Multi-scale support vector algorithms for hot spot detection and modelling

The algorithmic approach to data modelling has developed rapidly these last years, in particular methods based on data mining and machine learning have been used in a growing number of applications. These methods follow a data-driven methodology, aiming at providing the best possible generalization and predictive abilities instead of concentrating on the properties of the data model. One of the most successful groups of such methods is known as Support Vector algorithms. Following the fruitful developments in applying Support Vector algorithms to spatial data, this paper introduces a new extension of the traditional support vector regression (SVR) algorithm. This extension allows for the simultaneous modelling of environmental data at several spatial scales. The joint influence of environmental processes presenting different patterns at different scales is here learned automatically from data, providing the optimum mixture of short and large-scale models. The method is adaptive to the spatial scale of the data. With this advantage, it can provide efficient means to model local anomalies that may typically arise in situations at an early phase of an environmental emergency. However, the proposed approach still requires some prior knowledge on the possible existence of such short-scale patterns. This is a possible limitation of the method for its implementation in early warning systems. The purpose of this paper is to present the multi-scale SVR model and to illustrate its use with an application to the mapping of Cs137 activity given the measurements taken in the region of Briansk following the Chernobyl accident.

Treatment motivation in adolescents with psychosis or at high risk: Determinants and impact on improvements in symptoms and cognitive functioning, preliminary results.

Abstract Low motivation is frequent in chronic disorders such as psychosis and may limit treatment efficacy. Although some evidence supports this view in adults, few studies so far have focused on adolescents. We assessed the impact of baseline symptoms, cognitive deficits and cognitive treatment characteristics on treatment motivation (TM), and examined whether TM affected treatment outcome. Twenty-eight adolescents with psychotic disorders participated in 16 sessions of computerized cognitive remediation or games. TM was assessed for each session. Lower TM was predicted by more severe symptoms at baseline, and was associated with smaller improvements in symptoms and both cognitive and psychosocial functioning at the end of the intervention. Experiencing success in the treatment exercises enhanced TM in all patients.