Results 91 to 100 of about 1,007,951 (256)
PySymmPol - Symmetric Polynomials
<p>A Python package designed for efficient manipulation of symmetric polynomials. It provides functionalities for working with various types of symmetric polynomials, including elementary, homogeneous, monomial symmetric, (skew-) Schur, and Hall ...
Rocha Araujo, Thiago
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A machine learning method, opt‐GPRNN, is presented that combines the advantages of neural networks and kernel regressions. It is based on additive GPR in optimized redundant coordinates and allows building a representation of the target with a small number of terms while avoiding overfitting when the number of terms is larger than optimal.
Sergei Manzhos, Manabu Ihara
wiley +1 more source
Elementary symmetric polynomials in Stanley--Reisner face ring
19 pages, 3 ...
Lü, Zhi, Ma, Jun, Sun, Yi
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This article investigates how persistent homology, persistent Laplacians, and persistent commutative algebra reveal complementary geometric, topological, and algebraic invariants or signatures of real‐world data. By analyzing shapes, synthetic complexes, fullerenes, and biomolecules, the article shows how these mathematical frameworks enhance ...
Yiming Ren, Guo‐Wei Wei
wiley +1 more source
Generating functions and short recursions, with applications to the moments of quadratic forms in noncentral normal vectors [PDF]
Using generating functions, the top-order zonal polynomials that occur in much distribution theory under normality can be recursively related to other symmetric functions (power-sum and elementary symmetric functions, Ruben, Hillier, Kan, and Wang ...
Grant Hillier, Xiaolu Wang, Raymond Kan
core
Hall polynomials for the representation-finite hereditary algebras [PDF]
Ringel CM. Hall polynomials for the representation-finite hereditary algebras. Advances in mathematics.
Ringel, Claus Michael
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Harnessing Machine Learning to Understand and Design Disordered Solids
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source
In Situ Contact Angle Measurement for Autonomous Spin Coating in Self‐Driving Labs
A vision‐based add‐on transforms commercial spin coaters into autonomous modules of Self‐Driving Labs. Combining a width‐scaled U‐Net with classical geometric analysis, the system simultaneously measures contact angles and estimates substrate pose using a single camera.
Sven Fischer, Micha Hiegle, Holger Röhm
wiley +1 more source
ELEMENTARY SYMMETRIC POLYNOMIALS AND A POTENTIALLY INJECTIVE FAMILY OF MAPS ON PARTITIONS
Abstract Ballantine et al. [‘Partitions and elementary symmetric polynomials: an experimental approach’, Ramanujan J. 66 (2) (2025), Article no. 34] proposed two conjectures on the injectivity of a class of maps
AMAN DEVNANI, PRAMOD EYYUNNI
openaire +2 more sources

