Optimized bio-signal reconstruction and watermarking via enhanced fractional orthogonal moments. [PDF]
Sci RepHassan G, Hosny KM, Fathi IS.
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Classical multiple orthogonal polynomials for arbitrary number of weights and their explicit representation [PDF]
Amílcar Branquinho +3 more
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STRUCTURE RELATIONS OF CLASSICAL MULTIPLE ORTHOGONAL POLYNOMIALS BY A GENERATING FUNCTION [PDF]
, 2013 Dong Won Lee
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Statistical Complexity of Quantum Learning
Advanced Quantum Technologies, Volume 8, Issue 12, December 2025.The statistical performance of quantum learning is investigated as a function of the number of training data N$N$, and of the number of copies available for each quantum state in the training and testing data sets, respectively S$S$ and V$V$. Indeed, the biggest difference in quantum learning comes from the destructive nature of quantum measurements ...
Leonardo Banchi +3 more
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A Sparse Hierarchical <i>hp</i>-Finite Element Method on Disks and Annuli. [PDF]
J Sci ComputPapadopoulos IPA, Olver S.
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A method of composition orthogonality and new sequences of orthogonal\n polynomials and functions for non-classical weights [PDF]
, 2020 Semyon Yakubovich
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Fast and Slow Mixing of the Kawasaki Dynamics on Bounded‐Degree Graphs
Random Structures &Algorithms, Volume 67, Issue 4, December 2025.ABSTRACT We study the worst‐case mixing time of the global Kawasaki dynamics for the fixed‐magnetization Ising model on the class of graphs of maximum degree Δ$$ \Delta $$. Proving a conjecture of Carlson, Davies, Kolla, and Perkins, we show that below the tree‐uniqueness threshold, the Kawasaki dynamics mix rapidly for all magnetizations. Disproving a
Aiya Kuchukova +3 more
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Modeling and analysis of fascioliasis disease with Katugampola fractional derivative: a memory-incorporated epidemiological approach. [PDF]
Sci RepPandey RK, Nisar KS.
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Algebro-geometric aspectsof the Christoffel-Darboux kernelsfor classical orthogonal polynomials [PDF]
, 2019 Masanori Sawa, Yukihiro Uchida
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Robust and Differentially Private Principal Component Analysis
Statistical Analysis and Data Mining: An ASA Data Science Journal, Volume 18, Issue 6, December 2025.ABSTRACT Recent advances have sparked significant interest in the development of privacy‐preserving Principal Component Analysis (PCA). However, many existing approaches rely on restrictive assumptions, such as assuming sub‐Gaussian data or being vulnerable to data contamination.
Minwoo Kim, Sungkyu Jung
wiley +1 more source