Biometrics & [and] Security:Combining Fingerprints, Smart Cards and Cryptography

Since the beginning of this brand new century, and especially since the 2001 Sept 11 events in the U.S, several biometric technologies are considered mature enough to be a new tool for security. Generally associated to a personal device for privacy protection, biometric references are stored in secured electronic devices such as smart cards, and systems are using cryptographic tools to communicate with the smart card and securely exchange biometric data. After a general introduction about biometrics, smart cards and cryptography, a second part will introduce our work with fake finger attacks on fingerprint sensors and tests done with different materials. The third part will present our approach for a lightweight fingerprint recognition algorithm for smart cards. The fourth part will detail security protocols used in different applications such as Personal Identity Verification cards. We will discuss our implementation such as the one we developed for the NIST to be used in PIV smart cards. Finally, a fifth part will address Cryptography-Biometrics interaction. We will highlight the antagonism between Cryptography – determinism, stable data – and Biometrics – statistical, error-prone –. Then we will present our application of challenge-response protocol to biometric data for easing the fingerprint recognition process

Estimating Fisher Information Matrix in Latent Variable Models based on the Score Function

The Fisher information matrix (FIM) is a key quantity in statistics as it is required for example for evaluating asymptotic precisions of parameter estimates, for computing test statistics or asymptotic distributions in statistical testing, for evaluating post model selection inference results or optimality criteria in experimental designs. However its exact computation is often not trivial. In particular in many latent variable models, it is intricated due to the presence of unobserved variables. Therefore the observed FIM is usually considered in this context to estimate the FIM. Several methods have been proposed to approximate the observed FIM when it can not be evaluated analytically. Among the most frequently used approaches are Monte-Carlo methods or iterative algorithms derived from the missing information principle. All these methods require to compute second derivatives of the complete data log-likelihood which leads to some disadvantages from a computational point of view. In this paper, we present a new approach to estimate the FIM in latent variable model. The advantage of our method is that only the first derivatives of the log-likelihood is needed, contrary to other approaches based on the observed FIM. Indeed we consider the empirical estimate of the covariance matrix of the score. We prove that this estimate of the Fisher information matrix is unbiased, consistent and asymptotically Gaussian. Moreover we highlight that none of both estimates is better than the other in terms of asymptotic covariance matrix. When the proposed estimate can not be directly analytically evaluated, we present a stochastic approximation estimation algorithm to compute it. This algorithm provides this estimate of the FIM as a by-product of the parameter estimates. We emphasize that the proposed algorithm only requires to compute the first derivatives of the complete data log-likelihood with respect to the parameters. We prove that the estimation algorithm is consistent and asymptotically Gaussian when the number of iterations goes to infinity. We evaluate the finite sample size properties of the proposed estimate and of the observed FIM through simulation studies in linear mixed effects models and mixture models. We also investigate the convergence properties of the estimation algorithm in non linear mixed effects models. We compare the performances of the proposed algorithm to those of other existing methods

A cortical surface-based meta-analysis of human reasoning

Recent advances in neuroimaging have augmented numerous findings in the human reasoning process but have yielded varying results. One possibility for this inconsistency is that reasoning is such an intricate cognitive process, involving attention, memory, executive functions, symbolic processing, and fluid intelligence, whereby various brain regions are inevitably implicated in orchestrating the process. Therefore, researchers have used meta-analyses for a better understanding of neural mechanisms of reasoning. However, previous meta-analysis techniques include weaknesses such as an inadequate representation of the cortical surface’s highly folded geometry. Accordingly, we developed a new meta-analysis method called Bayesian meta-analysis of the cortical surface (BMACS). BMACS offers a fast, accurate, and accessible inference of the spatial patterns of cognitive processes from peak brain activations across studies by applying spatial point processes to the cortical surface. Using BMACS, we found that the common pattern of activations from inductive and deductive reasoning was colocalized with the multiple-demand system, indicating that reasoning is a high-level convergence of complex cognitive processes. We hope surface-based meta-analysis will be facilitated by BMACS, bringing more profound knowledge of various cognitive processes

The critical role of natural forest as refugium for generalist species in oil palm-dominated landscapes

