Results 51 to 60 of about 70,207 (165)
Some inequalities for two simplices in the Euclidean space En. [PDF]
Heliyon, 2023 Pan J, Zuo X.
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Modified Sweeping Surfaces in Euclidean 3-Space
AxiomsIn this study, we explore the sweeping surfaces in Euclidean 3-space, utilizing the modified orthogonal frames with non-zero curvature and torsion, which allows us to consider the spine curves even if their second differentiations vanish.
Yanlin Li +3 more
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Geometry of conformal vector fields
Arab Journal of Mathematical Sciences, 2017 It is well known that the Euclidean space (Rn,〈,〉), the n-sphere Sn(c) of constant curvature c and Euclidean complex space form (Cn,J,〈,〉) are examples of spaces admitting conformal vector fields and therefore conformal vector fields are used in ...
Sharief Deshmukh
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Representing logic gates over Euclidean space via heaviside step function. [PDF]
Sci Rep, 2022 Iacovelli G, Iacovelli C.
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Learning behavior aware features across spaces for improved 3D human motion prediction
Scientific Reports3D skeleton-based human motion prediction is an essential and challenging task for human-machine interactions, aiming to forecast future poses given a history of previous motions.
Ruiya Ji +3 more
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El mapa como metáfora o la espacialización del pensamiento
Terra Brasilis, 2015 The expression "map" is used to name non-geographical images: map of the DNA, "site map" of online navigability, "conceptual maps", among many others. Why (or in what sense) are these images maps?
Carla Lois
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Semicomputable Points in Euclidean Spaces.
, 2019 We introduce the notion of a semicomputable point in R^n , defined as a point having left-c.e. projections. We study the range of such a point, which is the set of directions on which its projections are left-c.e., and is a convex cone. We provide a thorough study of these notions, proving along the way new results on the computability of convex sets ...
Hoyrup, Mathieu, Stull, Donald
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Space Complexity of Euclidean Clustering
IEEE Transactions on Information TheoryThe $(k, z)$-Clustering problem in Euclidean space $\mathbb{R}^d$ has been extensively studied. Given the scale of data involved, compression methods for the Euclidean $(k, z)$-Clustering problem, such as data compression and dimension reduction, have received significant attention in the literature.
Xiaoyi Zhu +3 more
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Convexity and the Euclidean Metric of Space-Time
Universe, 2017 We address the reasons why the “Wick-rotated”, positive-definite, space-time metric obeys the Pythagorean theorem. An answer is proposed based on the convexity and smoothness properties of the functional spaces purporting to provide the kinematic ...
Nikolaos Kalogeropoulos
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Jixie qiangdu, 2022 Most of the existing local linear embedding algorithms assume that the original data set is located in Euclidean space, but in reality almost all the original space is non-Euclidean space.
LIU QingQiang +4 more
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