CMS Inner Tracker Detector Modules

The production of silicon detector modules that will instrument the CMS Inner Tracker has nowadays reached 1300 units out of the approximately 3700 needed in total, with an overall yield close to 96%. A description of the module design, the assembly procedures and the qualification tests is given. The results of the quality assurance are presented and the experience gained is discussed.Comment: 5 pages, 3 figures, talk presented at RESMDD04, 5th International Conference on Radiation Effects on Semiconductor Materials Detectors and Devices, October 2004, Florence, Ital

Mean-Field Stochastic Control with Elephant Memory in Finite and Infinite Time Horizon

Our purpose of this paper is to study stochastic control problem for systems driven by mean-field stochastic differential equations with elephant memory, in the sense that the system (like the elephants) never forgets its history. We study both the finite horizon case and the infinite time horizon case. - In the finite horizon case, results about existence and uniqueness of solutions of such a system are given. Moreover, we prove sufficient as well as necessary stochastic maximum principles for the optimal control of such systems. We apply our results to solve a mean-field linear quadratic control problem. - For infinite horizon, we derive sufficient and necessary maximum principles. As an illustration, we solve an optimal consumption problem from a cash flow modelled by an elephant memory mean-field system

Infinite horizon optimal control of forward-backward stochastic differential equations with delay

We consider a problem of optimal control of an infinite horizon system governed by forward-backward stochastic differential equations with delay. Sufficient and necessary maximum principles for optimal control under partial information in infinite horizon are derived. We illustrate our results by an application to a problem of optimal consumption with respect to recursive utility from a cash flow with delay

Malliavin calculus and optimal control of stochastic Volterra equations

Solutions of stochastic Volterra (integral) equations are not Markov processes, and therefore classical methods, like dynamic programming, cannot be used to study optimal control problems for such equations. However, we show that by using {\em Malliavin calculus} it is possible to formulate a modified functional type of {\em maximum principle} suitable for such systems. This principle also applies to situations where the controller has only partial information available to base her decisions upon. We present both a sufficient and a necessary maximum principle of this type, and then we use the results to study some specific examples. In particular, we solve an optimal portfolio problem in a financial market model with memory.Comment: 18 page