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Seok-Young Chung

Publications and source records attributed to Seok-Young Chung.

11 recordsLinked to original sources
arXiv2026

Shift-invariant spaces on finite undirected graphs

Shift-invariant spaces (SISs) on the real line provide a natural framework for representing, analyzing and processing signals with inherent shift-invariant structure. In this paper, we extend this framework to the finite undirected graph setting by introducing the concept of graph shift-invariant spaces (GSISs). We examine several properties of GSISs, including their characterization via range functions and fiber functions in the Fourier domain, their connections to shift-invariant filters and polynomial filters, the frame and Riesz basis structures of finitely generated GSISs, and their intricate relationships with bandlimited spaces, finitely generated GSISs, and graph reproducing kernel Hilbert spaces with shift-invariant reproducing kernels (SIGRKHSs). Our analysis reveals several distinctions between SISs on the line and GSISs, such as the shift-invariance of the frame operator, the existence of shift-invariant dual frames, the emergence of fractional shift-invariance, and the interrelationships among GSISs, finitely generated GSISs, SIGRKHSs and bandlimited spaces. In this paper, we also introduce a spectral decomposition of the identity associated with graph shifts and propose a novel definition of the graph Fourier transform (GFT) of spectral type, together with explicit formulations for the GFTs on complete graphs and circulant graphs. In addition, we establish a clear connection between polynomial filters and shift-invariant filters, and we derive a graph uncertainty principle governing the essential supports of a nonzero graph signal and its GFT.

math.FA↗
arXiv2024

Shift-invariant spaces, bandlimited spaces and reproducing kernel spaces with shift-invariant kernels on undirected finite graphs

In this paper, we introduce the concept of graph shift-invariant space (GSIS) on an undirected finite graph, which is the linear space of graph signals being invariant under graph shifts, and we study its bandlimiting, kernel reproducing and sampling properties. Graph bandlimited spaces have been widely applied where large datasets on networks need to be handled efficiently. In this paper, we show that every GSIS is a bandlimited space, and every bandlimited space is a principal GSIS. Functions in a reproducing kernel Hilbert space with shift-invariant kernel could be learnt with significantly low computational cost. In this paper, we demonstrate that every GSIS is a reproducing kernel Hilbert space with a shift-invariant kernel. Based on the nested Krylov structure of GSISs in the spatial domain, we propose a novel sampling and reconstruction algorithm with finite steps, with its performance tested for well-localized signals on circulant graphs and flight delay dataset of the 50 busiest airports in the USA.

eess.SP↗
arXiv2024

Partial fraction expansions and zeros of Hankel transforms

It is proved by the method of partial fraction expansions and Sturm's oscillation theory that the zeros of certain Hankel transforms are all real and distributed regularly between consecutive zeros of Bessel functions. As an application, the sufficient or necessary conditions on parameters for which ${}_1F_2$ hypergeometric functions belong to the Laguerre-Pólya class are investigated in a constructive manner.

math.CA↗
arXiv2024

Uniform bounds, zero separation and monotonicity for the regular Coulomb wave functions

This paper begins by deriving the uniform bounds for the regular Coulomb wave function $F_{\ell,η}$ and its derivative $F_{\ell,η}'$. We then examine detailed zero configurations of $F_{\ell,η}$ and $F_{\ell+1,η}$, extending insights into the earlier work that was restricted to $\ell>-3/2$. Our investigation also includes an analysis of the monotonicity of the zeros of $F_{\ell,η}$ with respect to parameters $\ell$ and $η$, respectively. Furthermore, we expand our exploration to associated orthogonal polynomials, as well as the functions involving both $F_{\ell,η}$ and $F_{\ell,η}'$. Finally, we explore the breakdown of the Sturm separation theorem by means of the zeros of associated orthogonal polynomials.

math.CA↗
arXiv2024

Complex zeros of Bessel function derivatives and associated orthogonal polynomials

