Robust Nonlinear Mapping of Soil Contamination Using Support Regression

Spatial data analysis mapping and visualization is of great importance in various fields: environment, pollution, natural hazards and risks, epidemiology, spatial econometrics, etc. A basic task of spatial mapping is to make predictions based on some empirical data (measurements). A number of state-of-the-art methods can be used for the task: deterministic interpolations, methods of geostatistics: the family of kriging estimators (Deutsch and Journel, 1997), machine learning algorithms such as artificial neural networks (ANN) of different architectures, hybrid ANN-geostatistics models (Kanevski and Maignan, 2004; Kanevski et al., 1996), etc. All the methods mentioned above can be used for solving the problem of spatial data mapping. Environmental empirical data are always contaminated/corrupted by noise, and often with noise of unknown nature. That's one of the reasons why deterministic models can be inconsistent, since they treat the measurements as values of some unknown function that should be interpolated. Kriging estimators treat the measurements as the realization of some spatial randomn process. To obtain the estimation with kriging one has to model the spatial structure of the data: spatial correlation function or (semi-)variogram. This task can be complicated if there is not sufficient number of measurements and variogram is sensitive to outliers and extremes. ANN is a powerful tool, but it also suffers from the number of reasons. of a special type ? multiplayer perceptrons ? are often used as a detrending tool in hybrid (ANN+geostatistics) models (Kanevski and Maignank, 2004). Therefore, development and adaptation of the method that would be nonlinear and robust to noise in measurements, would deal with the small empirical datasets and which has solid mathematical background is of great importance. The present paper deals with such model, based on Statistical Learning Theory (SLT) - Support Vector Regression. SLT is a general mathematical framework devoted to the problem of estimation of the dependencies from empirical data (Hastie et al, 2004; Vapnik, 1998). SLT models for classification - Support Vector Machines - have shown good results on different machine learning tasks. The results of SVM classification of spatial data are also promising (Kanevski et al, 2002). The properties of SVM for regression - Support Vector Regression (SVR) are less studied. First results of the application of SVR for spatial mapping of physical quantities were obtained by the authorsin for mapping of medium porosity (Kanevski et al, 1999), and for mapping of radioactively contaminated territories (Kanevski and Canu, 2000). The present paper is devoted to further understanding of the properties of SVR model for spatial data analysis and mapping. Detailed description of the SVR theory can be found in (Cristianini and Shawe-Taylor, 2000; Smola, 1996) and basic equations for the nonlinear modeling are given in section 2. Section 3 discusses the application of SVR for spatial data mapping on the real case study - soil pollution by Cs137 radionuclide. Section 4 discusses the properties of the modelapplied to noised data or data with outliers.

Micro-ordinateurs de poche : du gadget à l'irremplaçable [Handheld computers: the gadget that became an invaluable tool]

Les ordinateurs de poche ont le potentiel de devenir un outil de travail indispensable pour le médecin praticien. De part une facilité d'utilisation toujours plus grande, un format ultra-portable, une grande capacité de stockage de l'information et de synchronisation à de multiples bases de données, ils permettent l'accès en tout temps et littéralement au lit du malade à une gamme complète de ressources médicales. Cet article se veut une lllustration des différentes possibilités d'utilisation de ces nouveaux outils, au travers de situations cliniques banales rencontrées par tout praticien.

The Alcohol Use Disorders Identification Test (AUDIT) as a screening tool for excessive drinking in primary care: reliability and validity of a French version.

