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Related papers: Analyticity and supershift with irregular sampling

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The notion of supershift (in itself a generalization of the notion of superoscillation arising in quantum mechanics) expresses the fact that the sampling of a function in an interval allows to compute the values of the function far from the…

Complex Variables · Mathematics 2023-11-07 F. Colombo , I. Sabadini , D. C. Struppa , A. Yger

Superoscillations have roots in various scientific disciplines, including optics, signal processing, radar theory, and quantum mechanics. This intriguing mathematical phenomenon permits specific functions to oscillate at a rate surpassing…

Complex Variables · Mathematics 2024-03-12 F. Colombo , I. Sabadini , D. C. Struppa , A. Yger

Sampling theory in spaces other than the space of band-limited functions has recently received considerable attention. This is in part because the band-limitedness assumption is not very realistic in many applications. In addition,…

Classical Analysis and ODEs · Mathematics 2007-05-23 Cristina Blanco , Carlos Cabrelli , Sigrid Heineken

The classical sampling theorem for bandlimited functions has recently been generalized to apply to so-called bandlimited operators, that is, to operators with band-limited Kohn-Nirenberg symbols. Here, we discuss operator sampling versions…

Functional Analysis · Mathematics 2009-03-06 Yoon Mi Hong , Goetz E. Pfander

A natural connection between rational functions of several real or complex variables, and subspace collections is explored. A new class of function, superfunctions, are introduced which are the counterpart to functions at the level of…

Algebraic Geometry · Mathematics 2016-02-23 Graeme W. Milton

Recently there has been much work on selective sampling, an online active learning setting, in which algorithms work in rounds. On each round an algorithm receives an input and makes a prediction. Then, it can decide whether to query a…

Machine Learning · Computer Science 2014-02-18 Edward Moroshko , Koby Crammer

In this article we recover the distribution function (and possible density) of an arbitrary random variable that is subject to an additive measurement error. This problem is also known as deconvolution and has a long tradition in…

Statistics Theory · Mathematics 2025-10-07 Henrik Kaiser

We consider certain finite sets of circle-valued functions defined on intervals of real numbers and estimate how large the intervals must be for the values of these functions to be uniformly distributed in an approximate way. This is used…

Functional Analysis · Mathematics 2018-12-27 Stefano Ferri , Jorge Galindo , Camilo Gómez

Arithmetic operations can be defined in various ways, even if one assumes commutativity and associativity of addition and multiplication, and distributivity of multiplication with respect to addition. In consequence, whenever one encounters…

Mathematical Physics · Physics 2016-05-19 Marek Czachor

When modelling spacetime and classical physical fields, one typically assumes smoothness (infinite differentiability). But this assumption and its philosophical implications have not been sufficiently scrutinized. For example, we can appeal…

History and Philosophy of Physics · Physics 2026-02-20 Lu Chen , Tobias Fritz

We prove that certain quotients of entire functions are characteristic functions. Under some conditions, the probability measure corresponding to a characteristic function of that type has a density which can be expressed as a generalized…

Probability · Mathematics 2010-09-09 Albert Ferreiro-Castilla , Frederic Utzet

Considered are operators that leave the set of non-invertible (in the sense of Ehrenpreis) distributions stable. They simultaneously generalise the operation of convolution by a distribution with compact support and the operation of…

Functional Analysis · Mathematics 2013-12-18 Richard F. Bonner

We consider the variance of sums of arithmetic functions over random short intervals in the function field setting. Based on the analogy between factorizations of random elements of $\mathbb{F}_q[T]$ into primes and the factorizations of…

Number Theory · Mathematics 2018-08-08 Brad Rodgers

A compression function is a map that slims down an observational set into a subset of reduced size, while preserving its informational content. In multiple applications, the condition that one new observation makes the compressed set change…

Machine Learning · Computer Science 2024-01-09 Marco C. Campi , Simone Garatti

We prove a.s. (almost sure) unisolvency of interpolation by continuous random sampling with respect to any given density, in spaces of multivariate a.e. (almost everywhere) analytic functions. Examples are given concerning polynomial and…

Numerical Analysis · Mathematics 2023-03-27 Francesco Dell'Accio , Alvise Sommariva , Marco Vianello

The paper considers the properties of pseudo stationarity in a broad sense and pseudo strong mixing for sequences of random variables corresponding to arithmetic functions. Assertions on this topic have been proven. The implementation of…

Number Theory · Mathematics 2019-06-19 Victor Volfson

Average-case analysis computes the complexity of an algorithm averaged over all possible inputs. Compared to worst-case analysis, it is more representative of the typical behavior of an algorithm, but remains largely unexplored in…

Optimization and Control · Mathematics 2021-10-05 Courtney Paquette , Bart van Merriënboer , Elliot Paquette , Fabian Pedregosa

We establish a regular sampling theory in the range of the analysis operator of a continuous frame having a unitary structure. The unitary structure is related with a unitary representation of a locally compact abelian group on a separable…

Functional Analysis · Mathematics 2020-11-11 Antonio G. García

Resampling is an operation costly in calculation time and accuracy. It regularizes irregular sampling, replacing N data by N periodic estimations. This stage can be suppressed, using formulas built with incoming data and completed by…

Data Analysis, Statistics and Probability · Physics 2019-05-28 Bernard Lacaze

The shapelet transform is a form of feature extraction for time series, in which a time series is described by its similarity to each of a collection of `shapelets'. However it has previously suffered from a number of limitations, such as…

Machine Learning · Computer Science 2020-06-01 Patrick Kidger , James Morrill , Terry Lyons
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