Quelques conséquences de l'application du nouveau code pénal suisse sur la psychiatrie légale [Some consequences of the application of the new Swiss penal code on legal psychiatry]

The new text of the Swiss penal code, which entered into effect at the beginning of 2007, has many incidences on the practice of the psychiatrists realizing expertises in the penal field or engaged in the application of legal measures imposing a treatment. The most notable consequences of this text are, on the one hand, a new definition of the concept of penal irresponsibility which is not necessarily any more related to a psychiatric diagnosis and, on the other hand, a new definition of legal constraints that justice can take to prevent new punishable acts and which appreciably modifies the place of the psychiatrists in the questions binding psychiatric care and social control.

An Automatic Approach for Learning and Tuning Gaussian Interval Type-2 Fuzzy Membership Functions Applied to Lung CAD Classification System

The potential of type-2 fuzzy sets for managing high levels of uncertainty in the subjective knowledge of experts or of numerical information has focused on control and pattern classification systems in recent years. One of the main challenges in designing a type-2 fuzzy logic system is how to estimate the parameters of type-2 fuzzy membership function (T2MF) and the Footprint of Uncertainty (FOU) from imperfect and noisy datasets. This paper presents an automatic approach for learning and tuning Gaussian interval type-2 membership functions (IT2MFs) with application to multi-dimensional pattern classification problems. T2MFs and their FOUs are tuned according to the uncertainties in the training dataset by a combination of genetic algorithm (GA) and crossvalidation techniques. In our GA-based approach, the structure of the chromosome has fewer genes than other GA methods and chromosome initialization is more precise. The proposed approach addresses the application of the interval type-2 fuzzy logic system (IT2FLS) for the problem of nodule classification in a lung Computer Aided Detection (CAD) system. The designed IT2FLS is compared with its type-1 fuzzy logic system (T1FLS) counterpart. The results demonstrate that the IT2FLS outperforms the T1FLS by more than 30% in terms of classification accuracy.

Report on applying agreed-upon procedures to the City of Protivin’s certification of compliance with Chapter 388.10 of the Code of Iowa

Report on applying agreed-upon procedures to the City of Protivin’s certification of compliance with Chapter 388.10 of the Code of Iowa

Computation of multiple correspondence analysis, with code in R

The generalization of simple correspondence analysis, for two categorical variables, to multiple correspondence analysis where they may be three or more variables, is not straighforward, both from a mathematical and computational point of view. In this paper we detail the exact computational steps involved in performing a multiple correspondence analysis, including the special aspects of adjusting the principal inertias to correct the percentages of inertia, supplementary points and subset analysis. Furthermore, we give the algorithm for joint correspondence analysis where the cross-tabulations of all unique pairs of variables are analysed jointly. The code in the R language for every step of the computations is given, as well as the results of each computation.