Probabilistic electrical resistivity tomography of a CO2 sequestration analog

Electrical resistivity tomography (ERT) is a well-established method for geophysical characterization and has shown potential for monitoring geologic CO2 sequestration, due to its sensitivity to electrical resistivity contrasts generated by liquid/gas saturation variability. In contrast to deterministic inversion approaches, probabilistic inversion provides the full posterior probability density function of the saturation field and accounts for the uncertainties inherent in the petrophysical parameters relating the resistivity to saturation. In this study, the data are from benchtop ERT experiments conducted during gas injection into a quasi-2D brine-saturated sand chamber with a packing that mimics a simple anticlinal geological reservoir. The saturation fields are estimated by Markov chain Monte Carlo inversion of the measured data and compared to independent saturation measurements from light transmission through the chamber. Different model parameterizations are evaluated in terms of the recovered saturation and petrophysical parameter values. The saturation field is parameterized (1) in Cartesian coordinates, (2) by means of its discrete cosine transform coefficients, and (3) by fixed saturation values in structural elements whose shape and location is assumed known or represented by an arbitrary Gaussian Bell structure. Results show that the estimated saturation fields are in overall agreement with saturations measured by light transmission, but differ strongly in terms of parameter estimates, parameter uncertainties and computational intensity. Discretization in the frequency domain (as in the discrete cosine transform parameterization) provides more accurate models at a lower computational cost compared to spatially discretized (Cartesian) models. A priori knowledge about the expected geologic structures allows for non-discretized model descriptions with markedly reduced degrees of freedom. Constraining the solutions to the known injected gas volume improved estimates of saturation and parameter values of the petrophysical relationship. (C) 2014 Elsevier B.V. All rights reserved.

Two-dimensional probabilistic inversion of plane-wave electromagnetic data: Methodology, model constraints and joint inversion with electrical resistivity data

Probabilistic inversion methods based on Markov chain Monte Carlo (MCMC) simulation are well suited to quantify parameter and model uncertainty of nonlinear inverse problems. Yet, application of such methods to CPU-intensive forward models can be a daunting task, particularly if the parameter space is high dimensional. Here, we present a 2-D pixel-based MCMC inversion of plane-wave electromagnetic (EM) data. Using synthetic data, we investigate how model parameter uncertainty depends on model structure constraints using different norms of the likelihood function and the model constraints, and study the added benefits of joint inversion of EM and electrical resistivity tomography (ERT) data. Our results demonstrate that model structure constraints are necessary to stabilize the MCMC inversion results of a highly discretized model. These constraints decrease model parameter uncertainty and facilitate model interpretation. A drawback is that these constraints may lead to posterior distributions that do not fully include the true underlying model, because some of its features exhibit a low sensitivity to the EM data, and hence are difficult to resolve. This problem can be partly mitigated if the plane-wave EM data is augmented with ERT observations. The hierarchical Bayesian inverse formulation introduced and used herein is able to successfully recover the probabilistic properties of the measurement data errors and a model regularization weight. Application of the proposed inversion methodology to field data from an aquifer demonstrates that the posterior mean model realization is very similar to that derived from a deterministic inversion with similar model constraints.

Feature Selection, Ranking of Each Feature and Classification for the Diagnosis of Community Acquired Legionella Pneumonia

Diagnosis of community acquired legionella pneumonia (CALP) is currently performed by means of laboratory techniques which may delay diagnosis several hours. To determine whether ANN can categorize CALP and non-legionella community-acquired pneumonia (NLCAP) and be standard for use by clinicians, we prospectively studied 203 patients with community-acquired pneumonia (CAP) diagnosed by laboratory tests. Twenty one clinical and analytical variables were recorded to train a neural net with two classes (LCAP or NLCAP class). In this paper we deal with the problem of diagnosis, feature selection, and ranking of the features as a function of their classification importance, and the design of a classifier the criteria of maximizing the ROC (Receiving operating characteristics) area, which gives a good trade-off between true positives and false negatives. In order to guarantee the validity of the statistics; the train-validation-test databases were rotated by the jackknife technique, and a multistarting procedure was done in order to make the system insensitive to local maxima.

