Combinatorial Tools for Regge Calculus

In this short note we briefly review some recent mathematical results relevant to the classical Regge Calculus evolution problem.Comment: 5 pages, LaTeX, no figures. To appear on the Proceedings of the 12th Italian Conference on General Relativity and Gravitational Physic

The Use of HepRep in GLAST

HepRep is a generic, hierarchical format for description of graphics representables that can be augmented by physics information and relational properties. It was developed for high energy physics event display applications and is especially suited to client/server or component frameworks. The GLAST experiment, an international effort led by NASA for a gamma-ray telescope to launch in 2006, chose HepRep to provide a flexible, extensible and maintainable framework for their event display without tying their users to any one graphics application. To support HepRep in their GUADI infrastructure, GLAST developed a HepRep filler and builder architecture. The architecture hides the details of XML and CORBA in a set of base and helper classes allowing physics experts to focus on what data they want to represent. GLAST has two GAUDI services: HepRepSvc, which registers HepRep fillers in a global registry and allows the HepRep to be exported to XML, and CorbaSvc, which allows the HepRep to be published through a CORBA interface and which allows the client application to feed commands back to GAUDI (such as start next event, or run some GAUDI algorithm). GLAST's HepRep solution gives users a choice of client applications, WIRED (written in Java) or FRED (written in C++ and Ruby), and leaves them free to move to any future HepRep-compliant event display.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics (CHEP03), La Jolla, Ca, USA, March 2003, 9 pages pdf, 15 figures. PSN THLT00

Distributed interpolatory algorithms for set membership estimation

This work addresses the distributed estimation problem in a set membership framework. The agents of a network collect measurements which are affected by bounded errors, thus implying that the unknown parameters to be estimated belong to a suitable feasible set. Two distributed algorithms are considered, based on projections of the estimate of each agent onto its local feasible set. The main contribution of the paper is to show that such algorithms are asymptotic interpolatory estimators, i.e. they converge to an element of the global feasible set, under the assumption that the feasible set associated to each measurement is convex. The proposed techniques are demonstrated on a distributed linear regression estimation problem

Prehistoric landscape dynamics and social complexity in the Erei uplands (Central Sicily) between the 5th and the 3rd millennium cal. BC

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A Distributed Asynchronous Method of Multipliers for Constrained Nonconvex Optimization

This paper presents a fully asynchronous and distributed approach for tackling optimization problems in which both the objective function and the constraints may be nonconvex. In the considered network setting each node is active upon triggering of a local timer and has access only to a portion of the objective function and to a subset of the constraints. In the proposed technique, based on the method of multipliers, each node performs, when it wakes up, either a descent step on a local augmented Lagrangian or an ascent step on the local multiplier vector. Nodes realize when to switch from the descent step to the ascent one through an asynchronous distributed logic-AND, which detects when all the nodes have reached a predefined tolerance in the minimization of the augmented Lagrangian. It is shown that the resulting distributed algorithm is equivalent to a block coordinate descent for the minimization of the global augmented Lagrangian. This allows one to extend the properties of the centralized method of multipliers to the considered distributed framework. Two application examples are presented to validate the proposed approach: a distributed source localization problem and the parameter estimation of a neural network.Comment: arXiv admin note: substantial text overlap with arXiv:1803.0648

Electroencephalographic Findings in Deaf Psychotic Patients

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