Stanley Osher

TL;DR: A graph Fractional-Order Total Variation based method, which provides the freedom to choose the smoothness order by imposing sparsity of the spatial fractional derivatives so that it locates source peaks accurately and demonstrates the superior performance of gFOTV not only in spatial resolution but also in localization accuracy and total reconstruction accuracy.

TL;DR: In this article, an improved level set method is presented for vapor bubble kinematics prediction for the switch from terrestrial gravity (1 g ) to micro-gravity (1.5 g ).

TL;DR: A novel framework to model and solve mean-field game systems with nonlocal interactions that yields a computationally efficient saddle-point reformulation of the original problem that is amenable to state-of-the-art convex optimization methods such as the primal-dual hybrid gradient method (PDHG).

TL;DR: In this article , the inverse problem in mean-field games (MFGs) is considered and a fast and robust operator splitting algorithm is developed to solve the optimization using techniques including harmonic extensions, three-operator splitting scheme, and primal-dual hybrid gradient method.

TL;DR: It is proved that for sampling from both log-concave and non-log- Concave densities, LS-SGLD achieves strictly smaller discretization error in $2$-Wasserstein distance, although its mixing rate can be slightly slower.