BACKGROUND: Excessive drinking is a major problem in Western countries. AUDIT (Alcohol Use Disorders Identification Test) is a 10-item questionnaire developed as a transcultural screening tool to detect excessive alcohol consumption and dependence in primary health care settings. OBJECTIVES: The aim of the study is to validate a French version of the Alcohol Use Disorders Identification Test (AUDIT). METHODS: We conducted a validation cross-sectional study in three French-speaking areas (Paris, Geneva and Lausanne). We examined psychometric properties of AUDIT as its internal consistency, and its capacity to correctly diagnose alcohol abuse or dependence as defined by DSM-IV and to detect hazardous drinking (defined as alcohol intake >30 g pure ethanol per day for men and >20 g of pure ethanol per day for women). We calculated sensitivity, specificity, positive and negative predictive values and Receiver Operator Characteristic curves. Finally, we compared the ability of AUDIT to accurately detect "alcohol abuse/dependence" with that of CAGE and MAST. RESULTS: 1207 patients presenting to outpatient clinics (Switzerland, n = 580) or general practitioners' (France, n = 627) successively completed CAGE, MAST and AUDIT self-administered questionnaires, and were independently interviewed by a trained addiction specialist. AUDIT showed a good capacity to discriminate dependent patients (with AUDIT > or =13 for males, sensitivity 70.1%, specificity 95.2%, PPV 85.7%, NPV 94.7% and for females sensitivity 94.7%, specificity 98.2%, PPV 100%, NPV 99.8%); and hazardous drinkers (with AUDIT > or =7, for males sensitivity 83.5%, specificity 79.9%, PPV 55.0%, NPV 82.7% and with AUDIT > or =6 for females, sensitivity 81.2%, specificity 93.7%, PPV 64.0%, NPV 72.0%). AUDIT gives better results than MAST and CAGE for detecting "Alcohol abuse/dependence" as showed on the comparative ROC curves. CONCLUSIONS: The AUDIT questionnaire remains a good screening instrument for French-speaking primary care.

Towards Machine Consciousness: Grounding Abstract Models as pi-Processes

We present a two-level model of concurrent communicating systems (CCS) to serve as a basis formachine consciousness. A language implementing threads within logic programming is ¯rstintroduced. This high-level framework allows for the de¯nition of abstract processes that can beexecuted on a virtual machine. We then look for a possible grounding of these processes into thebrain. Towards this end, we map abstract de¯nitions (including logical expressions representingcompiled knowledge) into a variant of the pi-calculus. We illustrate this approach through aseries of examples extending from a purely reactive behavior to patterns of consciousness.

Environmental data mining and modeling based on machine learning algorithms and geostatistics

The paper presents some contemporary approaches to spatial environmental data analysis. The main topics are concentrated on the decision-oriented problems of environmental spatial data mining and modeling: valorization and representativity of data with the help of exploratory data analysis, spatial predictions, probabilistic and risk mapping, development and application of conditional stochastic simulation models. The innovative part of the paper presents integrated/hybrid model-machine learning (ML) residuals sequential simulations-MLRSS. The models are based on multilayer perceptron and support vector regression ML algorithms used for modeling long-range spatial trends and sequential simulations of the residuals. NIL algorithms deliver non-linear solution for the spatial non-stationary problems, which are difficult for geostatistical approach. Geostatistical tools (variography) are used to characterize performance of ML algorithms, by analyzing quality and quantity of the spatially structured information extracted from data with ML algorithms. Sequential simulations provide efficient assessment of uncertainty and spatial variability. Case study from the Chernobyl fallouts illustrates the performance of the proposed model. It is shown that probability mapping, provided by the combination of ML data driven and geostatistical model based approaches, can be efficiently used in decision-making process. (C) 2003 Elsevier Ltd. All rights reserved.

Shallow-transfer rule-based machine translation for Czech to Polish

This article describes the developmentof an Open Source shallow-transfer machine translation system from Czech to Polish in theApertium platform. It gives details ofthe methods and resources used in contructingthe system. Although the resulting system has quite a high error rate, it is still competitive with other systems.

Automatic acquisition of Named Entities for Rule-Based Machine Translation

This paper proposes to enrich RBMTdictionaries with Named Entities(NEs) automatically acquired fromWikipedia. The method is appliedto the Apertium English-Spanishsystem and its performance comparedto that of Apertium with and withouthandtagged NEs. The system withautomatic NEs outperforms the onewithout NEs, while results vary whencompared to a system with handtaggedNEs (results are comparable forSpanish to English but slightly worstfor English to Spanish). Apart fromthat, adding automatic NEs contributesto decreasing the amount of unknownterms by more than 10%.

