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We give normal forms for generic k-dimensional parametric families $(Z_\varepsilon)_\varepsilon$ of germs of holomorphic vector fields near $0\in\mathbb{C}^2$ unfolding a saddle-node singularity $Z_0$, under the condition that there exists…

Dynamical Systems · Mathematics 2018-10-12 C. Rousseau , Loïc Jean Dit Teyssier

We investigate several integer invariants of curves in 3-space. We demonstrate relationships of these invariants to crossing number and to total curvature.

Geometric Topology · Mathematics 2007-05-23 Joel Hass , J. Hyam Rubinstein , Abigail Thompson

Genetic association analyses often involve data from multiple potentially-heterogeneous subgroups. The expected amount of heterogeneity can vary from modest (e.g., a typical meta-analysis) to large (e.g., a strong gene--environment…

Methodology · Statistics 2014-04-15 Xiaoquan Wen , Matthew Stephens

We find that Koschorke's $\beta$-invariant and the triple $\mu$-invariant of link maps in the critical dimension can be computed as degrees of certain maps of configuration spaces - just like the linking number. Both formulas admit…

Geometric Topology · Mathematics 2017-11-10 Sergey A. Melikhov

Single-Index Models are high-dimensional regression problems with planted structure, whereby labels depend on an unknown one-dimensional projection of the input via a generic, non-linear, and potentially non-deterministic transformation. As…

Machine Learning · Computer Science 2024-03-14 Alex Damian , Loucas Pillaud-Vivien , Jason D. Lee , Joan Bruna

In this paper, we study a structured family of matrices whose entries are given by products of $k$-Fibonacci and $k$-Lucas numbers. For this family, we obtain explicit and unified formulas for several classical matrix invariants, including…

It has been proposed that complex populations, such as those that arise in genomics studies, may exhibit dependencies among observations as well as among variables. This gives rise to the challenging problem of analyzing unreplicated…

Machine Learning · Statistics 2018-06-08 Michael Hornstein , Roger Fan , Kerby Shedden , Shuheng Zhou

Link invariants, for 3-manifolds, are defined in the context of the Rozansky-Witten theory. To each knot in the link one associates a holomorphic bundle over a holomorphic symplectic manifold X. The invariants are evaluated for b_{1}(M)…

High Energy Physics - Theory · Physics 2007-05-23 George Thompson

Genetic prediction of complex traits and diseases has attracted enormous attention in precision medicine, mainly because it has the potential to translate discoveries from genome-wide association studies (GWAS) into medical advances. As the…

Methodology · Statistics 2022-03-24 Bingxin Zhao , Shurong Zheng , Hongtu Zhu

We introduce new skein invariants of links based on a procedure where we first apply the skein relation only to crossings of distinct components, so as to produce collections of unlinked knots. We then evaluate the resulting knots using a…

Geometric Topology · Mathematics 2019-04-04 Louis H. Kauffman , Sofia Lambropoulou

Combined inference for heterogeneous high-dimensional data is critical in modern biology, where clinical and various kinds of molecular data may be available from a single study. Classical genetic association studies regress a single…

Applications · Statistics 2017-03-22 Hélène Ruffieux , Anthony C. Davison , Jörg Hager , Irina Irincheeva

We find conditions for the connectivity of inhomogeneous random graphs with intermediate density. Our results generalize the classical result for G(n, p), when p = c log n/n. We draw n independent points X_i from a general distribution on a…

Probability · Mathematics 2012-10-25 Luc Devroye , Nicolas Fraiman

We construct a 2-variable link polynomial, called $W_L$, for classical links by considering simultaneously the Kauffman state models for the Alexander and for the Jones polynomials. We conjecture that this polynomial is the product of two…

Geometric Topology · Mathematics 2007-05-23 Thomas Fiedler

The salient properties of large empirical covariance and correlation matrices are studied for three datasets of size 54, 55 and 330. The covariance is defined as a simple cross product of the returns, with weights that decay logarithmically…

Statistical Finance · Quantitative Finance 2009-03-10 Gilles Zumbach

Nonsingular estimation of high dimensional covariance matrices is an important step in many statistical procedures like classification, clustering, variable selection an future extraction. After a review of the essential background…

Statistics Theory · Mathematics 2015-03-19 Deniz Akdemir

The aim of this paper consists of providing summation formulas for the $k$-Fibonacci numbers ($k \in \mathbb{Z}$, $k \geq 2$) and their asymptotic equivalents in terms of generalized binomial coefficients. Our main tools are the Lagrange…

Number Theory · Mathematics 2023-07-28 Bakir Farhi

Motivated by the important problem of detecting association between genetic markers and binary traits in genome-wide association studies, we present a novel Bayesian model that establishes a hierarchy between markers and genes by defining…

Applications · Statistics 2016-06-22 Ian Johnston , Timothy Hancock , Hiroshi Mamitsuka , Luis Carvalho

We present a short and unified representation-theoretical treatment of type A link invariants (that is, the HOMFLY-PT polynomials, the Jones polynomial, the Alexander polynomial and, more generally, the gl(m|n) quantum invariants) as link…

Quantum Algebra · Mathematics 2015-06-11 Hoel Queffelec , Antonio Sartori

Given any oriented link diagram, two types of new knot invariants are constructed. They satisfy some generalized skein relations. The coefficients of each invariant is from a commutative ring. Homomorphisms and representations of those…

Geometric Topology · Mathematics 2011-05-10 Zhiqing Yang

Matrix normal models have an associated 4-tensor for their covariance representation. The covariance array associated with a matrix normal model is naturally represented as a Kronecker-product structured covariance associated with the…

Computation · Statistics 2025-01-10 Quinn Simonis , Martin T. Wells
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