Results 201 to 210 of about 20,179 (255)

Interpretability and Representability of Commutative Algebra, Algebraic Topology, and Topological Spectral Theory for Real‐World Data

Advanced Intelligent Discovery, EarlyView.
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

Engaging Latinx or Hispanic-Identifying Families in Science Education: A Longitudinal Intervention Study. [PDF]

J Educ Psychol
Jiang S, Woodcock A, Haddad K, Hernandez PR.   +3 more
europepmc   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

A constructive method to determine the total vertex irregularity strength of two flower graph variants. [PDF]

MethodsX
Hinding N, Sunusi N, Mutmainnah S, Rahayu II, Sy S, Sugeng KA, Simanjuntak RG.   +6 more
europepmc   +1 more source

Interfacing biology, category theory and mathematical statistics

, 2019
Pastor, Dominique, Beurier, Erwan, Ehresmann, Andrée   +2 more
openaire   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
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

The neurobiology of human sequential signal prediction: Insights from language, music, and mathematics.

Neurosci Res
Nakai T, Daikoku T, Oseki Y.
europepmc   +1 more source

Soliton structures and dynamical characteristics of fractional nonlinear waves in the classical Boussinesq framework. [PDF]

Sci Rep
Rimu NN, Islam MA, Dey P.
europepmc   +1 more source

DeepMapper: Attention‐Based AutoEncoder for System Identification in Wound Healing and Stage Prediction

Advanced Intelligent Discovery, EarlyView.
The authors develop a deep learning model for real‐time tracking of wound progression. The deep learning framework maps the nonlinear evolution of a time series of images to a latent space, where they learn a linear representation of the dynamics. The linear model is interpretable and suitable for applications in feedback control.
Fan Lu, Ksenia Zlobina, Michael Briden, Oscar Avalos, Héctor Carrión, Sebastian Osorio, Hsin‐ya Yang, Alexandra Nava, Michelle D. Bagood, Marco Rolandi, Roslyn Rivkah Isseroff, Marcella Gomez   +11 more
wiley   +1 more source

The role of parent and teacher involvement in shaping academic emotions. [PDF]

BMC Psychol
Avcı S, Avcu A, Akıncı T, Kafadar EN, Kopuz M, Işkin BE.   +5 more
europepmc   +1 more source