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The directed last-passage percolation (LPP) model with independent exponential times is considered. We complete the study of asymptotic directions of infinite geodesics, started by Ferrari and Pimentel \cite{FP}. In particular, using a…

Probability · Mathematics 2011-04-08 David Coupier

Suppose that $A$ and $B$ are closed subsets of a Euclidean space such that $A\cap B\neq\varnothing$, and we aim to find a point in this intersection with the help of the sequences $(a_n)_\nnn$ and $(b_n)_\nnn$ generated by the \emph{method…

Optimization and Control · Mathematics 2013-07-11 Heinz H. Bauschke , Dominikus Noll

We develop a general theory for percolation in directed random networks with arbitrary two point correlations and bidirectional edges, that is, edges pointing in both directions simultaneously. These two ingredients alter the previously…

Disordered Systems and Neural Networks · Physics 2009-11-11 M. Boguna , M. A. Serrano

This paper proves the existence and uniqueness results (in the sense of maximally defined regularity) as well as the stability analysis for the solutions to a class of nonlocal fully-nonlinear parabolic systems, where the nonlocality stems…

Analysis of PDEs · Mathematics 2023-09-11 Qian Lei , Chi Seng Pun

We construct a non-locality game that can be won with certainty by a quantum strategy using log n shared EPR-pairs, while any classical strategy has winning probability at most 1/2+O(log n/sqrt{n}). This improves upon a recent result of…

Quantum Physics · Physics 2010-07-15 Harry Buhrman , Giannicola Scarpa , Ronald de Wolf

We derive a backward and forward nonlinear PDEs that govern the implied volatility of a contingent claim whenever the latter is well-defined. This would include at least any contingent claim written on a positive stock price whose payoff at…

Computational Finance · Quantitative Finance 2019-07-18 Peter Carr , Andrey Itkin , Sasha Stoikov

We define the probability structure of a continuous-time time-homogeneous Markov jump process, on a finite graph, that represents the continuous-time counterpart of the so-called Ruelle-Bowen discrete-time random walk. It constitutes the…

Optimization and Control · Mathematics 2018-02-14 Yongxin Chen , Tryphon T. Georgiou , Michele Pavon

This paper investigates the algorithmic dimension spectra of lines in the Euclidean plane. Given any line L with slope a and vertical intercept b, the dimension spectrum sp(L) is the set of all effective Hausdorff dimensions of individual…

Computational Complexity · Computer Science 2017-01-17 Neil Lutz , D. M. Stull

In a recent paper the author proved a theorem to the effect that the matrix of normalized Euclidean distances on the set of specially distributed random points in the $n$-dimensional Euclidean space $\mathbb R^{n}$ with independent…

Mathematical Physics · Physics 2015-09-07 A. P. Zubarev

We formulate the conditional Kolmogorov complexity of x given y at precision r, where x and y are points in Euclidean spaces and r is a natural number. We demonstrate the utility of this notion in two ways. 1. We prove a point-to-set…

Computational Complexity · Computer Science 2016-12-02 Jack H. Lutz , Neil Lutz

We examine rules for predicting whether a point in $\mathbb{R}$ generated from a 50-50 mixture of two different probability distributions came from one distribution or the other, given limited (or no) information on the two distributions,…

Statistics Theory · Mathematics 2024-04-09 Kevin Bleakley

We introduce the notion of $P_{\lambda}$ points, which canonically parametrize points on the Euler line. This allows us to show that the Euler line of any $d$-dimensional inscribed polygon in Euclidean space arises from the Euler lines of…

Metric Geometry · Mathematics 2026-02-25 Dylan Wyrzykowski

In this letter, we employ and design the expectation--conditional maximization either (ECME) algorithm, a generalisation of the EM algorithm, for solving the maximum likelihood direction finding problem of stochastic sources, which may be…

Signal Processing · Electrical Eng. & Systems 2025-08-05 Ming-yan Gong , Bin Lyu

We describe expectation propagation for approximate inference in dynamic Bayesian networks as a natural extension of Pearl s exact belief propagation.Expectation propagation IS a greedy algorithm, converges IN many practical cases, but NOT…

Artificial Intelligence · Computer Science 2013-01-07 Tom Heskes , Onno Zoeter

We prove that positive solutions of the superlinear Lane-Emden system in a two-dimensional smooth bounded domain are bounded independently of the exponents in the system, provided the exponents are comparable. As a consequence, the energy…

Analysis of PDEs · Mathematics 2022-06-01 Nikola Kamburov , Boyan Sirakov

We study exceptional sets of the local time of the continuous-time simple random walk in scaled-up (by $N$) versions $D_N\subseteq \mathbb Z^2$ of bounded open domains $D\subseteq \mathbb R^2$. Upon exit from $D_N$, the walk lands on a…

Probability · Mathematics 2023-10-05 Yoshihiro Abe , Marek Biskup

We propose a novel nonparametric online predictor for discrete labels conditioned on multivariate continuous features. The predictor is based on a feature space discretization induced by a full-fledged k-d tree with randomly picked…

Machine Learning · Computer Science 2020-02-03 Alix Lhéritier , Frédéric Cazals

Let S be a set of distinct points in general position in the Euclidean plane. A plane Hamiltonian path on S is a crossing-free geometric path such that every point of S is a vertex of the path. It is known that, if S is sufficiently large,…

Computational Geometry · Computer Science 2025-12-02 Todor Antić , Aleksa Džuklevski , Jiří Fiala , Jan Kratochvíl , Giuseppe Liotta , Morteza Saghafian , Maria Saumell , Johannes Zink

We present a short and self-contained proof of the following result: a random time is an honest time that avoids all stopping times if and only if it coincides with the (last) time of maximum of a nonnegative local martingale with zero…

Probability · Mathematics 2013-05-20 Constantinos Kardaras

Given a heterogeneous time-series sample, the objective is to find points in time (called change points) where the probability distribution generating the data has changed. The data are assumed to have been generated by arbitrary unknown…

Machine Learning · Statistics 2015-05-13 Azadeh Khaleghi , Daniil Ryabko