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.

Changes in the isoflavone profile and in the chemical composition of tempeh during processing and refrigeration

The objective of this work was to analyze changes in the isoflavone profile, determined by high performance liquid chromatography, at different processing stages and after refrigeration of tempeh. For tempeh production, clean soybean grains from cultivars BR 36 (low isoflavone content) and IAS 5 (high) were dehulled, and the separated cotyledons were hydrated and then cooked in boiling water for 30 min. Spores of the fungus Rhizopus microsporus var. oligosporus were inoculated in the cooked and cooled cotyledons, and incubated at 32ºC for 6, 12, 18, and 24 hours in perforated polypropylene bags, for fermentation. The resulting tempeh was stored at 4ºC for 6, 12, 18, and 24 hours. After 24-hour fermentation, isoflavone glucosides were 50% reduced, and the aglycone forms in the tempeh from both cultivars was increased. The malonyl forms reduced 83% after cooking. Less than 24 hours of refrigeration did not affect the isoflavone profile of tempeh from either cultivar, which is a good indicator of its quality. The tempeh maintains the high and low isoflavone content of the cultivars, which indicates that cultivar differences in this trait should be considered when processing tempeh.

Reconciling phonological neighborhood effects in speech production through single trial analysis

A crucial step for understanding how lexical knowledge is represented is to describe the relative similarity of lexical items, and how it influences language processing. Previous studies of the effects of form similarity on word production have reported conflicting results, notably within and across languages. The aim of the present study was to clarify this empirical issue to provide specific constraints for theoretical models of language production. We investigated the role of phonological neighborhood density in a large-scale picture naming experiment using fine-grained statistical models. The results showed that increasing phonological neighborhood density has a detrimental effect on naming latencies, and re-analyses of independently obtained data sets provide supplementary evidence for this effect. Finally, we reviewed a large body of evidence concerning phonological neighborhood density effects in word production, and discussed the occurrence of facilitatory and inhibitory effects in accuracy measures. The overall pattern shows that phonological neighborhood generates two opposite forces, one facilitatory and one inhibitory. In cases where speech production is disrupted (e.g. certain aphasic symptoms), the facilitatory component may emerge, but inhibitory processes dominate in efficient naming by healthy speakers. These findings are difficult to accommodate in terms of monitoring processes, but can be explained within interactive activation accounts combining phonological facilitation and lexical competition.

Sistema de presa de decisions distribuït

DDM is a framework that combines intelligent agents and artificial intelligence traditional algorithms such as classifiers. The central idea of this project is to create a multi-agent system that allows to compare different views into a single one.

Características morfofisiológicas de abacaxizeiro 'gomo de mel' enraizado in vitro sob luz natural e substrato vermiculita

Objetivou-se avaliar o efeito da vermiculita, ágar, da luz artificial e a da natural no enraizamento in vitro de brotos de abacaxizeiro 'Gomo de Mel', bem como caracterizar anatomicamente essas plantas. O trabalho foi realizado no laboratório de Cultura de Tecidos Vegetais, do Departamento de Agricultura - UFLA, Lavras-MG. Foram utilizados brotos com 2 cm de comprimento, cultivados em meio MS acrescido de 30g.L-1 de sacarose. Testaram-se dois suportes físicos: 6g.L-1 de ágar e 15 g.L-1 de vermiculita para o enraizamento dos brotos em dois ambientes: sala de crescimento a 25±1 ºC, 45 W.m-2.s-1 durante 16 horas e casa de vegetação com radiação de 115,08 W.m-2.s-1 e 33 ºC (luz natural). Após 60 dias, avaliaram-se comprimento de parte aérea, massa fresca e seca de parte aérea e raízes, espessuras dos tecidos do limbo foliar, além de número, diâmetro polar e equatorial dos estômatos. O experimento foi instalado em delineamento inteiramente casualizado. Os resultados mostraram-se significativos para interação entre suportes físicos e ambientes para todas as variáveis analisadas. O uso do substrato vermiculita em luz artificial apresentou melhores resultados para todas as variáveis, exceto para número de estômatos. Para as características anatômicas, maiores espessuras dos tecidos do limbo foliar foram verificadas quando se utilizaram vermiculita e luz natural, sendo que, para o uso de ágar, também houve aumento das espessuras somente quando se utilizou o ambiente de luz natural. Quanto ao número de estômatos/mm², não houve diferença significativa para os tratamentos. Maior diâmetro polar e equatorial foi observado em estômatos de folhas cultivadas em luz artificial e vermiculita, e luz natural e vermiculita, respectivamente.