Probabilistic age classification with Bayesian networks: a study on the ossification status of the medial clavicular epiphysis

In the past few decades, the rise of criminal, civil and asylum cases involving young people lacking valid identification documents has generated an increase in the demand of age estimation. The chronological age or the probability that an individual is older or younger than a given age threshold are generally estimated by means of some statistical methods based on observations performed on specific physical attributes. Among these statistical methods, those developed in the Bayesian framework allow users to provide coherent and transparent assignments which fulfill forensic and medico-legal purposes. The application of the Bayesian approach is facilitated by using probabilistic graphical tools, such as Bayesian networks. The aim of this work is to test the performances of the Bayesian network for age estimation recently presented in scientific literature in classifying individuals as older or younger than 18 years of age. For these exploratory analyses, a sample related to the ossification status of the medial clavicular epiphysis available in scientific literature was used. Results obtained in the classification are promising: in the criminal context, the Bayesian network achieved, on the average, a rate of correct classifications of approximatively 97%, whilst in the civil context, the rate is, on the average, close to the 88%. These results encourage the continuation of the development and the testing of the method in order to support its practical application in casework.

Probabilistic graphical models to deal with age estimation of living persons

Due to the rise of criminal, civil and administrative judicial situations involving people lacking valid identity documents, age estimation of living persons has become an important operational procedure for numerous forensic and medicolegal services worldwide. The chronological age of a given person is generally estimated from the observed degree of maturity of some selected physical attributes by means of statistical methods. However, their application in the forensic framework suffers from some conceptual and practical drawbacks, as recently claimed in the specialised literature. The aim of this paper is therefore to offer an alternative solution for overcoming these limits, by reiterating the utility of a probabilistic Bayesian approach for age estimation. This approach allows one to deal in a transparent way with the uncertainty surrounding the age estimation process and to produce all the relevant information in the form of posterior probability distribution about the chronological age of the person under investigation. Furthermore, this probability distribution can also be used for evaluating in a coherent way the possibility that the examined individual is younger or older than a given legal age threshold having a particular legal interest. The main novelty introduced by this work is the development of a probabilistic graphical model, i.e. a Bayesian network, for dealing with the problem at hand. The use of this kind of probabilistic tool can significantly facilitate the application of the proposed methodology: examples are presented based on data related to the ossification status of the medial clavicular epiphysis. The reliability and the advantages of this probabilistic tool are presented and discussed.

Pose-based SLAM with probabilistic scan matching algorithm using a mechanical scanned imaging sonar

This paper proposes a pose-based algorithm to solve the full SLAM problem for an autonomous underwater vehicle (AUV), navigating in an unknown and possibly unstructured environment. The technique incorporate probabilistic scan matching with range scans gathered from a mechanical scanning imaging sonar (MSIS) and the robot dead-reckoning displacements estimated from a Doppler velocity log (DVL) and a motion reference unit (MRU). The proposed method utilizes two extended Kalman filters (EKF). The first, estimates the local path travelled by the robot while grabbing the scan as well as its uncertainty and provides position estimates for correcting the distortions that the vehicle motion produces in the acoustic images. The second is an augment state EKF that estimates and keeps the registered scans poses. The raw data from the sensors are processed and fused in-line. No priory structural information or initial pose are considered. The algorithm has been tested on an AUV guided along a 600 m path within a marina environment, showing the viability of the proposed approach

