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About 10,000 papers for “Pointwise”

Pointwise Control

Boris Vexler, Dominik Meidner · Springer series in computational mathematics · 2025

Cited by 0

Pointwise convolution.

Shaoming Qiu (19959324), Liangyu Liu (403314), Yan Wang (15435) et al. · Figshare · 2024

<div><p>Fault diagnosis of mechanical equipment can effectively reduce property losses and casualties. Bearing vibration signals, as one of the effective sources of diagnostic information, are often overwhelmed by substantial environmental noise. To address this issue, we present a fault diagnosis method, CCSDRSN, which exhibits strong noise resistance. This method enhances the soft threshold function in the traditional deep residual shrinkage network, allowing it to extract useful information from the fault signal to the maximum extent, thus significantly improving diagnostic accuracy. Additionally, we have developed a novel activation function that can nonlinearly transform the time frequency map across multiple dimensions and the entire region. In pursuit of network optimization and parameter reduction, we have strategically incorporated depthwise separable convolutions, effectively replacing conventional convolutional layers. This architectural innovation streamlines the network. By verifying the effectiveness of the proposed method using Case Western Reserve University datasets, the results demonstrate that the proposed method not only possesses strong noise resistance in high noise environments but also achieves high diagnostic accuracy and good generalization performance under different load conditions.</p></div>

Cited by 0Open access

Pointwise entropies.

Kourosh Zarringhalam (113775), Michelle M. Meyer (126321), Ivan Dotu (110595) et al. · Figshare · 2015

<p>Pointwise entropy of yeast asp-tRNA, computed from RNAsc using shape data (red squares), in-line probing (blue diamonds), and using no probing data (black circles). Average pointwise entropies: 0.210 (shape data), 0.267 (in-line probing), 0.269 (no data). As expected, by integrating either shape or in-line probing data into RNAsc, the variability (entropy) decreases; however, it appears that variability (entropy) is decreased more by shape than by in-line probing data – again, suggesting that shape data is more robust than in-line probing data when used with RNAsc.</p>

Cited by 0Open access

Pointwise Tracking

Boris Vexler, Dominik Meidner · Springer series in computational mathematics · 2025

Cited by 0

Pointwise maximal subrings

Rahul Kumar, Atul Gaur · Beiträge zur Algebra und Geometrie / Contributions to Algebra and Geometry · 2020

Cited by 0

Pointwise Compact Spaces

Pedro Morales · Canadian Mathematical Bulletin · 1973

In 1962, J. M. G. Fell [5] indicated the important role played by certain topological spaces which, though locally compact in a specialized sense, do not, in general, satisfy even the weakest separation axiom. He called them "locally compact". These were called "punktal kompakt" by Flachsmeyer [6] and to avoid confusion, we shall call them pointwise compact spaces.

Cited by 1

Pointwise regularity criteria

Stéphane Jaffard · Comptes Rendus Mathématique · 2004

A wavelet characterization of the pointwise regularity condition <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" overflow="scroll"> <mml:msubsup> <mml:mi>T</mml:mi> <mml:mi>u</mml:mi> <mml:mi>p</mml:mi> </mml:msubsup> <mml:mo stretchy="false">(</mml:mo> <mml:msub> <mml:mi>x</mml:mi> <mml:mn>0</mml:mn> </mml:msub> <mml:mo stretchy="false">)</mml:mo> </mml:math> of Calderón and Zygmund is obtained. The extremal case (a pointwise BMO condition) yields the sharpest wavelet condition which is implied by pointwise Hölder regularity; in particular, this criterium is sharper than the usual two-microlocal condition.

Cited by 19

Pointwise hardy inequalities

Hajlasz, Piotr, International Centre for Theoretical Physics, Trieste (Italy) · OpenGrey (Institut de l'Information Scientifique et Technique) · 1996

Cited by 0

Coconvex Pointwise Approximation

Dzyubenko, G.A., Gilewicz, J., Shevchuk, I.A. · The scientific electronic library of periodicals of the National Academy of Sciences of Ukraine (National Academy of Sciences of Ukraine) · 2013

Assume that a function f ∈ C[−1, 1] changes its convexity at a finite collection Y := {y 1, ... y s} of s points yi ∈ (−1, 1). For each n &gt; N(Y), we construct an algebraic polynomial Pn of degree ≤ n that is coconvex with f, i.e., it changes its convexity at the same points yi as f and&#13;\n|f(x)−Pn(x)| ≤ cω₂ (f, (√(1−x²))/n,x∈[−1,1],&#13;\nwhere c is an absolute constant, ω₂(f, t) is the second modulus of smoothness of f, and if s = 1, then N(Y) = 1. We also give some counterexamples showing that this estimate cannot be extended to the case of higher smoothness.

Cited by 1Open access

Pointwise Maximal Leakage

Sara Saeidian, Giulia Cervia, Tobias J. Oechtering et al. · IEEE Transactions on Information Theory · 2023

We introduce a privacy measure called pointwise maximal leakage, generalizing the pre-existing notion of maximal leakage, which quantifies the amount of information leaking about a secret <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> by disclosing a single outcome of a (randomized) function calculated on <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> . Pointwise maximal leakage is a robust and operationally meaningful privacy measure that captures the largest amount of information leaking about <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> to adversaries seeking to guess arbitrary (possibly randomized) functions of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$X$ </tex-math></inline-formula> , or equivalently, aiming to maximize arbitrary gain functions. We study several properties of pointwise maximal leakage, e.g., how it composes over multiple outcomes, how it is affected by pre- and post-processing, etc. Furthermore, we propose to view information leakage as a random variable which, in turn, allows us to regard privacy guarantees as requirements imposed on different statistical properties of the information leakage random variable. We define several privacy guarantees and study how they behave under pre-processing, post-processing and composition. Finally, we examine the relationship between pointwise maximal leakage and other privacy notions such as local differential privacy, local information privacy, <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$f$ </tex-math></inline-formula> -information, and so on. Overall, our paper constructs a robust and flexible framework for privacy risk assessment whose central notion has a strong operational meaning which can be adapted to a variety of applications and practical scenarios.

Cited by 22

POINTWISE SLANT SUBMERSIONS

Jae Won Lee, Bayram Şahin · Bulletin of the Korean Mathematical Society · 2014

The purpose of this paper is to study pointwise slant submersions from almost Hermitian manifolds which extends slant submersion in a natural way. Several basic results in this point of view are proven in this paper.

Cited by 59Open access

Pointwise measurable functions

Hermann Render, Lothar Rogge · MATHEMATICA SCANDINAVICA · 2004

We introduce the new concept of pointwise measurability. It is shown in this paper that a measurable function is measurable at each point and that for a large class of topological spaces the converse also holds. Moreover it can be seen that a function which is continuous at a point is Borel-measurable at this point too. Furthermore the set of measurability points is considered. If the range space is a $\sigma$-compact metric space, then this set is a $G_{\delta}$-set; if the range space is only a Polish space this is in general not true any longer.

Cited by 1Open access