In Borneo, oil palm plantations have replaced much of natural resources, where generalist species tend to be the principal beneficiaries, due to the abundant food provided by oil palm plantations. Here, we analyse the distribution of the Asian water monitor lizard (Varanus salvator) population within an oil palm-dominated landscape in the Kinabatangan floodplain, Malaysian Borneo. By using mark-recapture methods we estimated its population size, survival, and growth in forest and plantation habitats. We compared body measurements (i.e. body weight and body length) of individuals living in forest and oil palm habitats as proxy for the population’s health status, and used general least squares estimation models to evaluate its response to highly fragmented landscapes in the absence of intensive hunting pressures. Contrary to previous studies, the abundance of lizards was higher in the forest than in oil palm plantations. Recruitment rates were also higher in the forest, suggesting that these areas may function as a source of new individuals into the landscape. While there were no morphometric differences among plantation sites, we found significant differences among forested areas, where larger lizards were found inhabiting forest adjacent to oil palm plantations. Although abundant in food resources, the limited availability of refugia in oil palm plantations may intensify intra-specific encounters and competition, altering the body size distribution in plantation populations, contrary to what happens in the forest. We conclude that large patches of forest, around and within oil palm plantations, are essential for the dynamics of the monitor lizard population in the Kinabatangan floodplain, as well as a potential source of individuals to the landscape. We recommend assessing this effect in other generalist species, as well as the impact on the prey communities, especially to reinforce the establishment of buffer zones and corridors as a conservation strategy within plantations

