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In consideration of the neighborhood structure manifold which exploits the relative relationship between the concerned samples and their neighbors in the feature space, we propose a novel method, "Neighborhood Structure Metric Learning", to learn discriminative dissimilarities on such manifold by adapting the codomain metrics of its charts.
Using the Shannon Information Entropy (IE) as a soil structure metric is proposed to analyze the effect of the particle size distribution (PSD) heterogeneity on soil bulk density values.
Because the variation correlates with a network structure metric, we infer that the heterogeneities in overall infection rate of individuals across all simulations arise from the network structure or related parameters, consistent with the findings of [ 6].
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Table 5 Similarity degree of community structures Metric Sample Degree of similarity FNCA vs. LPA Common Mean Median Std.
Following this approach, the N-connection and the bulk of fundamental geometric structures (metric, canonical linear connection, almost symplectic and almost complex structures...) are derived in general form starting from regular (for simplicity) Lagrangian and/or Hamiltonian.
In 1970, Takahashi [20] defined a convex structure on metric spaces.
If d = 3, the structure tensor metric g i j is symmetric positive and ∧ is the largest eigenvalue of g i j.
Weyl called this additional structure the "metric connection" on a manifold; however, we shall use the term "length connection" instead, in order to avoid confusion with the modern usage of the term "metric connection", which today denotes the symmetric linear connection that is uniquely determined by a Riemannian metric tensor according to (9).
It has been recently studied by Ambrosio, Gigli, Savaré and new collaborators (Kuwada and Ohta) in a series of papers [6, 48, 50], and has been the starting point of recent researches on the differential structure of metric measure spaces.
Finally, let us note a work of Khamsi [27] in which he introduced a metric-type structure in cone metric spaces over a normal Banach space.
So it was interesting to investigate the extension of these fundamental results in nonlinear structures like metric spaces.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com