Abstract
Objective: This work presents a novel approach for the automated prediction of the aesthetic result of breast cancer conservative treatment (BCCT). Cosmetic assessment plays a major rote in the study of BCCT. Objective assessment methods are being preferred to overcome the drawbacks of subjective evaluation. Methodology: The problem is addressed as a pattern recognition task. A dataset of images of patients was classified in four classes (excellent, good, fair, poor) by a panel of international experts, providing a gold standard classification. As possible types of objective features we considered those already identified by domain experts as relevant to the aesthetic evaluation of the surgical procedure, namely those assessing breast asymmetry, skin colour difference and scar visibility. A classifier based on support vector machines was developed from objective features extracted from the reference dataset. Results: A correct classification rate of about 70% was obtained when categorizing a set of unseen images into the aforementioned four classes. This accuracy is comparable with the result of the best evaluator from the panel of experts. Conclusion: The results obtained are rather encouraging and the developed tool, could be very helpful in assuring objective assessment of the aesthetic outcome of BCCT.

Abstract
The basic statistical features of radon time series from continuous radon monitoring at the Elat granite, Israel are analysed. A similar analysis is carried out for ancillary and possibly related geophysical parameters for the Elat area. The results show that air temperature, precipitable water and longwave radiation time series exhibit constant variance over the analyzed period, while radon time series, atmospheric pressure, short-wave radiation and total electron content exhibit heteroscedasticity. Furthermore, for radon and shortwave radiation the variability is associated with the overall mean level, while for atmospheric pressure such an association is not present. The analyzed radon time series not only are non-stationary but also nonlinear, reflecting the complex dynamics of radon emanation and transport in natural subsurface systems.

Abstract
This paper presents the development of a bivalve farmer agent interacting with a realistic ecological simulation system. The purpose of the farmer agent is to determine the best combinations of bivalve seeding areas in a large region, maximizing the production without exceeding the total allowed seeding area. A system based on simulated annealing, tabu search, genetic algorithms and reinforcement learning, was developed to minimize the number of iterations required to unravel a semi-optimum solution by using customizable tactics. The farmer agent is part of a multi-agent system where several agents, representing human interaction with the coastal ecosystems, communicate with a realistic simulator developed especially for aquatic ecological simulations. The experiments performed revealed promising results in the field of optimization techniques and multi-agent systems applied to ecological simulations. The results obtained open many other possible uses of the simulation architecture with applications in industrial and ecological management problems, towards sustainable development.

Abstract
In this demonstration, I exemplify how a musical channel of communication can be established in computer-mediated interaction between musicians and dancers in real time. This channel of communication uses a software library implemented as a library of external objects for Max/MSP[1], that processes data from an object or library that performs frame-differencing analysis of a video stream in real time in this programming environment.

Abstract

Abstract
One of the challenges in Grid computing research is to provide a means to automatically submit, manage, and monitor applications whose main characteristic is to be composed of a large number of tasks. The large number of explicit tasks, generally placed on a centralized job queue, can cause several problems: (1) they can quickly exhaust the memory of the submission machine; (2) they can deteriorate the response time of the submission machine due to these demanding too many open ports to manage remote execution of each of the tasks; (3) they may cause network traffic congestion if all tasks try to transfer input and/or output files across the network at the same time; (4) they make it impossible for the user to follow execution progress without an automatic tool or interface; (5) they may depend on fault-tolerance mechanisms implemented at application level to ensure that all tasks terminate successfully. In this work we present and validate a novel architectural model, GRAND (Grid Robust ApplicatioN Deployment), whose main objective is to deal with the submission of a large numbers of tasks. Copyright (c) 2006 John Wiley & Sons, Ltd.