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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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3876113151 · Jun 202019922001200920172026
48 results for isotypical components

Derives Fredholm criteria for isotypical components from a Simonenko principle.

problem Finding Fredholm conditions for isotypical components of invariant pseudodifferential operators.
method General Simonenko's local principle and equivariant local principle for restriction to isotypical components.
result Full proof of equivariant local principle and extension of results.

Characterizes Fredholm conditions for group-invariant pseudodifferential operators.

problem Understanding Fredholm conditions for operators on quotient spaces.
method Study of symbol CC^*-algebra and topology of primitive ideal spectrum.
result Explicit characterization of Fredholm operators in terms of principal symbol and group action.

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.

We consider the polynomial representation S(V*) of the rational Cherednik algebra H_c(W) associated to a finite Coxeter group W at constant parameter c. We show that for any degree d of W and nonnegative integer m the space S(V*) contains a single copy of the reflection representation V of W spanned by the homogeneous …

2011-10-10abs ↗pdf ↗

Fredholm conditions for invariant operators on compact manifolds.

problem Characterizing operators with Fredholm maps induced by their action on manifolds.
method Defining transversally αα-elliptic operators and proving Fredholmness based on their principal symbols and group actions.
result Operators are Fredholm if and only if they are transversally αα-elliptic.

We determine the structure of the *-Lie superalgebra generated by a set of carefully chosen natural operators of an orientable WSD manifold of rank three. This Lie superalgebra is formed by global sections of a natural Lie superalgebra bundle, and turns out to be a product of $\mathbf{sl}(4,\C)$ with the full special…

2007-06-07abs ↗pdf ↗

We characterise the integrability of any co-CR quaternionic structure in terms of the curvature and a generalized torsion of the connection. Also, we apply this result to obtain, for example, the following. (1) New co-CR quaternionic structures built on vector bundles over a quaternionic manifold M, whose twistor space…

2013-05-14abs ↗pdf ↗

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.

The paper proposes reusable network components by making them compatible across tasks.

problem Training networks for different tasks independently leads to incompatible components.
method The paper splits a network into a features extractor and a target task head, and proposes various approaches to make them compatible.
result The proposed methods produce components that are directly compatible without compromising accuracy on original tasks.

We construct a graph G such that any embedding of G into R^{3} contains a nonsplit link of two components, where at least one of the components is a nontrivial knot. Further, for any m < n we produce a graph H so that every embedding of H contains a nonsplit n component link, where at least m of the components are nont…

2007-05-15abs ↗pdf ↗

Identifying components and estimating mixing weights in unlabeled finite mixtures under marginal independence.

problem Identifying components and estimating mixing weights in unlabeled finite mixtures.
method Proving structural results and extending them to observable mixtures.
result Identifying components and estimating mixing weights under marginal independence.

We propose a penalized orthogonal-components regression (POCRE) for large p small n data. Orthogonal components are sequentially constructed to maximize, upon standardization, their correlation to the response residuals. A new penalization framework, implemented via empirical Bayes thresholding, is presented to effecti…

2008-11-25abs ↗pdf ↗

Essential principal components simplify spectral analysis with minimal training data.

problem Accurate spectral quantification from complex mixtures.
method Identifying essential principal components and using molar extinction coefficients.
result Near one-to-one projection from principal components to mixture constituents.

Classifies connected components of meromorphic differentials with residue conditions.

problem Understanding the structure of meromorphic differentials with residue constraints.
method Analyzes the multi-scale compactification and residue conditions.
result Classified connected components of generalized strata of meromorphic differentials.

Identifies Anosov representations of hyperbolic triangle groups in SL(3,R).

problem Classifying Anosov representations of hyperbolic triangle groups into SL(3,R).
method Proving representations are Anosov if they lie in the Hitchin component or the Barbot component, with specific conditions for eigenvalues.
result Anosov representations in SL(3,R) have non-convex boundary maps.