Results 171 to 180 of about 4,865,824 (373)
Abstract Introduction Restoration outcomes in cold desert ecosystems like sagebrush steppe are affected by weather variability, particularly during the spring, a critical time period for seedling establishment. Seedling emergence phenology is also highly variable among species in these ecosystems.
Stella M. Copeland +3 more
wiley +1 more source
Geodesic mapping onto Kählerian spaces of the first kind [PDF]
Milan Lj. Zlatanović +2 more
openalex +1 more source
Geodesics in Goedel-Synge spaces
Our purpose is to study the geodesic lines in the form: ds2=dx2 + 2h(x4)dx1dx2 + g(x4)dx22 – dx23 – dx24, and to compare with the work of S. Chandrasekhar and J. P. Wright on geodesics in Gödel’s universe. We will give, first, an isometric embedding of the form ds2=dx2 + 2h(x4)dx1dx2 + g(x4)dx22 – dx23 – dx24 in a pseudo Euclidean space of 10 ...
openaire +2 more sources
Semiparametric regression for circular response with application in ecology
ABSTRACT A regression model for a circular response variable depending on a linear or a circular predictor is presented in this paper. The conditional density belongs to a parametric flexible family that allows for asymmetry and varying peakedness around the modal direction.
Jose Ameijeiras‐Alonso, Irène Gijbels
wiley +1 more source
A Cortical-Inspired Contour Completion Model Based on Contour Orientation and Thickness
An extended four-dimensional version of the traditional Petitot–Citti–Sarti model on contour completion in the visual cortex is examined. The neural configuration space is considered as the group of similarity transformations, denoted as M=SIM(2).
Ivan Galyaev, Alexey Mashtakov
doaj +1 more source
Surfaces of a Euclidean space with Helical or planar geodesics through a point [PDF]
Young Ho Kim
openalex +1 more source
Feature-Based Interpolation and Geodesics in the Latent Spaces of Generative Models [PDF]
Łukasz Struski +4 more
openalex +1 more source
Spatial depth for data in metric spaces
Abstract We propose a novel measure of statistical depth, the metric spatial depth, for data residing in an arbitrary metric space. The measure assigns high (low) values for points located near (far away from) the bulk of the data distribution, allowing quantifying their centrality/outlyingness.
Joni Virta
wiley +1 more source
Functional inequalities and Hamilton-Jacobi Equations in Geodesic Spaces [PDF]
Zoltán M. Balogh +3 more
openalex +1 more source
Geodesic PCA in the Wasserstein space by Convex PCA
Jérémie Bigot +3 more
semanticscholar +1 more source

