Results 61 to 70 of about 125,665 (304)

On some properties of the Łojasiewicz exponent

Comptes Rendus. Mathématique, 2021
In this note, we investigate the behaviour of the Łojasiewicz exponent under hyperplane sections and its relation to the order of tangency.
Eyral, Christophe, Mostowski, Tadeusz, Pragacz, Piotr   +2 more
doaj   +1 more source

Data‐Guided Photocatalysis: Supervised Machine Learning in Water Splitting and CO2 Conversion

Advanced Intelligent Discovery, EarlyView.
This review highlights recent advances in supervised machine learning (ML) for photocatalysis, emphasizing methods to optimize photocatalyst properties and design materials for solar‐driven water splitting and CO2 reduction. Key applications, challenges, and future directions are discussed, offering a practical framework for integrating ML into the ...
Paul Rossener Regonia, Christian Mark Pelicano   +1 more
wiley   +1 more source

Structure in space of hyperplane element with special metrics

Lietuvos Matematikos Rinkinys, 2010
The present work analyses intrinsic antiquaternionic structures of metric hyperplane elements. Spaces of metric hyperplane elements is Cartan space generalisation.
Edmundas Mazėtis
doaj   +1 more source

Crooked Maps in Finite Fields [PDF]

Discrete Mathematics & Theoretical Computer Science, 2005
We consider the maps $f:\mathbb{F}_{2^n} →\mathbb{F}_{2^n}$ with the property that the set $\{ f(x+a)+ f(x): x ∈F_{2^n}\}$ is a hyperplane or a complement of hyperplane for every $a ∈\mathbb{F}_{2^n}^*$.
Gohar Kyureghyan
doaj   +1 more source

Hyperplane sections of non-standard graded rings [PDF]

, 2023
Kazuma Shimomoto
openalex   +1 more source

Deformations of quantum hyperplanes [PDF]

Letters in Mathematical Physics, 1996
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
openaire   +2 more sources

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

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

Hyperplane equipartitions plus constraints [PDF]

Journal of Combinatorial Theory, Series A, 2019
While equivariant methods have seen many fruitful applications in geometric combinatorics, their inability to answer the now settled Topological Tverberg Conjecture has made apparent the need to move beyond the use of Borsuk--Ulam type theorems alone.
openaire   +3 more sources

Gauge Distances and Median Hyperplanes [PDF]

Journal of Optimization Theory and Applications, 2001
A median hyperplane in d-dimensional space minimizes the weighted sum of the distances from a finite set of points to it. When the distances from these points are measured by possibly different gauges, we prove the existence of a median hyperplane passing through at least one of the points. When all the gauges are equal, some median
Plastria, Frank, Carrizosa, E.
openaire   +5 more sources