No Arabic abstract
Let $mathbf{X}$ be a random variable uniformly distributed on the discrete cube $left{ -1,1right} ^{n}$, and let $T_{rho}$ be the noise operator acting on Boolean functions $f:left{ -1,1right} ^{n}toleft{ 0,1right} $, where $rhoin[0,1]$ is the noise parameter, representing the correlation coefficient between each coordination of $mathbf{X}$ and its noise-corrupted version. Given a convex function $Phi$ and the mean $mathbb{E}f(mathbf{X})=ain[0,1]$, which Boolean function $f$ maximizes the $Phi$-stability $mathbb{E}left[Phileft(T_{rho}f(mathbf{X})right)right]$ of $f$? Special cases of this problem include the (symmetric and asymmetric) $alpha$-stability problems and the Most Informative Boolean Function problem. In this paper, we provide several upper bounds for the maximal $Phi$-stability. Considering specific $Phi$s, we partially resolve Mossel and ODonnells conjecture on $alpha$-stability with $alpha>2$, Li and Medards conjecture on $alpha$-stability with $1<alpha<2$, and Courtade and Kumars conjecture on the Most Informative Boolean Function which corresponds to a conjecture on $alpha$-stability with $alpha=1$. Our proofs are based on discrete Fourier analysis, optimization theory, and improvements of the Friedgut--Kalai--Naor (FKN) theorem. Our improvements of the FKN Theorem are sharp or asymptotically sharp for certain cases.
We investigate the Renyi entropy of independent sums of integer valued random variables through Fourier theoretic means, and give sharp comparisons between the variance and the Renyi entropy, for Poisson-Bernoulli variables. As applications we prove that a discrete ``min-entropy power is super additive on independent variables up to a universal constant, and give new bounds on an entropic generalization of the Littlewood-Offord problem that are sharp in the ``Poisson regime.
We derive a new coupling of the running maximum of an Ornstein-Uhlenbeck process and the running maximum of an explicit i.i.d. sequence. We use this coupling to verify a conjecture of Darling and Erdos (1956).
Let $T_{epsilon}$ be the noise operator acting on Boolean functions $f:{0, 1}^nto {0, 1}$, where $epsilonin[0, 1/2]$ is the noise parameter. Given $alpha>1$ and fixed mean $mathbb{E} f$, which Boolean function $f$ has the largest $alpha$-th moment $mathbb{E}(T_epsilon f)^alpha$? This question has close connections with noise stability of Boolean functions, the problem of non-interactive correlation distillation, and Courtade-Kumars conjecture on the most informative Boolean function. In this paper, we characterize maximizers in some extremal settings, such as low noise ($epsilon=epsilon(n)$ is close to 0), high noise ($epsilon=epsilon(n)$ is close to 1/2), as well as when $alpha=alpha(n)$ is large. Analogous results are also established in more general contexts, such as Boolean functions defined on discrete torus $(mathbb{Z}/pmathbb{Z})^n$ and the problem of noise stability in a tree model.
The purpose of this note is to share some observations and speculations concerning the asymptotic behavior of Gromov-Witten invariants. They may be indicative of some deep phenomena in symplectic topology that in full generality are outside of the reach of current techniques. On the other hand, many interesting cases can perhaps be treated via combinatorial techniques.
Trace reconstruction considers the task of recovering an unknown string $x in {0,1}^n$ given a number of independent traces, i.e., subsequences of $x$ obtained by randomly and independently deleting every symbol of $x$ with some probability $p$. The information-theoretic limit of the number of traces needed to recover a string of length $n$ are still unknown. This limit is essentially the same as the number of traces needed to determine, given strings $x$ and $y$ and traces of one of them, which string is the source. The most studied class of algorithms for the worst-case version of the problem are mean-based algorithms. These are a restricted class of distinguishers that only use the mean value of each coordinate on the given samples. In this work we study limitations of mean-based algorithms on strings at small Hamming or edit distance. We show on the one hand that distinguishing strings that are nearby in Hamming distance is easy for such distinguishers. On the other hand, we show that distinguishing strings that are nearby in edit distance is hard for mean-based algorithms. Along the way we also describe a connection to the famous Prouhet-Tarry-Escott (PTE) problem, which shows a barrier to finding explicit hard-to-distinguish strings: namely such strings would imply explicit short solutions to the PTE problem, a well-known difficult problem in number theory. Our techniques rely on complex analysis arguments that involve careful trigonometric estimates, and algebraic techniques that include applications of Descartes rule of signs for polynomials over the reals.