Medical image registration based on random line sampling

One of the key aspects in 3D-image registration is the computation of the joint intensity histogram. We propose a new approach to compute this histogram using uniformly distributed random lines to sample stochastically the overlapping volume between two 3D-images. The intensity values are captured from the lines at evenly spaced positions, taking an initial random offset different for each line. This method provides us with an accurate, robust and fast mutual information-based registration. The interpolation effects are drastically reduced, due to the stochastic nature of the line generation, and the alignment process is also accelerated. The results obtained show a better performance of the introduced method than the classic computation of the joint histogram

Les característiques de les víctimes a Catalunya i a altres països industrialitzats. Un estudi multivariant

La recerca té l’objectiu de conèixer quines són les variables que expliquen i prediuen la victimització mitjançant anàlisis de regressió logística. L’estudi s’ha dut a terme a partir d’una mostra de 39.517 registres procedents de 17 països industrialitzats (inclosa Catalunya), que pertanyen a 17 enquestes sobre la victimització l’any 1999, totes elles realitzades amb els mateixos paràmetres metodològics. Les variables dependents (o tipus de victimització) que s’estudien són: robatori del/en el cotxe, robatori o temptativa de robatori en el domicili, delictes menors, delictes contra la propietat, delictes amb violència, agressions sexuals i delictes de contacte. Les variables independents són: país, hàbits de sortida nocturns, edat, nombre d’habitants de la ciutat o municipi, ocupació, anys d’estudi, ingressos, estat civil i sexe. Algunes conclusions són: (1) les variables país i edat són les que amb més força expliquen la victimització; (2)quant a les agressions sexuals, la variable que més explica la victimització és l’estat civil, seguit de l’edat i el país; (3) la variable país està present en totes i cada una de les equacions obtingudes de les regressions logístiques, la qual cosa vol dir que en tots els casos explica la victimització i, és més, té la capacitat de predir-la; (4) l’estat civil i nombre d’habitants estan presents en totes les equacions de regressió logística llevat de la referida als delictes contra els cotxes; (5) l’edat està present en 6 de les 8 equacions de regressió logística, no es presenta en els delictes contra els domicilis ni en els delictes amb violència, per la qual cosa no és útil a l’hora de predir-ne la victimització; (5) pel que fa al país, el fet de viure a Catalunya és un factor de protecció envers el delicte, llevat dels fets contra els vehicles.

An Interval Intelligent-based Approach for Fault Detection and Modelling

Not considered in the analytical model of the plant, uncertainties always dramatically decrease the performance of the fault detection task in the practice. To cope better with this prevalent problem, in this paper we develop a methodology using Modal Interval Analysis which takes into account those uncertainties in the plant model. A fault detection method is developed based on this model which is quite robust to uncertainty and results in no false alarm. As soon as a fault is detected, an ANFIS model is trained in online to capture the major behavior of the occurred fault which can be used for fault accommodation. The simulation results understandably demonstrate the capability of the proposed method for accomplishing both tasks appropriately

Dynamic diagnosis based on interval analytical redundancy relations and signs of the symptoms

A model-based approach for fault diagnosis is proposed, where the fault detection is based on checking the consistencyof the Analytical Redundancy Relations (ARRs) using an interval tool. The tool takes into account the uncertainty in theparameters and the measurements using intervals. Faults are explicitly included in the model, which allows for the exploitation of additional information. This information is obtained from partial derivatives computed from the ARRs. The signs in the residuals are used to prune the candidate space when performing the fault diagnosis task. The method is illustrated using a two-tank example, in which these aspects are shown to have an impact on the diagnosis and fault discrimination, since the proposed method goes beyond the structural methods

Understanding the two-component sensing System of virulent bacteria

Report for the scientific sojourn carried out at the l’ Institute for Computational Molecular Science of the Temple University, United States, from 2010 to 2012. Two-component systems (TCS) are used by pathogenic bacteria to sense the environment within a host and activate mechanisms related to virulence and antimicrobial resistance. A prototypical example is the PhoQ/PhoP system, which is the major regulator of virulence in Salmonella. Hence, PhoQ is an attractive target for the design of new antibiotics against foodborne diseases. Inhibition of the PhoQ-mediated bacterial virulence does not result in growth inhibition, presenting less selective pressure for the generation of antibiotic resistance. Moreover, PhoQ is a histidine kinase (HK) and it is absent in animals. Nevertheless, the design of satisfactory HK inhibitors has been proven to be a challenge. To compete with the intracellular ATP concentrations, the affinity of a HK inhibidor must be in the micromolar-nanomolar range, whereas the current lead compounds have at best millimolar affinities. Moreover, the drug selectivity depends on the conformation of a highly variable loop, referred to as the “ATP-lid, which is difficult to study by X-Ray crystallography due to its flexibility. I have investigated the binding of different HK inhibitors to PhoQ. In particular, all-atom molecular dynamics simulations have been combined with enhanced sampling techniques in order to provide structural and dynamic information of the conformation of the ATP-lid. Transient interactions between these drugs and the ATP-lid have been identified and the free energy of the different binding modes has been estimated. The results obtained pinpoint the importance of protein flexibility in the HK-inhibitor binding, and constitute a first step in developing more potent and selective drugs. The computational resources of the hosting institution as well as the experience of the members of the group in drug binding and free energy methods have been crucial to carry out this work.

