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The Hardy--Littlewood inequalities for $m$-linear forms have their origin with the seminal paper of Hardy and Littlewood (Q.J. Math, 1934). Nowadays it has been extensively investigated and many authors are looking for the optimal estimates…

Functional Analysis · Mathematics 2017-05-16 Wasthenny Vasconcelos Cavalcante

It is well-known that for a harmonic function $u$ defined on the unit ball of the $d$-dimensional Euclidean space, $d\geq 2$, the tangential and normal component of the gradient $\nabla u$ on the sphere are comparable by means of the…

Numerical Analysis · Mathematics 2019-07-05 Tuan Anh Nguyen

We study sequences of functions of the form F_p^n -> {0,1} for varying n, and define a notion of convergence based on the induced distributions from restricting the functions to a random affine subspace. Using a decomposition theorem and a…

Combinatorics · Mathematics 2013-08-20 Hamed Hatami , Pooya Hatami , James Hirst

By using the spectrum of the underlying symmetric diffusion operator, the convergence in $L^p$-Wasserstein distance $\mathbb W_p (p\ge 1)$ is characterized for the empirical measure $\mu_t$ of non-symmetric subordinated diffusion processes…

Probability · Mathematics 2023-02-28 Feng-Yu Wang

We propose a practical empirical fitting function to characterize the non-Gaussian displacement distribution functions (DispD) often observed for heterogeneous diffusion problems. We first test this fitting function with the problem of a…

Soft Condensed Matter · Physics 2022-07-20 Le Qiao , Nicholas Ilow , Maxime Ignacio , Gary W. Slater

While the mathematical foundations of score-based generative models are increasingly well understood for unconstrained Euclidean spaces, many practical applications involve data restricted to bounded domains. This paper provides a…

Statistics Theory · Mathematics 2026-03-26 Asbjørn Holk , Claudia Strauch , Lukas Trottner

Given a cloud of $n$ data points in $\mathbb{R}^d$, consider all projections onto $m$-dimensional subspaces of $\mathbb{R}^d$ and, for each such projection, the empirical distribution of the projected points. What does this collection of…

Machine Learning · Statistics 2025-05-06 Andrea Montanari , Kangjie Zhou

The problem of determining a periodic Lipschitz vector field $b=(b_1, \dots, b_d)$ from an observed trajectory of the solution $(X_t: 0 \le t \le T)$ of the multi-dimensional stochastic differential equation \begin{equation*} dX_t =…

Statistics Theory · Mathematics 2020-07-21 Richard Nickl , Kolyan Ray

We study the properties of points in $[0,1]^d$ generated by applying Hilbert's space-filling curve to uniformly distributed points in $[0,1]$. For deterministic sampling we obtain a discrepancy of $O(n^{-1/d})$ for $d\ge2$. For random…

Methodology · Statistics 2014-06-19 Zhijian He , Art B. Owen

Consider $n$ $d$-dimensional vectors with iid entries from a lattice distribution $X$. We show that the probability that all distances between them are equal is asymptotically \[ C_n\cdot\frac{1}{d^{(m-1)/2}} \quad \text{for} \quad d \to…

Probability · Mathematics 2025-02-06 Stefan Gerdjikov , Martin Minchev , Mladen Savov

In this paper, we mainly employed the idea of the previous paper to study the sharp uniform $W^{1,p}$ estimates with $1<p\leq \infty$ for more general elliptic systems with the Neumann boundary condition on a bounded $C^{1,\eta}$ domain,…

Analysis of PDEs · Mathematics 2015-12-14 Qiang Xu

We have introduced a new sequence space $l(r, s, t, p ;\Delta^{(m)})$ combining by using generalized means and difference operator of order $m$. We have shown that the space $l(r, s, t, p ;\Delta^{(m)})$ is complete under some suitable…

Functional Analysis · Mathematics 2016-01-05 Amit Maji , P. D. Srivastava

In this short note we report on a coincidence of two mathematical quantities that, at first glance, have little to do with each other. On the one hand, there are the Lebesgue constants of the Walsh function system that play an important…

Number Theory · Mathematics 2026-02-26 Josef Dick , Friedrich Pillichshammer

High-dimensional depth separation results for neural networks show that certain functions can be efficiently approximated by two-hidden-layer networks but not by one-hidden-layer ones in high-dimensions $d$. Existing results of this type…

Machine Learning · Computer Science 2021-09-23 Luca Venturi , Samy Jelassi , Tristan Ozuch , Joan Bruna

Let $\mathscr{C}_\mathbb{Z}([0,1])$ be the metric space of real-valued continuous functions on $[0,1]$ with integer values at $0$ and $1$, equipped with the uniform (supremum) metric $d_\infty$. It is a classical theorem in approximation…

Number Theory · Mathematics 2023-11-21 C. Sinan Güntürk , Weilin Li

Motivated by the constructions of pseudorandom sequences over the cyclic elliptic function fields by Hu \textit{et al.} in \text{[IEEE Trans. Inf. Theory, 53(7), 2007]} and the constructions of low-correlation, large linear span binary…

Information Theory · Computer Science 2026-02-09 Xiaofeng Liu , Jun Zhang , Fang-Wei Fu

We introduce a two-parameter family of discrepancy measures, termed \emph{$(G,f)$-divergences}, obtained by applying a non-decreasing function $G$ to an $f$-divergence $D_f$. Building on Csisz\'ar's formulation of mutual $f$-information, we…

Information Theory · Computer Science 2026-01-23 Hamidreza Abin , Mahdi Zinati , Amin Gohari , Mohammad Hossein Yassaee , Mohammad Mahdi Mojahedian

Motivated by the Beck-Fiala conjecture, we study the discrepancy problem in two related models of random hypergraphs on $n$ vertices and $m$ edges. In the first (edge-independent) model, a random hypergraph $H_1$ is constructed by fixing a…

Combinatorics · Mathematics 2024-01-12 Calum MacRury , Tomáš Masařík , Leilani Pai , Xavier Pérez-Giménez

Given several sequences of Hermitian holomorphic line bundles $\{(L_{kp}, h_{kp})\}_{p=1}^{\infty}$, we establish the distribution of common zeros of random holomorphic sections of $L_{kp}$ with respect to singular measures. We also study…

Complex Variables · Mathematics 2022-08-09 Manli Liu , Weixiong Mai , Guokuan Shao

Score-matching generative models have proven successful at sampling from complex high-dimensional data distributions. In many applications, this distribution is believed to concentrate on a much lower $d$-dimensional manifold embedded into…

Machine Learning · Statistics 2025-04-25 Peter Potaptchik , Iskander Azangulov , George Deligiannidis
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