Results 101 to 110 of about 399,425 (214)

Smarandache Multi-Space Theory(III)--Map geometries and pseudo-plane geometries [PDF]

open access: yesarXiv, 2006
A Smarandache multi-space is a union of $n$ different spaces equipped with some different structures for an integer $n\geq 2$, which can be both used for discrete or connected spaces, particularly for geometries and spacetimes in theoretical physics. This is the third part on multi-spaces concertrating on Smarandache geometries, including those of map ...
arxiv  

NEUF: Learning Point Cloud Quality by Non-Euclidean Fast Filtering

open access: yesIEEE Access
This paper addresses the problem of no reference visual quality assessment in point clouds, useful for extended reality communication service such as remote surgery and education. Accurate, computationally efficient metrics for point cloud visual quality
Eleonora Di Salvo   +5 more
doaj   +1 more source

Stringy Geometry and Topology of Orbifolds [PDF]

open access: yesarXiv, 2000
This is a survey article on the recent development of "stringy geometry and topology of orbifolds", a new subject of mathematics motivated by orbifold string theory.
arxiv  

The notion of dimension in geometry and algebra [PDF]

open access: yesarXiv, 2005
This talk reviews some mathematical and physical ideas related to the notion of dimension. After a brief historical introduction, various modern constructions from fractal geometry, noncommutative geometry, and theoretical physics are invoked and compared.
arxiv  

Non-Euclidean Pythagorean triples, a problem of Euler, and rational points on K3 surfaces

open access: yes, 2006
We discover suprising connections between three seemingly different problems: finding right triangles with rational sides in a non-Euclidean geometry, finding three integers such that the difference of the squares of any two is a square, and the problem ...
Robin Hartshorne, Ronald, Van Luijk
core   +1 more source

Learning neural operators on Riemannian manifolds

open access: yesNational Science Open
Learning mappings between functions (operators) defined on complex computational domains is a common theoretical challenge in machine learning. Existing operator learning methods mainly focus on regular computational domains, and have many components ...
Chen Gengxiang   +5 more
doaj   +1 more source

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