Irregular to regular sampling, denoising and deconvolution

We propose a restoration algorithm for band limited images that considers irregular(perturbed) sampling, denoising, and deconvolution. We explore the application of a family ofregularizers that allow to control the spectral behavior of the solution combined with the irregular toregular sampling algorithms proposed by H.G. Feichtinger, K. Gr¨ochenig, M. Rauth and T. Strohmer.Moreover, the constraints given by the image acquisition model are incorporated as a set of localconstraints. And the analysis of such constraints leads to an early stopping rule meant to improvethe speed of the algorithm. Finally we present experiments focused on the restoration of satellite images, where the micro-vibrations are responsible of the type of distortions we are considering here. We will compare results of the proposed method with previous methods and show an extension tozoom.

Life-Cycle Effects on Household Expenditures: A Latent-Variable Approach

Using data from the Spanish household budget survey, we investigate life- cycle effects on several product expenditures. A latent-variable model approach is adopted to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the measurement error in income. The proposed methodology also takes care of the case in which product expenditures exhibit a pattern of infrequent purchases. Multiple-group analysis is used to assess the variation of key parameters of the model across various household life-cycle typologies. The analysis discloses significant life-cycle effects on the mean levels of expenditures; it also detects significant life-cycle effects on the way expenditures are affected by income and family size. Asymptotic robust methods are used to account for possible non-normality of the data.

Lyfe-cycle effects on household expenditures: A latent-variable approach

Using data from the Spanish household budget survey, we investigate life-cycle effects on several product expenditures. A latent-variable model approach is adopted to evaluate the impact of income on expenditures, controlling for the number of members in the family. Two latent factors underlying repeated measures of monetary and non-monetary income are used as explanatory variables in the expenditure regression equations, thus avoiding possible bias associated to the measurement error in income. The proposed methodology also takes care of the case in which product expenditures exhibit a pattern of infrequent purchases. Multiple-group analysis is used to assess the variation of key parameters of the model across various household life-cycle typologies. The analysis discloses significant life-cycle effects on the mean levels of expenditures; it also detects significant life-cycle effects on the way expenditures are affected by income and family size. Asymptotic robust methods are used to account for possible non-normality of the data.

Canonical correspondence analysis in social science research

The use of simple and multiple correspondence analysis is well-established in socialscience research for understanding relationships between two or more categorical variables.By contrast, canonical correspondence analysis, which is a correspondence analysis with linearrestrictions on the solution, has become one of the most popular multivariate techniques inecological research. Multivariate ecological data typically consist of frequencies of observedspecies across a set of sampling locations, as well as a set of observed environmental variablesat the same locations. In this context the principal dimensions of the biological variables aresought in a space that is constrained to be related to the environmental variables. Thisrestricted form of correspondence analysis has many uses in social science research as well,as is demonstrated in this paper. We first illustrate the result that canonical correspondenceanalysis of an indicator matrix, restricted to be related an external categorical variable, reducesto a simple correspondence analysis of a set of concatenated (or stacked ) tables. Then weshow how canonical correspondence analysis can be used to focus on, or partial out, aparticular set of response categories in sample survey data. For example, the method can beused to partial out the influence of missing responses, which usually dominate the results of amultiple correspondence analysis.

Variable Kernel estimates: On the impossibility of tuning the parameters

For the standard kernel density estimate, it is known that one can tune the bandwidth such that the expected L1 error is within a constant factor of the optimal L1 error (obtained when one is allowed to choose the bandwidth with knowledge of the density). In this paper, we pose the same problem for variable bandwidth kernel estimates where the bandwidths are allowed to depend upon the location. We show in particular that for positive kernels on the real line, for any data-based bandwidth, there exists a densityfor which the ratio of expected L1 error over optimal L1 error tends to infinity. Thus, the problem of tuning the variable bandwidth in an optimal manner is ``too hard''. Moreover, from the class of counterexamples exhibited in the paper, it appears thatplacing conditions on the densities (monotonicity, convexity, smoothness) does not help.

Testing financing constraints on firm investment using variable capital

We consider a dynamic multifactor model of investment with financing imperfections,adjustment costs and fixed and variable capital. We use the model to derive a test offinancing constraints based on a reduced form variable capital equation. Simulation resultsshow that this test correctly identifies financially constrained firms even when the estimationof firms investment opportunities is very noisy. In addition, the test is well specified inthe presence of both concave and convex adjustment costs of fixed capital. We confirmempirically the validity of this test on a sample of small Italian manufacturing companies.

Biased quantitative measurement of interval ordered homothetic preferences

We represent interval ordered homothetic preferences with a quantitative homothetic utility function and a multiplicative bias. When preferences are weakly ordered (i.e. when indifference is transitive), such a bias equals 1. When indifference is intransitive, the biasing factor is a positive function smaller than 1 and measures a threshold of indifference. We show that the bias is constant if and only if preferences are semiordered, and we identify conditions ensuring a linear utility function. We illustrate our approach with indifference sets on a two dimensional commodity space.

Analysis of matched matrices

We consider the joint visualization of two matrices which have common rowsand columns, for example multivariate data observed at two time pointsor split accord-ing to a dichotomous variable. Methods of interest includeprincipal components analysis for interval-scaled data, or correspondenceanalysis for frequency data or ratio-scaled variables on commensuratescales. A simple result in matrix algebra shows that by setting up thematrices in a particular block format, matrix sum and difference componentscan be visualized. The case when we have more than two matrices is alsodiscussed and the methodology is applied to data from the InternationalSocial Survey Program.