Resolución de problemas relacionados con el comportamiento y la salud mediante la aplicación de técnicas avanzadas de reconocimiento de patrones

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Psicología, Departamento de Psicología Social y Metodología. Fecha de lectura: 14 de mayo de 2016En esta tesis, se investigan los beneficios que se obtendrían al aplicar técnicas recientes de reconocimiento de patrones en la resolución de diversos problemas existentes en el área de la Psicología del comportamiento y de la salud. En las ciencias de la salud, se observa que estas técnicas son capaces de mejorar, en más de un 10%, la precisión o capacidad predictiva de las técnicas actuales de identificación de personas en riesgo de cometer un intento de suicidio. Además, se muestra que estas técnicas pueden ser utilizadas para construir escalas de propósito específico mediante la selección de los ítems más adecuados de escalas de propósito general. En el área de las ciencias del comportamiento, estas técnicas son capaces de identificar a los candidatos más adecuados en los procesos de selección de personal lo que permite reducir considerablemente los gastos de las empresas y facilitar su crecimiento. También se muestra que estas técnicas pueden ser utilizadas para verificar la identidad del participante cuando parte del proceso de selección se realiza online. Se presentan 6 artículos en los que se comparan diversas técnicas (regresión lineal, regresión lineal con selección de variables, regresión logística, análisis discriminante lineal y cuadrático, análisis discriminante de Fisher, boosting, árboles de decisión, Máquinas de Vectores Soporte, y el algoritmo Lars‐en) con distintos objetivos, resultando especialmente eficientes las dos últimas. Esta tesis también investiga la creación de predictores más discriminativos analizando el comportamiento facial y corporal del participante mientras realiza las pruebas. Para ello, en el séptimo artículo se estudia la posibilidad de combinar test psicométricos informatizados conductuales con técnicas de reconocimiento de patrones y visión por ordenador. Se observa que es posible encontrar determinados patrones de movimientos que mejoran las valoraciones de impulsividad. Estos hallazgos abren nuevas líneas de investigación que serán exploradas en los próximos años.This thesis analyses the benefits that can be obtained when pattern recognition techniques are utilized for solving several existing problems in the research areas of health and behavioral psychology. In the field of health, it is shown that these techniques are capable of improving, in more than 10%, the accuracy obtained by the current techniques in identifying suicidal behavior. Moreover, it is also exposed that these techniques can build accurate scales for specific purposes by selecting the most suitable items from general purpose scales. On the other hand, in the research area of behavioral psychology, it is observed that recent pattern recognition techniques are able to identify the most suitable candidates in the recruitment processes, which allows to reduce considerably the companies’ costs and, therefore, making easier their growth. In this area, it is also shown that these techniques can be used to verify the identity of the participant when some assessments during the recruitment process are realized online. This PhD thesis contains six articles in which several statistical and pattern recognition techniques are applied. The techniques are linear regression, stepwise linear regression, linear and quadratic discriminant analysis, Fisher discriminant analysis, boosting, decision trees, support vector machines and the Lars‐en algorithm. These techniques were used with different purposes. Support vector machine and the Lars‐en algorithm showed to obtain accurate results. This thesis also investigates the development of new discriminative predictors by analyzing the facial and corporal behavior of the examinee while performing some behavioral computerized tasks. This goal is achieved by combining pattern recognition and computerized psychometrical tests with computer vision techniques. Obtained results show that it is possible to find patterns related to corporal movement that can be used to improve the assessment of impulsivity. These findings open new future research lines that will explored in the following years

Directrices democráticas en la participación social

Este documento ofrece una aproximación a un cambio político desde el discurso de la Plataforma de Afectados por la Hipoteca del Garraf. Contextualizado bajo parámetros democráticos, los movimientos sociales y la acción colectiva son inherentes a la sistematización del orden político. Esta investigación nos aproxima a la realidad social que demanda un cambio político

