A case report of a huge congenital granular cell epulis operated under local anesthesia

Congenital granular cell epulis (CGCE) is a very rare benign soft tissue lesion of the neonate, it most frequently located on the anterior maxillary alveolar ridge. It has a female predilection. It is a tumor with no tendency to recur after excision. The exact histogenesis of this tumor remains unresolved and it may be hamartomata.Key words: Congenital granular cell tumor, gingival tumor, newborn, local anesthesi

A rare neuronal tumor of the cerebellum with myoid features

We report an extremely rare tumor presenting with myoid features in the left cerebellar hemisphere in a 62-year-old man. This tumor consisted of medium to large round cells with focal lipomatous and myoid differentiation. Immunohistochemically, the tumor cells expressed synaptophysin, GFAP (glial fibrillary acidic protein) and focally desmin. From these findings, we concluded that this tumor was a liponeurocytoma with myoid features. To the best of our knowledge, this report constitutes the second described case of liponeurocytoma with myoid differentiation in the cerebellum

Convergence of iterates for first-order optimization algorithms with inertia and Hessian driven damping

International audienceIn a Hilbert space setting, for convex optimization, we show the convergence of the iterates to optimal solutions for a class of accelerated first-order algorithms. They can be interpreted as discrete temporal versions of an inertial dynamic involving both viscous damping and Hessian-driven damping. The asymptotically vanishing viscous damping is linked to the accelerated gradient method of Nesterov while the Hessian driven damping makes it possible to significantly attenuate the oscillations. By treating the Hessian-driven damping as the time derivative of the gradient term, this gives, in discretized form, first-order algorithms. These results complement the previous work of the authors where it was shown the fast convergence of the values, and the fast convergence towards zero of the gradients. KEYWORDS Convergence of iterates; Hessian driven damping; inertial optimization algorithms; Nesterov accelerated gradient method; time rescaling

Fast convergence of dynamical ADMM via time scaling of damped inertial dynamics

International audienceIn this paper, we propose in a Hilbertian setting a second-order timecontinuous dynamic system with fast convergence guarantees to solve structured convex minimization problems with an affine constraint. The system is associated with the augmented Lagrangian formulation of the minimization problem. The corresponding dynamics brings into play three general time-varying parameters, each with specific properties, and which are respectively associated with viscous damping, extrapolation and temporal scaling. By appropriately adjusting these parameters, we develop a Lyapunov analysis which provides fast convergence properties of the values and of the feasibility gap. These results will naturally pave the way for developing corresponding accelerated ADMM algorithms, obtained by temporal discretization