Paper Surface Image Processing for Roughness Quality Measurement

Diplomityössä on käsitelty paperin pinnankarkeuden mittausta, joka on keskeisimpiä ongelmia paperimateriaalien tutkimuksessa. Paperiteollisuudessa käytettävät mittausmenetelmät sisältävät monia haittapuolia kuten esimerkiksi epätarkkuus ja yhteensopimattomuus sileiden papereiden mittauksissa, sekä suuret vaatimukset laboratorio-olosuhteille ja menetelmien hitaus. Työssä on tutkittu optiseen sirontaan perustuvia menetelmiä pinnankarkeuden määrittämisessä. Konenäköä ja kuvan-käsittelytekniikoita tutkittiin karkeilla paperipinnoilla. Tutkimuksessa käytetyt algoritmit on tehty Matlab® ohjelmalle. Saadut tulokset osoittavat mahdollisuuden pinnankarkeuden mittaamiseen kuvauksen avulla. Parhaimman tuloksen perinteisen ja kuvausmenetelmän välillä antoi fraktaaliulottuvuuteen perustuva menetelmä.

Intelligent Internet Data Searching

In this thesis author approaches the problem of automated text classification, which is one of basic tasks for building Intelligent Internet Search Agent. The work discusses various approaches to solving sub-problems of automated text classification, such as feature extraction and machine learning on text sources. Author also describes her own multiword approach to feature extraction and pres-ents the results of testing this approach using linear discriminant analysis based classifier, and classifier combining unsupervised learning for etalon extraction with supervised learning using common backpropagation algorithm for multilevel perceptron.

Automatic target validation based on neuroscientific literature mining for tractography.

Target identification for tractography studies requires solid anatomical knowledge validated by an extensive literature review across species for each seed structure to be studied. Manual literature review to identify targets for a given seed region is tedious and potentially subjective. Therefore, complementary approaches would be useful. We propose to use text-mining models to automatically suggest potential targets from the neuroscientific literature, full-text articles and abstracts, so that they can be used for anatomical connection studies and more specifically for tractography. We applied text-mining models to three structures: two well-studied structures, since validated deep brain stimulation targets, the internal globus pallidus and the subthalamic nucleus and, the nucleus accumbens, an exploratory target for treating psychiatric disorders. We performed a systematic review of the literature to document the projections of the three selected structures and compared it with the targets proposed by text-mining models, both in rat and primate (including human). We ran probabilistic tractography on the nucleus accumbens and compared the output with the results of the text-mining models and literature review. Overall, text-mining the literature could find three times as many targets as two man-weeks of curation could. The overall efficiency of the text-mining against literature review in our study was 98% recall (at 36% precision), meaning that over all the targets for the three selected seeds, only one target has been missed by text-mining. We demonstrate that connectivity for a structure of interest can be extracted from a very large amount of publications and abstracts. We believe this tool will be useful in helping the neuroscience community to facilitate connectivity studies of particular brain regions. The text mining tools used for the study are part of the HBP Neuroinformatics Platform, publicly available at http://connectivity-brainer.rhcloud.com/.

Call for uniform neuropsychological assessment after aneurysmal subarachnoid hemorrhage: Swiss recommendations.

BACKGROUND: In a high proportion of patients with favorable outcome after aneurysmal subarachnoid hemorrhage (aSAH), neuropsychological deficits, depression, anxiety, and fatigue are responsible for the inability to return to their regular premorbid life and pursue their professional careers. These problems often remain unrecognized, as no recommendations concerning a standardized comprehensive assessment have yet found entry into clinical routines. METHODS: To establish a nationwide standard concerning a comprehensive assessment after aSAH, representatives of all neuropsychological and neurosurgical departments of those eight Swiss centers treating acute aSAH have agreed on a common protocol. In addition, a battery of questionnaires and neuropsychological tests was selected, optimally suited to the deficits found most prevalent in aSAH patients that was available in different languages and standardized. RESULTS: We propose a baseline inpatient neuropsychological screening using the Montreal Cognitive Assessment (MoCA) between days 14 and 28 after aSAH. In an outpatient setting at 3 and 12 months after bleeding, we recommend a neuropsychological examination, testing all relevant domains including attention, speed of information processing, executive functions, verbal and visual learning/memory, language, visuo-perceptual abilities, and premorbid intelligence. In addition, a detailed assessment capturing anxiety, depression, fatigue, symptoms of frontal lobe affection, and quality of life should be performed. CONCLUSIONS: This standardized neuropsychological assessment will lead to a more comprehensive assessment of the patient, facilitate the detection and subsequent treatment of previously unrecognized but relevant impairments, and help to determine the incidence, characteristics, modifiable risk factors, and the clinical course of these impairments after aSAH.

A Sparse Bayesian Approach for Joint Feature Selection and Classifier Learning

Peer-reviewed