal preferences.
method Combines model-defined components, learns activation and probability combination from unlabeled text.
result Directly generates text with personalized components from unlabeled data.