Impact of laser attacks on the switching behavior of RRAM devices

The ubiquitous use of critical and private data in electronic format requires reliable and secure embedded systems for IoT devices. In this context, RRAMs (Resistive Random Access Memories) arises as a promising alternative to replace current memory technologies. However, their suitability for this kind of application, where the integrity of the data is crucial, is still under study. Among the different typology of attacks to recover information of secret data, laser attack is one of the most common due to its simplicity. Some preliminary works have already addressed the influence of laser tests on RRAM devices. Nevertheless, the results are not conclusive since different responses have been reported depending on the circuit under testing and the features of the test. In this paper, we have conducted laser tests on individual RRAM devices. For the set of experiments conducted, the devices did not show faulty behaviors. These results contribute to the characterization of RRAMs and, together with the rest of related works, are expected to pave the way for the development of suitable countermeasures against external attacks.Postprint (published version

Deep importance sampling based on regression for model inversion and emulation

Understanding systems by forward and inverse modeling is a recurrent topic of research in many domains of science and engineering. In this context, Monte Carlo methods have been widely used as powerful tools for numerical inference and optimization. They require the choice of a suitable proposal density that is crucial for their performance. For this reason, several adaptive importance sampling (AIS) schemes have been proposed in the literature. We here present an AIS framework called Regression-based Adaptive Deep Importance Sampling (RADIS). In RADIS, the key idea is the adaptive construction via regression of a non-parametric proposal density (i.e., an emulator), which mimics the posterior distribution and hence minimizes the mismatch between proposal and target densities. RADIS is based on a deep architecture of two (or more) nested IS schemes, in order to draw samples from the constructed emulator. The algorithm is highly efficient since employs the posterior approximation as proposal density, which can be improved adding more support points. As a consequence, RADIS asymptotically converges to an exact sampler under mild conditions. Additionally, the emulator produced by RADIS can be in turn used as a cheap surrogate model for further studies. We introduce two specific RADIS implementations that use Gaussian Processes (GPs) and Nearest Neighbors (NN) for constructing the emulator. Several numerical experiments and comparisons show the benefits of the proposed schemes. A real-world application in remote sensing model inversion and emulation confirms the validity of the approach.This work has been supported by Spanish government via grant FPU19/00815, by Agencia Estatal de Investigación AEI (project SPGRAPH, ref. num. PID2019-105032GB-I00), by the Found action by the Community of Madrid in the framework of the Multiannual Agreement with the Rey Juan Carlos University in line of action 1, “Encouragement of Young Phd students investigation”, Project Ref. F661 Acronym Mapping-UCI, and by the European Research Council (ERC) under the ERC Consolidator Grant 2014 project SEDAL (647423)

Patient No-Show Prediction: A Systematic Literature Review

Nowadays, across the most important problems faced by health centers are those caused by the existence of patients who do not attend their appointments. Among others, these patients cause loss of revenue to the health centers and increase the patients’ waiting list. In order to tackle these problems, several scheduling systems have been developed. Many of them require predicting whether a patient will show up for an appointment. However, obtaining these estimates accurately is currently a challenging problem. In this work, a systematic review of the literature on predicting patient no-shows is conducted aiming at establishing the current state-of-the-art. Based on a systematic review following the PRISMA methodology, 50 articles were found and analyzed. Of these articles, 82% were published in the last 10 years and the most used technique was logistic regression. In addition, there is significant growth in the size of the databases used to build the classifiers. An important finding is that only two studies achieved an accuracy higher than the show rate. Moreover, a single study attained an area under the curve greater than the 0.9 value. These facts indicate the difficulty of this problem and the need for further research

MCMC-driven importance samplers

Monte Carlo sampling methods are the standard procedure for approximating complicated integrals of multidimensional posterior distributions in Bayesian inference. In this work, we focus on the class of layered adaptive importance sampling algorithms, which is a family of adaptive importance samplers where Markov chain Monte Carlo algorithms are employed to drive an underlying multiple importance sampling scheme. The modular nature of the layered adaptive importance sampling scheme allows for different possible implementations, yielding a variety of different performances and computational costs. In this work, we propose different enhancements of the classical layered adaptive importance sampling setting in order to increase the efficiency and reduce the computational cost, of both upper and lower layers. The different variants address computational challenges arising in real-world applications, for instance with highly concentrated posterior distributions. Furthermore, we introduce different strategies for designing cheaper schemes, for instance, recycling samples generated in the upper layer and using them in the final estimators in the lower layer. Different numerical experiments show the benefits of the proposed schemes, comparing with benchmark methods presented in the literature, and in several challenging scenarios

cat.dt: An R package for fast construction of accurate Computerized Adaptive Tests using Decision Trees

