Zobrazeno 1 - 10
of 116
pro vyhledávání: '"Leonardi, Gian"'
Let $E \subset \Omega$ be a local almost-minimizer of the relative perimeter in the open set $\Omega \subset \mathbb{R}^{n}$. We prove a free-boundary monotonicity inequality for $E$ at a point $x\in \partial\Omega$, under a geometric property called
Externí odkaz:
http://arxiv.org/abs/2407.05039
We analyze some properties of the measures in the dual of the space $BV$, by considering (signed) Radon measures satisfying a perimeter bound condition, which means that the absolute value of the measure of a set is controlled by the perimeter of the
Externí odkaz:
http://arxiv.org/abs/2407.06224
Given a convex domain $\Omega\subset \mathbb{R}^{3}$ and an almost-minimizer $E$ of the relative perimeter in $\Omega$, we prove that the closure of $\partial E \cap \Omega$ does not contain vertices of $\Omega$.
Externí odkaz:
http://arxiv.org/abs/2401.14725
We introduce a weak formulation of the non-parametric prescribed mean curvature equation with measure data and show the existence and several properties of $BV$ solutions under natural assumptions on the prescribed measure. Our approach does not rely
Externí odkaz:
http://arxiv.org/abs/2302.10592
We consider a system of $N$ hard spheres sitting on the nodes of either the $\mathrm{FCC}$ or $\mathrm{HCP}$ lattice and interacting via a sticky-disk potential. As $N$ tends to infinity (continuum limit), assuming the interaction energy does not exc
Externí odkaz:
http://arxiv.org/abs/2204.12892
Publikováno v:
In Nonlinear Analysis February 2025 251
Training quantised neural networks (QNNs) is a non-differentiable optimisation problem since weights and features are output by piecewise constant functions. The standard solution is to apply the straight-through estimator (STE), using different func
Externí odkaz:
http://arxiv.org/abs/2203.11323
Autor:
Leonardi, Gian Paolo, Saracco, Giorgio
Publikováno v:
Calc. Var. Partial Differential Equations 61(2):56, 2022
We give a complete characterization of all isoperimetric sets contained in a domain of the Euclidean plane, that is bounded by a Jordan curve and satisfies a no-neck property. Further, we prove that the isoperimetric profile of such domain is convex
Externí odkaz:
http://arxiv.org/abs/2108.10762
Research in computational deep learning has directed considerable efforts towards hardware-oriented optimisations for deep neural networks, via the simplification of the activation functions, or the quantization of both activations and weights. The r
Externí odkaz:
http://arxiv.org/abs/2011.01858