From Fine- to Coarse-Grained Dynamic Information Flow Control and Back, a Tutorial on Dynamic Information Flow

This tutorial provides a complete and homogeneous account of the latestadvances in fine- and coarse-grained dynamic information-flow control (IFC)security. Since the 70s, the programming language and the operating systemcommunities have proposed different IFC approaches. IFC operating systems trackinformation flows in a coarse-grained fashion, at the granularity of a process.In contrast, traditional language-based approaches to IFC are fine-grained:they track information flows at the granularity of program variables. Fordecades, researchers believed coarse-grained IFC to be strictly less permissivethan fine-grained IFC -- coarse-grained IFC systems seem inherently lessprecise because they track less information -- and so granularity appeared tobe a fundamental feature of IFC systems. We show that the granularity of thetracking system does not fundamentally restrict how precise or permissivedynamic IFC systems can be. To this end, we mechanize two mostly standardlanguages, one with a fine-grained dynamic IFC system and the other with acoarse-grained dynamic IFC system, and prove a semantics-preserving translationfrom each language to the other. In addition, we derive the standard securityproperty of non-interference of each language from that of the other via ourverified translation. These translations stand to have important implicationson the usability of IFC approaches. The coarse- to fine-grained direction canbe used to remove the label annotation burden that fine-grained systems imposeon developers, while the fine- to coarse-grained translation shows thatcoarse-grained systems -- which are easier to design and implement -- can trackinformation as precisely as fine-grained systems and provides an algorithm forautomatically retrofitting legacy applications to run on existingcoarse-grained systems.<br

Neural Architectures for Control

The cerebellar model articulated controller (CMAC) neural architectures are shown to be viable for the purposes of real-time learning and control. Software tools for the exploration of CMAC performance are developed for three hardware platforms, the MacIntosh, the IBM PC, and the SUN workstation. All algorithm development was done using the C programming language. These software tools were then used to implement an adaptive critic neuro-control design that learns in real-time how to back up a trailer truck. The truck backer-upper experiment is a standard performance measure in the neural network literature, but previously the training of the controllers was done off-line. With the CMAC neural architectures, it was possible to train the neuro-controllers on-line in real-time on a MS-DOS PC 386. CMAC neural architectures are also used in conjunction with a hierarchical planning approach to find collision-free paths over 2-D analog valued obstacle fields. The method constructs a coarse resolution version of the original problem and then finds the corresponding coarse optimal path using multipass dynamic programming. CMAC artificial neural architectures are used to estimate the analog transition costs that dynamic programming requires. The CMAC architectures are trained in real-time for each obstacle field presented. The coarse optimal path is then used as a baseline for the construction of a fine scale optimal path through the original obstacle array. These results are a very good indication of the potential power of the neural architectures in control design. In order to reach as wide an audience as possible, we have run a seminar on neuro-control that has met once per week since 20 May 1991. This seminar has thoroughly discussed the CMAC architecture, relevant portions of classical control, back propagation through time, and adaptive critic designs

An Efficient Policy Iteration Algorithm for Dynamic Programming Equations

We present an accelerated algorithm for the solution of static Hamilton-Jacobi-Bellman equations related to optimal control problems. Our scheme is based on a classic policy iteration procedure, which is known to have superlinear convergence in many relevant cases provided the initial guess is sufficiently close to the solution. In many cases, this limitation degenerates into a behavior similar to a value iteration method, with an increased computation time. The new scheme circumvents this problem by combining the advantages of both algorithms with an efficient coupling. The method starts with a value iteration phase and then switches to a policy iteration procedure when a certain error threshold is reached. A delicate point is to determine this threshold in order to avoid cumbersome computation with the value iteration and, at the same time, to be reasonably sure that the policy iteration method will finally converge to the optimal solution. We analyze the methods and efficient coupling in a number of examples in dimension two, three and four illustrating its properties

A hierarchic task-based programming model for distributed heterogeneous computing

Distributed computing platforms are evolving to heterogeneous ecosystems with Clusters, Grids and Clouds introducing in its computing nodes, processors with different core architectures, accelerators (i.e. GPUs, FPGAs), as well as different memories and storage devices in order to achieve better performance with lower energy consumption. As a consequence of this heterogeneity, programming applications for these distributed heterogeneous platforms becomes a complex task. Additionally to the complexity of developing an application for distributed platforms, developers must also deal now with the complexity of the different computing devices inside the node. In this article, we present a programming model that aims to facilitate the development and execution of applications in current and future distributed heterogeneous parallel architectures. This programming model is based on the hierarchical composition of the COMP Superscalar and Omp Superscalar programming models that allow developers to implement infrastructure-agnostic applications. The underlying runtime enables applications to adapt to the infrastructure without the need of maintaining different versions of the code. Our programming model proposal has been evaluated on real platforms, in terms of heterogeneous resource usage, performance and adaptation.This work has been supported by the European Commission through the Horizon 2020 Research and Innovation program under contract 687584 (TANGO project) by the Spanish Government under contract TIN2015-65316 and grant SEV-2015-0493 (Severo Ochoa Program) and by Generalitat de Catalunya under contracts 2014-SGR-1051 and 2014-SGR-1272.Peer ReviewedPostprint (author's final draft

Pricing American Options using Monte Carlo Method

This thesis reviewed a number of Monte Carlo based methods for pricing American options. The least-squares regression based Longstaff-Schwartz method (LSM) for approximating lower bounds of option values and the Duality approach through martingales for estimating the upper bounds of option values were implemented with simple examples of American put options. The effectiveness of these techniques and the dependencies on various simulation parameters were tested and discussed. A computing saving technique was suggested to reduce the computational complexity by constructing regression basis functions which are orthogonal to each other with respect to the natural distribution of the underlying asset price. The orthogonality was achieved by using Hermite polynomials. The technique was tested for both the LSM approach and the Duality approach. At the last, the Multilevel Mote Carlo (MLMC) technique was employed with pricing American options and the effects on variance reduction were discussed. A smoothing technique using artificial probability weighted payoff functions jointly with Brownian Bridge interpolations was proposed to improve the Multilevel Monte Carlo performances for pricing American options

Multigrid methods for two-player zero-sum stochastic games

We present a fast numerical algorithm for large scale zero-sum stochastic games with perfect information, which combines policy iteration and algebraic multigrid methods. This algorithm can be applied either to a true finite state space zero-sum two player game or to the discretization of an Isaacs equation. We present numerical tests on discretizations of Isaacs equations or variational inequalities. We also present a full multi-level policy iteration, similar to FMG, which allows to improve substantially the computation time for solving some variational inequalities.Comment: 31 page