This article introduces the cat.dt package for the creation of Computerized Adaptive Tests (CATs). Unlike existing packages, the cat.dt package represents the CAT in a Decision Tree (DT) structure. This allows building the test before its administration, ensuring that the creation time of the test is independent of the number of participants. Moreover, to accelerate the construction of the tree, the package controls its growth by joining nodes with similar estimations or distributions of the ability level and uses techniques such as message passing and pre-calculations. The constructed tree, as well as the estimation procedure, can be visualized using the graphical tools included in the package. An experiment designed to evaluate its performance shows that the cat.dt package drastically reduces computational time in the creation of CATs without compromising accuracy.This article has been funded by the Spanish National Project No. RTI2018-101857-B-I00

Adaptive posterior distributions for covariance matrix learning in Bayesian inversion problems for multioutput signals

In this work, we propose an adaptive importance sampling (AIS) scheme for multivariate Bayesian inversion problems, which is based in two main ideas: the inference procedure is divided in two parts and the variables of interest are split in two blocks. We assume that the observations are generated from a complex multivariate non-linear function perturbed by correlated Gaussian noise. We estimate both the unknown parameters of the multivariate non-linear model and the covariance matrix of the noise. In the first part of the proposed inference scheme, a novel AIS technique called adaptive target AIS (ATAIS) is designed, which alternates iteratively between an IS technique over the parameters of the non-linear model and a frequentist approach for the covariance matrix of the noise. In the second part of the proposed inference scheme, a prior density over the covariance matrix is considered and the cloud of samples obtained by ATAIS are recycled and re-weighted for obtaining a complete Bayesian study over the model parameters and covariance matrix. Two numerical examples are presented that show the benefits of the proposed approach

Computerized adaptive test and decision trees: A unifying approach

In the last few years, several articles have proposed decision trees (DTs) as an alternative to computerized adapted tests (CATs). These works have focused on showing the differences between the two methods with the aim of identifying the advantages of each of them and thus determining when it is preferable to use one method or another. In this article, Tree-CAT, a new technique for building CATs is presented. Unlike the existing work, Tree-CAT exploits the similarities between CATs and DTs. This technique allows the creation of CATs that minimise the mean square error in the estimation of the examinee’s ability level, and controls the item’s exposure rate. The decision tree is sequentially built by means of an innovative algorithmic procedure that selects the items associated with each of the tree branches by solving a linear program. In addition, our work presents further advantages over alternative item selection techniques with exposure control, such as instant item selection or simultaneous administration of the test to an unlimited number of participants. These advantages allow accurate on-line CATs to be implemented even when the item selection method is computationally costly.Numerical experiments were conducted in Uranus, a supercomputer cluster located at Universidad Carlos III de Madrid and jointly funded by EU-FEDER funds and by the Spanish Government via the National Projects No. UNC313-4E-2361, No. ENE2009-12213- C03-03, No. ENE2012-33219, No. ENE2012-31753 and No. ENE2015-68265-P

Clustering and graph mining techniques for classification of complex structural variations in cancer genomes

For many years, a major question in cancer genomics has been the identification of those variations that can have a functional role in cancer, and distinguish from the majority of genomic changes that have no functional consequences. This is particularly challenging when considering complex chromosomal rearrangements, often composed of multiple DNA breaks, resulting in difficulties in classifying and interpreting them functionally. Despite recent efforts towards classifying structural variants (SVs), more robust statistical frames are needed to better classify these variants and isolate those that derive from specific molecular mechanisms. We present a new statistical approach to analyze SVs patterns from 2392 tumor samples from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium and identify significant recurrence, which can inform relevant mechanisms involved in the biology of tumors. The method is based on recursive KDE clustering of 152,926 SVs, randomization methods, graph mining techniques and statistical measures. The proposed methodology was able not only to identify complex patterns across different cancer types but also to prove them as not random occurrences. Furthermore, a new class of pattern that was not previously described has been identified.Among others, this study has been supported by projects: SAF2017-89450-R (TransTumVar) and PID2020-119797RB-100 (BenchSV) from Science and Innovation Spanish Minstry. It has also been supported by the Spanish Goverment (contract PID2019-107255GB), Generalitat de Catalunya (contract 2014-SGR-1051) and Universitat Politècnica de Catalunya (45-FPIUPC2018).Peer ReviewedPostprint (published version