Epigenetic hotspots in cancer

DNA methylation is one of the most studied epigenetic events. In normal cells, it assures the regulation of gene expression without changing the genetic code. However, alterations in DNA methylation are now widely recognized as a contributing factor in tumorigenesis. The bulk of research done in cancer epigenetics focuses on one of two events: promoter hypermethylation and global hypomethylation. Advances in the understanding of how DNA methylation shapes the chromatin’s organization and how the later affects gene expression have been made. Less is known about how DNA methylation affects genes not only locally but also at a distance. We hypothesized that during tumorigenesis specific genomic regions are more susceptible to DNA methylation (epi-hotspots) and other are resistance to DNA methylation changes (epi-blackholes). We also hypothesized that these regions might persist in tumor cells by exerting some selective pressure in the primary tumor clones. By performing a pan-cancer analysis comparing normal to stage-I primary stage-I primary tumor samples gathered from TCGA consortium, we observed that both epi-hotspots and epi-blackholes occurred in all of the analyzed cancer cohorts. Furthermore, generally, epi-hotspots were able to predict gene expression alterations during tumorigenesis, and epi-blackholes were predictors of maintenance of gene expression during tumor initiation, which was in accordance with our hypothesis. We also found that several epi-hotspots and epi-blackholes are predictors of survival in stage-III tumor patients, which may provide potential study targets for candidate prognostic biomarkers. In summary, this study provides new evidence that regional methylation patterns potentially might exert selective pressure in tumor initiation by influencing genome-wide gene expression, and that these traits might be used to develop novel diagnostic and prognostic candidate biomarkers.O cancro é um conjunto heterogéneo de várias doenças que são caracterizadas por uma taxa de crescimento e divisão celular anormais. Durante o processo tumorigénico, as células tumorais vão sucessivamente adquirindo alterações genéticas e epigenéticas, o que leva a uma continua seleção de subclones tumorais. Durante esta evolução tumoral, as células sofrem alterações a nível da metilação de DNA que, tal como as mutações, podem ser propagadas para as células-filha. Estes tipos de alterações contribuem não só para o início do processo tumorigénico, como também para o seu continuo desenvolvimento, sem alterarem a sequência de DNA. Alterações a nível da metilação de DNA participam no processo tumorigénico influenciando diretamente a expressão génica, e afetando a conformação da cromatina que, por sua vez, está relacionada com a toda a expressão génica na célula. Para serem ativamente expressos, os genes têm de estar acessíveis a fatores regulatórios. Por outro lado, genes que têm a sua expressão silenciada tendem a estar compactados na cromatina, de forma a estarem inacessíveis às proteínas responsáveis pela sua transcrição. Alterações a nível da conformação da cromatina podem promover a tumorigénese pelo facto de mudarem a acessibilidade de certas regiões de DNA, assim alterando o padrão global de expressão génica da célula. A maioria dos tumores apresenta um padrão de metilação de DNA global anormal. Uma vez que estes mesmos padrões têm um papel importante na modulação da acessibilidade da cromatina, que por sua vez tem impacto no fenótipo da célula, surgiu a pergunta biológica: “ durante o processo de iniciação tumoral, serão certas regiões genómicas mais suscetíveis a alterações a nível de metilação de DNA?”. E no caso da resposta a esta pergunta ser afirmativa, surge ainda a questão: “Será que estas regiões estão associadas à alteração de padrões de expressão génica nas células tumorais?”. No caso de existirem zonas genómicas de maior suscetibilidade a alterações de metilação de DNA, e estas estarem associadas a alterações a nível de expressão génica, surge ainda a hipótese que estas regiões poderão ter valor de prognóstico em pacientes com doença avançada. Numa tentativa de respondermos a estas questões, realizámos uma análise a doze tipos de cancro (adenocarcinoma do colon, adenocarcinoma do pâncreas, carcinoma da mama, colangiocarcinoma, carcinoma do esófago, cancro da cabeça e pescoço, carcinoma de células renais de células claras, carcinoma de células renais papilar, carcinoma hepatocelular, adenocarcinoma do pulmão, carcinoma do pulmão de células escamosas, e carcinoma da tireoide), onde comparámos dados de metilação entre amostras de tecido normal com amostras de tecido tumoral em estádio I. Por forma a se encontrarem regiões de maior suscetibilidade a alterações de metilação de DNA, aplicámos dois algoritmos de identificação de regiões diferencialmente metiladas e intercetámos os resultados. As regiões genómicas identificadas por ambos os métodos foram designadas epi-hotspots. De modo a aferir se os epi-hotspots estavam associados a alterações de expressão génica no processo de iniciação tumoral, efetuou-se ainda uma análise de regressão linear múltipla entre cada gene diferencialmente expresso em estádio I e cada epi-hotspot. Os genes diferencialmente expressos, cuja variação entre tecido normal e tumor estádio I podia ser explicada por epi-hotspots, foram sujeitos a um estudo de ontologia genética, por forma a se compreender se estes genes potencialmente epigeneticamente regulados enriqueciam algum processo celular. Este processo foi também repetido por forma a se identificarem regiões de baixa suscetibilidade a alterações de metilação de DNA, que designámos epi-blackholes. De modo a testar se estas regiões estavam associadas a genes não-diferencialmente expressos, realizou-se ainda uma análise de regressão linear múltipla entre cada gene não-diferencialmente expresso em estádio I e cada epi-blackhole. Estudou-se ainda o grau de semelhança entre os doze tipos de cancro aqui analisádos relativamente à presença de epi-hotspots e epi-blackholes por meio de uma análise de agrupamento hierárquico. Finalmente, examinou-se o potencial de prognóstico de cada epi-hotspot e cada epi-blackhole em pacientes tumorais de estádio III, fazendo uso de uma análise baseada em regressão multivariada de Cox. Os nossos resultados indicam que, apesar de existirem pequenas semelhanças, o número e localização de epi-hospots e epi-blackholes é característico de cada tipo de cancro, o que sugere que tanto a alteração como a manutenção dos padrões de metilação nestas regiões dependem da célula de origem. Verificou-se ainda que os padrões de metilação em epi-hotspots estavam associados a padrões alterados de expressão génica, em amostras de tecido tumoral em estádio I. Este resultado suporta a hipótese de que alterações regionais de metilação de DNA podem conferir vantagem seletiva na evolução clonal do tumor por influenciarem a expressão génica. De uma forma geral, os genes cuja variação em iniciação tumoral era explicada pela variação da metilação de epi-hotspots enriquecem processos celulares de forma distinta nos diferentes tipos de cancro analisados. Por outro lado, padrões de metilação em epi-blackholes estavam associados à manutenção dos padrões de expressão génica, em amostras de tecido tumoral em estádio I, o que sugere que a conservação de padrões de metilação em certas regiões do DNA pode também ser relevante para a tumorigénese. Observou-se também que em dois terços dos tipos de cancro analisados, a metilação das CpGs de pelo menos um epi-hotspot ou epi-blackhole foi capaz de dividir os pacientes oncológicos de estádio III em dois grupos com padrões de sobrevida distintos, independentemente da idade dos pacientes. Apesar de nem todas as regiões aqui identificadas terem demonstrado potencial de prognóstico, este estudo sugere que os padrões de metilação de DNA em epi-hotspots e epi-blackholes podem ser potênciais candidatos para biomarcadores de prognóstico em pacientes oncológicos de estádio III. Em suma, este trabalho demonstra que, durante o processo de iniciação tumoral, há uma alteração do padrão de metilação de DNA de certas regiões genómicas (epi-hotspots). Por outro lado, parece também haver uma conservação do padrão de metilação de DNA de outras regiões (epi-blackholes). Além disso, parece existir uma associação entre a variação da expressão génica na iniciação tumoral e a metilação dos epi-hotspots. A manutenção do padrão de metilação dos epi-blackholes identificados parece estar associada com a ausência de variação de expressão de determinados genes. Este estudo revela ainda que epi-hotspots e epi-blackholes podem ter também exercer uma pressão seletiva no tumor, já que para além de estarem associados à expressão génica são ainda capazes de prever o prognóstico de pacientes oncológicos em estádio III