Kernel-Based Ranking. Methods for Learning and Performance Estimation

Machine learning provides tools for automated construction of predictive models in data intensive areas of engineering and science. The family of regularized kernel methods have in the recent years become one of the mainstream approaches to machine learning, due to a number of advantages the methods share. The approach provides theoretically well-founded solutions to the problems of under- and overfitting, allows learning from structured data, and has been empirically demonstrated to yield high predictive performance on a wide range of application domains. Historically, the problems of classification and regression have gained the majority of attention in the field. In this thesis we focus on another type of learning problem, that of learning to rank. In learning to rank, the aim is from a set of past observations to learn a ranking function that can order new objects according to how well they match some underlying criterion of goodness. As an important special case of the setting, we can recover the bipartite ranking problem, corresponding to maximizing the area under the ROC curve (AUC) in binary classification. Ranking applications appear in a large variety of settings, examples encountered in this thesis include document retrieval in web search, recommender systems, information extraction and automated parsing of natural language. We consider the pairwise approach to learning to rank, where ranking models are learned by minimizing the expected probability of ranking any two randomly drawn test examples incorrectly. The development of computationally efficient kernel methods, based on this approach, has in the past proven to be challenging. Moreover, it is not clear what techniques for estimating the predictive performance of learned models are the most reliable in the ranking setting, and how the techniques can be implemented efficiently. The contributions of this thesis are as follows. First, we develop RankRLS, a computationally efficient kernel method for learning to rank, that is based on minimizing a regularized pairwise least-squares loss. In addition to training methods, we introduce a variety of algorithms for tasks such as model selection, multi-output learning, and cross-validation, based on computational shortcuts from matrix algebra. Second, we improve the fastest known training method for the linear version of the RankSVM algorithm, which is one of the most well established methods for learning to rank. Third, we study the combination of the empirical kernel map and reduced set approximation, which allows the large-scale training of kernel machines using linear solvers, and propose computationally efficient solutions to cross-validation when using the approach. Next, we explore the problem of reliable cross-validation when using AUC as a performance criterion, through an extensive simulation study. We demonstrate that the proposed leave-pair-out cross-validation approach leads to more reliable performance estimation than commonly used alternative approaches. Finally, we present a case study on applying machine learning to information extraction from biomedical literature, which combines several of the approaches considered in the thesis. The thesis is divided into two parts. Part I provides the background for the research work and summarizes the most central results, Part II consists of the five original research articles that are the main contribution of this thesis.

Probabilistic structure of an annual extreme rainfall series of a coastal area of the State of São Paulo, Brazil

This study aimed to describe the probabilistic structure of the annual series of extreme daily rainfall (Preabs), available from the weather station of Ubatuba, State of São Paulo, Brazil (1935-2009), by using the general distribution of extreme value (GEV). The autocorrelation function, the Mann-Kendall test, and the wavelet analysis were used in order to evaluate the presence of serial correlations, trends, and periodical components. Considering the results obtained using these three statistical methods, it was possible to assume the hypothesis that this temporal series is free from persistence, trends, and periodicals components. Based on quantitative and qualitative adhesion tests, it was found that the GEV may be used in order to quantify the probabilities of the Preabs data. The best results of GEV were obtained when the parameters of this function were estimated using the method of maximum likelihood. The method of L-moments has also shown satisfactory results.

The probabilistic risk analysis of external hazards of an interim storage for spent nuclear fuel

Tässä diplomityössä tehtiin Olkiluodon ydinvoimalaitoksella sijaitsevan käytetyn ydinpolttoaineen allasvarastointiin perustuvan välivaraston todennäköisyysperustainen ulkoisten uhkien riskianalyysi. Todennäköisyysperustainen riskianalyysi (PRA) on yleisesti käytetty riskien tunnistus- ja lähestymistapa ydinvoimalaitoksella. Työn tarkoituksena oli laatia täysin uusi ulkoisten uhkien PRA-analyysi, koska Suomessa ei ole aiemmin tehty vastaavanlaisia tämän tutkimusalueen riskitarkasteluja. Riskitarkastelun motiivina ovat myös maailmalla tapahtuneiden luonnonkatastrofien vuoksi korostunut ulkoisten uhkien rooli käytetyn ydinpolttoaineen välivarastoinnin turvallisuudessa. PRA analyysin rakenne pohjautui tutkimuksen alussa luotuun metodologiaan. Analyysi perustuu mahdollisten ulkoisten uhkien tunnistamiseen pois lukien ihmisen aikaansaamat tahalliset vahingot. Tunnistettujen ulkoisten uhkien esiintymistaajuuksien ja vahingoittamispotentiaalin perusteella ulkoiset uhat joko karsittiin pois tutkimuksessa määriteltyjen karsintakriteerien avulla tai analysoitiin tarkemmin. Tutkimustulosten perusteella voitiin todeta, että tiedot hyvin harvoin tapahtuvista ulkoisista uhista ovat epätäydellisiä. Suurinta osaa näistä hyvin harvoin tapahtuvista ulkoisista uhista ei ole koskaan esiintynyt eikä todennäköisesti koskaan tule esiintymään Olkiluodon vaikutusalueella tai edes Suomessa. Esimerkiksi salaman iskujen ja öljyaltistuksen roolit ja vaikutukset erilaisten komponenttien käytettävyyteen ovat epävarmasti tunnettuja. Tutkimuksen tuloksia voidaan pitää kokonaisuudessaan merkittävinä, koska niiden perusteella voidaan osoittaa ne ulkoiset uhat, joiden vaikutuksia olisi syytä tutkia tarkemmin. Yksityiskohtaisempi tietoisuus hyvin harvoin esiintyvistä ulkoisista uhista tarkentaisi alkutapahtumataajuuksien estimaatteja.

