Presenting a few dots moving coherently on a screen can yield to the perception of human motion. This perception is based on a specific network that is segregated from the traditional motion perception network and that includes the superior temporal sulcus (STS). In this study, we investigate whether this biological motion perception network could influence the smooth pursuit response evoked by...

In recent years, some generalized structures of topology were introduced. Supra was one the most important those generalizations. To contribute in this orientation, we devoted work to studying limit points and separation axioms on supra topological spaces by using $b$-open sets. We define them a similar way their counterparts spaces. general, demonstrate main properties investigate sufficient c...

This note presents a preliminary study of the supra-convergence of well-balanced (finite volume) schemes for conservation laws with a (nonlinear) geometrical source term. In particular, we consider scalar (linear) advection equations in one dimension, for which smooth analytical solutions are available, and upwind interfacial discretizations for the numerical simulation on nonuniform grids (als...

In recent work nonlinear subdivision schemes which operate on manifold-valued data have been successfully analyzed with the aid of so-called proximity conditions bounding the difference between a linear scheme and the nonlinear one. The main difficulty with this method is the verification of these conditions. In the present paper we obtain a very clear understanding of which properties a nonlin...

The concept of soft ideal was first introduced by Kandil et al. [13]. In 1999, Molodtsov [22] introduced the concept of soft sets as a general mathematical tool for dealing with uncertain objects. The concept of generalized closed soft sets in soft topological spaces was introduced by Kannan [15] in 2012. The notions of supra soft topological space were first introduced by Kandil et al. [14]. I...

This paper introduces the generalized forward-backward splitting algorithm for minimizing convex functions of the form F + ∑i=1Gi, where F has a Lipschitzcontinuous gradient and the Gi’s are simple in the sense that their Moreau proximity operators are easy to compute. While the forward-backward algorithm cannot deal with more than n = 1 non-smooth function, our method generalizes it to the cas...