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Clustering is a fundamental task in unsupervised learning. The focus of this paper is the Correlation Clustering functional which combines positive and negative affinities between the data points. The contribution of this paper is two fold:…

Computer Vision and Pattern Recognition · Computer Science 2011-12-14 Shai Bagon , Meirav Galun

We extend a recent theory of parametric correlations in the spectrum of random matrices to study the response to an external perturbation of eigenvalues near the soft edge of the support. We demonstrate by explicit non-perturbative…

Condensed Matter · Physics 2009-10-22 A. M. S. Macedo

We study the distribution of zeros of general solutions of the Airy and Bessel equations in the complex plane. Our results characterize the patterns followed by the zeros for any solution, in such a way that if one zero is known it is…

Classical Analysis and ODEs · Mathematics 2014-04-01 A. Gil , J. Segura

In this paper a relative number density parameter, called the neighborhood function, is introduced so that the crowded nature of the neighborhood of individual sources can be described. With this parameter one can determine the probability…

Astrophysics · Physics 2009-11-13 Yi-Ping Qin , Lian-Zhong Lv , Fu-Wen Zhang , Bin-Bin Zhang , Jin Zhang

We introduce fast algorithms for correlation clustering with respect to the Min Max objective that provide constant factor approximations on complete graphs. Our algorithms are the first purely combinatorial approximation algorithms for…

Data Structures and Algorithms · Computer Science 2023-01-31 Sami Davies , Benjamin Moseley , Heather Newman

Gaussian Quadrature is a well known technique for numerical integration. Recently Gaussian quadrature with respect to discrete measures corresponding to finite sums have found some new interest. In this paper we apply these ideas to…

Numerical Analysis · Mathematics 2007-05-23 Hartmut Monien

In this work we study the estimation of the density of a totally positive random vector. Total positivity of the distribution of a random vector implies a strong form of positive dependence between its coordinates and, in particular, it…

Statistics Theory · Mathematics 2023-05-10 Ali Zartash , Elina Robeva

This thesis determines some of the implications of non-universal and emergent universal statistics on arithmetic correlations and fluctuations of arithmetic functions, in particular correlations amongst prime numbers and the variance of the…

Number Theory · Mathematics 2016-07-15 D. J. Smith

Cotangent sums are associated to the zeros of the Estermann zeta function. They have also proven to be of importance in the Nyman-Beurling criterion for the Riemann Hypothesis. The main result of the paper is the proof of the existence of a…

Number Theory · Mathematics 2014-10-09 Helmut Maier , Michael Th. Rassias

Consider critical Bernoulli percolation on $\mathbb{Z}^d$ for $d$ large; let $y_0, \dots, y_{k-1}$ be $k$ distinct points in $\mathbb{R}^d$. We prove that the probability that $\{\lfloor n y_i\rfloor\}_{i=0}^{k-1}$ all lie in the same open…

Probability · Mathematics 2026-04-10 Shirshendu Chatterjee , Pranav Chinmay , Jack Hanson , Philippe Sosoe

We show that if a sample of galaxy clusters is complete above some mass threshold, then hierarchical clustering theories for structure formation predict its autocorrelation function to be determined purely by the cluster abundance and by…

Astrophysics · Physics 2015-06-24 H. J. Mo , Y. P. Jing , S. D. M. White

A class of improved estimators is proposed for N-point correlation functions of galaxy clustering, and for discrete spatial random processes in general. In the limit of weak clustering, the variance of the unbiased estimator converges to…

Astrophysics · Physics 2007-05-23 István Szapudi , Alexander S. Szalay

Consider the weakly asymmetric simple exclusion processes on the one-dimensional torus. We study the non-equilibrium fluctuation of a class of additive functionals, and show that its scaling limit is a Gaussian process. The proof is mainly…

Probability · Mathematics 2023-06-05 Luiz Renato Fontes , Tiecheng Xu

We study the hole probability of Gaussian random entire functions. More specifically, we work with entire functions in Taylor series form with i.i.d complex Gaussian coefficients. A hole is the event where the function has no zeros in a…

Complex Variables · Mathematics 2010-04-07 Alon Nishry

Many statistics available to constrain non-Gaussianity from inflation are simplest to use under the assumption that the curvature correlation functions are hierarchical. That is, if the n-point function is proportional to the (n-1) power of…

Astrophysics · Physics 2010-04-30 Sarah Shandera

We consider the initial energy density in the transverse plane of a high energy nucleus-nucleus collision as a random field $\rho(\x)$, whose probability distribution $P[\rho]$, the only ingredient of the present description, encodes all…

Nuclear Theory · Physics 2014-10-06 Jean-Paul Blaizot , Wojciech Broniowski , Jean-Yves Ollitrault

We study the zeros sets of functions in the Dirichlet space. Using Carleson formula for Dirichlet integral, we obtain some new families of zero sets. We also show that any closed subset of $E \subset \TT$ with logarithmic capacity zero is…

Classical Analysis and ODEs · Mathematics 2011-03-01 Karim Kellay , Javad Mashreghi

We prove that any noetherian quasi-excellent scheme of characteristic zero admits a strong desingularization which is functorial with respect to all regular morphisms. We show that as an easy formal consequence of this result one obtains…

Algebraic Geometry · Mathematics 2019-12-19 Michael Temkin

Clustering, a fundamental activity in unsupervised learning, is notoriously difficult when the feature space is high-dimensional. Fortunately, in many realistic scenarios, only a handful of features are relevant in distinguishing clusters.…

Machine Learning · Statistics 2020-10-23 Zhiyue Zhang , Kenneth Lange , Jason Xu

We propose a simple and efficient clustering method for high-dimensional data with a large number of clusters. Our algorithm achieves high-performance by evaluating distances of datapoints with a subset of the cluster centres. Our…

Machine Learning · Computer Science 2022-03-30 Georgios Exarchakis , Omar Oubari , Gregor Lenz