Reflexive Space. A Constructionist Model of the Russian Reflexive Marker

This study examines the structure of the Russian Reflexive Marker ( ся/-сь) and offers a usage-based model building on Construction Grammar and a probabilistic view of linguistic structure. Traditionally, reflexive verbs are accounted for relative to non-reflexive verbs. These accounts assume that linguistic structures emerge as pairs. Furthermore, these accounts assume directionality where the semantics and structure of a reflexive verb can be derived from the non-reflexive verb. However, this directionality does not necessarily hold diachronically. Additionally, the semantics and the patterns associated with a particular reflexive verb are not always shared with the non-reflexive verb. Thus, a model is proposed that can accommodate the traditional pairs as well as for the possible deviations without postulating different systems. A random sample of 2000 instances marked with the Reflexive Marker was extracted from the Russian National Corpus and the sample used in this study contains 819 unique reflexive verbs. This study moves away from the traditional pair account and introduces the concept of Neighbor Verb. A neighbor verb exists for a reflexive verb if they share the same phonological form excluding the Reflexive Marker. It is claimed here that the Reflexive Marker constitutes a system in Russian and the relation between the reflexive and neighbor verbs constitutes a cross-paradigmatic relation. Furthermore, the relation between the reflexive and the neighbor verb is argued to be of symbolic connectivity rather than directionality. Effectively, the relation holding between particular instantiations can vary. The theoretical basis of the present study builds on this assumption. Several new variables are examined in order to systematically model variability of this symbolic connectivity, specifically the degree and strength of connectivity between items. In usage-based models, the lexicon does not constitute an unstructured list of items. Instead, items are assumed to be interconnected in a network. This interconnectedness is defined as Neighborhood in this study. Additionally, each verb carves its own niche within the Neighborhood and this interconnectedness is modeled through rhyme verbs constituting the degree of connectivity of a particular verb in the lexicon. The second component of the degree of connectivity concerns the status of a particular verb relative to its rhyme verbs. The connectivity within the neighborhood of a particular verb varies and this variability is quantified by using the Levenshtein distance. The second property of the lexical network is the strength of connectivity between items. Frequency of use has been one of the primary variables in functional linguistics used to probe this. In addition, a new variable called Constructional Entropy is introduced in this study building on information theory. It is a quantification of the amount of information carried by a particular reflexive verb in one or more argument constructions. The results of the lexical connectivity indicate that the reflexive verbs have statistically greater neighborhood distances than the neighbor verbs. This distributional property can be used to motivate the traditional observation that the reflexive verbs tend to have idiosyncratic properties. A set of argument constructions, generalizations over usage patterns, are proposed for the reflexive verbs in this study. In addition to the variables associated with the lexical connectivity, a number of variables proposed in the literature are explored and used as predictors in the model. The second part of this study introduces the use of a machine learning algorithm called Random Forests. The performance of the model indicates that it is capable, up to a degree, of disambiguating the proposed argument construction types of the Russian Reflexive Marker. Additionally, a global ranking of the predictors used in the model is offered. Finally, most construction grammars assume that argument construction form a network structure. A new method is proposed that establishes generalization over the argument constructions referred to as Linking Construction. In sum, this study explores the structural properties of the Russian Reflexive Marker and a new model is set forth that can accommodate both the traditional pairs and potential deviations from it in a principled manner.