We introduce a sequence of orthogonal polynomials whose associated moments are the Rayleigh-type sums, involving the zeros of the Bessel derivative $J_ν'$ of order $ν$. We also discuss the fundamental properties of those polynomials such as recurrence, orthogonality, etc. Consequently, we obtain a formula for the Hankel determinant, elements of which are chosen as the aforementioned Rayleigh-type sums. As an application, we complete the Hurwitz-type theorem for $J_ν'$, which deals with the number of complex zeros of $J_ν'$ depending on the range of $ν$.

math.CA↗
arXiv2023

Positivity of oscillatory integrals and Hankel transforms

In consideration of the integral transform whose kernel arises as an oscillatory solution of certain second-order linear differential equation, its positivity is investigated on the basis of Sturm's theory. As applications, positivity criteria are obtained for Hankel transforms as well as trigonometric integrals defined on the positive real line.

math.CA↗
arXiv2023

Barron Space for Graph Convolution Neural Networks

Graph convolutional neural network (GCNN) operates on graph domain and it has achieved a superior performance to accomplish a wide range of tasks. In this paper, we introduce a Barron space of functions on a compact domain of graph signals. We prove that the proposed Barron space is a reproducing kernel Banach space, it can be decomposed into the union of a family of reproducing kernel Hilbert spaces with neuron kernels, and it could be dense in the space of continuous functions on the domain. Approximation property is one of the main principles to design neural networks. In this paper, we show that outputs of GCNNs are contained in the Barron space and functions in the Barron space can be well approximated by outputs of some GCNNs in the integrated square and uniform measurements. We also estimate the Rademacher complexity of functions with bounded Barron norm and conclude that functions in the Barron space could be learnt from their random samples efficiently.

stat.ML↗
arXiv2023

On the generalized interlacing property for the zeros of Bessel functions

This paper investigates a generalized interlacing property between Bessel functions, particularly $J_ν$ and $J_μ$, where the difference $|ν-μ|$ exceeds $2$. This interlacing phenomenon is marked by a compensatory interaction with the zeros of Lommel polynomials, extending our understanding beyond the traditional $|ν-μ| \le 2$ regime. The paper extends the generalized interlacing property to cylinder functions and derivative of Bessel functions, as an application. It is also discussed that Siegel's extension of Bourget hypothesis to rational numbers of $ν$ cannot be further improved to arbitrary real numbers.

math.CA↗
arXiv2021

The Newton Polyhedron and positivity of ${}_2F_3$ hypergeometric functions

As for the ${}_2F_3$ hypergeometric function of the form \begin{equation*} {}_2F_3\left[\begin{array}{c} a_1, a_2\\ b_1, b_2, b_3\end{array}\biggr| -x^2\right]\qquad(x>0), \end{equation*} where all of parameters are assumed to be positive, we give sufficient conditions on $(b_1, b_2, b_3)$ for its positivity in terms of Newton polyhedra with vertices consisting of permutations of $\,(a_2, a_1+1/2, 2a_1)\,$ or $\,(a_1, a_2+1/2, 2a_2).$ As an application, we obtain an extensive validity region of $(α, λ, μ)$ for the inequality \begin{equation*} \int_0^x (x-t)^λ\, t^μ J_α(t)\, dt \ge 0\qquad(x>0). \end{equation*}

math.CA↗
arXiv2018

Rational extension of Newton diagram for the positivity of ${}_1F_2$ hypergeometric functions and Askey-Szegö problem

We present a rational extension of Newton diagram for the positivity of ${}_1F_2$ generalized hypergeometric functions. As an application, we give upper and lower bounds for the transcendental roots $β(α)$ of \begin{align*} \int_0^{j_{α, 2}} t^{-β} J_α(t) dt = 0\qquad(-1<α\le 1/2), \end{align*} where $j_{α, 2}$ denotes the second positive zero of Bessel function $J_α$.

math.CA↗