Taking on new challenges in multi-word unit processing for Machine Translation

This paper discusses the qualitativecomparative evaluation performed on theresults of two machine translation systemswith different approaches to the processing ofmulti-word units. It proposes a solution forovercoming the difficulties multi-word unitspresent to machine translation by adopting amethodology that combines the lexicongrammar approach with OpenLogos ontologyand semantico-syntactic rules. The paper alsodiscusses the importance of a qualitativeevaluation metrics to correctly evaluate theperformance of machine translation engineswith regards to multi-word units.

A Widely Used Machine Translation Service and its Migration to a Free/Open-Source Solution : the Case of Softcatalà

Softcatalà is a non-profit associationcreated more than 10 years ago to fightthe marginalisation of the Catalan languagein information and communicationtechnologies. It has led the localisationof many applications and thecreation of a website which allows itsusers to translate texts between Spanishand Catalan using an external closed-sourcetranslation engine. Recently,the closed-source translation back-endhas been replaced by a free/open-sourcesolution completely managed by Softcatalà: the Apertium machine translationplatform and the ScaleMT web serviceframework. Thanks to the opennessof the new solution, it is possibleto take advantage of the huge amount ofusers of the Softcatalà translation serviceto improve it, using a series ofmethods presented in this paper. In addition,a study of the translations requestedby the users has been carriedout, and it shows that the translationback-end change has not affected theusage patterns.

Patient hand hygiene compliance in the hemodialysis environment

Hand hygiene compliance of patients receiving hemodialysis treatment is a contemporary discussion topic among health care professionals in the Nephrology Clinic of Helsinki University City Hospital. The purpose of the Final Thesis is to review patient hand hygiene in terms of risks its lack entails and based on the evidence based findings to design an end product as a poster. The poster can be utilised in the Nephrology Clinic's nursing environment to educate and motivate patients to pay specifid attention to the importance of hand hygiene. The method used was a systematic literature review. The most important evidence based findings were extracted from the chosen thirteen scientific articles. All articles were searched from the Cumulative Index to Nursing and Allied Health Literature electronic database. The gathered information was then used to build the content of a patient education tool that for this project was defined as a Poster. The findings in this study showed that transmission of bloodborne infections, like Hepatitis B or C virus can occur through a vascular access and that the consequences of this can be very fatal. Additionally, environmental surfaces such as furniture, door knobs and dialysis machine control knobs were all possible infection sources for the patient receiving hemodialysis treatment. Adherence to good hand hygiene behaviour lowered the risk for infections. The end product of this study is a poster that is targeted to patients undergoing hemodialysis treatment. Using a health promotion agenda in the Poster, it is hoped that patients will pay more attention to the importance of hand hygiene and that they will be more motivated to use aseptic methods such as alcohol based hand rubs in the hemodialysis setting.

Hybrid model of a double-fed induction machine and back-to-back converter

This report details the port interconnection of two subsystems: a power electronics subsystem (a back-to-back AC/AC converter (B2B), coupled to a phase of the power grid), and an electromechanical subsystem (a doubly-fed induction machine (DFIM), coupled mechanically to a flywheel and electrically to the power grid and to a local varying load). Both subsystems have been essentially described in previous reports (deliverables D 0.5 and D 4.3.1), although some previously unpublished details are presented here. The B2B is a variable structure system (VSS), due to the presence of control-actuated switches: however from a modelling and simulation, as well as a control-design, point of view, it is sensible to consider modulated transformers (MTF in the bond-graph language) instead of the pairs of complementary switches. The port-Hamiltonian models of both subsystems are presents and coupled through a power-preserving interconnection, and the Hamiltonian description of the whole system is obtained; detailed bond-graphs of all the subsystems and the complete system are provided.