Probabilistic contact preferences in protein-ligand and protein-protein complexes

Modeller för intermolekulär växelvärkan utnyttjas brett inom biologin. Analys av kontakter mellan proteiner och läkemedelsforskning representerar typiska tillämpningsområden för dylika modeller. En modell som beskriver sådana molekylära växelverkningar kan utformas med hjälp av biofysisk teori, vilket tenderar att resultera i ytterst tung beräkningsbörda även för enkla tillämpningar. Ett alternativt sätt att formulera modeller är att utnyttja stora databaser som innehåller strukturmätningar gjorda med hjälp av till exempel röntgendiffraktion. Då man använder sig av empiriska mätdata direkt, möjliggör en statistisk modell att osäkerheten och inexaktheten i datat tas till hänsyn på ett adekvat sätt, samtidigt som beräkningsbördan håller sig på en rimligare nivå jämfört med kvantmekaniska metoder som i princip borde ge de optimala resultaten. I avhandlingen utvecklades en 3D modell för numerisk undersökning av intermolekulär växelverkan baserad på Bayesiansk statistik. Modellens syfte är att åstadkomma prognoser för det hurdana eller vilka molekylstrukturer prefereras i en given kontext, d.v.s. är mer sannolika inom ramen för interaktion. Modellen testades i essentiella molekyläromgivningar - en liten molekyl vid sin bindningsplats hos ett protein och en gränsyta mellan proteinerna i ett komplex. De erhållna numeriska resultaten motsvarar väl experimentella resultat som tidigare rapporterats i litteraturen, exempelvis kvalitativa bindningsaffiniteter och kemisk kännedom av vissa aminosyrors rumsliga förmågor att utgöra bindningar. I avhandlingen gjordes ytterligare preliminära tester av den statistiska ansatsen för modellering av den centrala molekylära strukturella anpassningsbarheten. I praktiken är den utvecklade modellen ämnad som ett led i en mer omfattande analysmetod, så som en s.k. farmakofor modell. Molekyylivuorovaikutusten mallintamista hyödynnetään laajasti biologisten kysymysten tarkastelussa. Tyypillisiä esimerkkejä sovelluskohteista ovat proteiinien väliset kontaktit ja lääkesuunnittelu. Vuorovaikutuksia kuvaavan mallin lähtökohta voi olla molekyyleihin liittyvä teoria, jolloin soveltamiseen liittyvä laskenta saattaa olla erityisen raskasta, tai suuri havaintojoukko joka on saatu aikaan esimerkiksi mittaamalla rakenteita röntgendiffraktio menetelmällä. Tilastollinen malli mahdollistaa havaintoaineistossa olevan epätarkkuuden ja epävarmuuden huomioimisen, samalla pitäen laskennallisen kuorman pienempänä verrattuna periaatteessa parhaan tuloksen antavaan kvanttimekaaniseen mallinnukseen. Väitöstyössä kehitettiin bayesiläiseen tilastotieteeseen perustuva 3D malli molekyylien välisten vuorovaikutusten laskennalliseen tarkasteluun. Mallin tehtävä on tuottaa ennusteita sen suhteen, minkä tai millaisten molekyylirakenteiden väliset kompleksit ovat etusijalla, toisin sanoen todennäköisempiä, vuorovaikutustilanteessa. Työssä kehitetyn menetelmän toimivuutta testattiin käyttötarkoituksen suhteen olennaisissa molekyyliympäristöissä - pieni molekyyli sitoutumiskohdassaan proteiinissa sekä rajapinta kahden proteiinin välilllä proteiinikompleksissa. Saadut laskennalliset tulokset vastasivat hyvin vertailuun käytettyjä kirjallisuudesta saatuja kokeellisia tuloksia, kuten laadullisia sitoutumisaffiniteetteja, sekä kemiallista tietoa esimerkiksi tiettyjen aminohappojen avaruudellisesta sidoksenmuodostuksesta. Väitöstyössä myös alustavasti testattiin tilastollista lähestymistapaa tärkeän molekyylien rakenteellisen mukautuvuuden mallintamiseen. Käytännössä malli on tarkoitettu osaksi jotakin laajempaa analyysimenetelmää, kuten farmakoforimallia.

Probabilistic position and orientation estimation of rigid objects using an RGB-D sensor

The recent emergence of low-cost RGB-D sensors has brought new opportunities for robotics by providing affordable devices that can provide synchronized images with both color and depth information. In this thesis, recent work on pose estimation utilizing RGBD sensors is reviewed. Also, a pose recognition system for rigid objects using RGB-D data is implemented. The implementation uses half-edge primitives extracted from the RGB-D images for pose estimation. The system is based on the probabilistic object representation framework by Detry et al., which utilizes Nonparametric Belief Propagation for pose inference. Experiments are performed on household objects to evaluate the performance and robustness of the system.