Spatio temporal modeling of species distribution

The aim of this thesis is study spatial distribution of different groups from different perspectives and to analyse the different approaches to this problem. We move away from the classical approach, commonly used by ecologists, to more complex solutions, already applied in several disciplines. We are focused in applying advanced modelling techniques in order to understand species distribution and species behaviour and the relationships between them and environmental factors and have used first the most common models applied in ecology to move then to more advanced and complex perspectives. From a general perspective and comparing the different models applied during the process, from MaxEnt to spatio-temporal models with INLA, we can affirm that the models that we have developed show better results that the already built. Also, it is difficult to compare between the different approaches, but the Bayesian approach shows more flexibility and also the inclusion of spatial field or the latent spatio-temporal process allows to include residuals as a proxy for unmeasured variables. Compared with additive models with thin plate splines, probably considered one of the greatest methods to analyse species distribution models working with presence-absence data, comparable to MaxEnt, CART and MARS, our results show a better fit and more flexibility in the design. As a natural process we have realised that the Bayesian approach could be a better solution or at least a different approach for consideration. The main advantage of the Bayesian model formulation is the computational ease in model fit and prediction compared to classical geostatistical methods. To do so, instead of MCMC we have used the novel integrated nested Laplace approximation approach through the Stochastic Partial Differential Equation (SPDE) approach. The SPDE approach can be easily implemented providing results in reasonable computing time (comparing with MCMC). We showed how SPDE is a useful tool in the analysis of species distribution. This modelling could be expanded to the spatio-temporal domain by incorporating an extra term for the temporal effect, using parametric or semiparametric constructions to reflect linear, nonlinear, autoregressive or more complex behaviours. We can conclude that spatial and spatio-temporal Bayesian models are a really interesting approach for the understanding of environmental dynamics, not only because of the possibility to develop and solve more complex problems but also for the easy understanding of the implementation processes.The aim of this thesis is study spatial distribution of different groups from different perspectives and to analyse the different approaches to this problem. We move away from the classical approach, commonly used by ecologists, to more complex solutions, already applied in several disciplines. We are focused in applying advanced modelling techniques in order to understand species distribution and species behaviour and the relationships between them and environmental factors and have used first the most common models applied in ecology to move then to more advanced and complex perspectives. From a general perspective and comparing the different models applied during the process, from MaxEnt to spatio-temporal models with INLA, we can affirm that the models that we have developed show better results that the already built. Also, it is difficult to compare between the different approaches, but the Bayesian approach shows more flexibility and also the inclusion of spatial field or the latent spatio-temporal process allows to include residuals as a proxy for unmeasured variables. Compared with additive models with thin plate splines, probably considered one of the greatest methods to analyse species distribution models working with presence-absence data, comparable to MaxEnt, CART and MARS, our results show a better fit and more flexibility in the design. As a natural process we have realised that the Bayesian approach could be a better solution or at least a different approach for consideration. The main advantage of the Bayesian model formulation is the computational ease in model fit and prediction compared to classical geostatistical methods. To do so, instead of MCMC we have used the novel integrated nested Laplace approximation approach through the Stochastic Partial Differential Equation (SPDE) approach. The SPDE approach can be easily implemented providing results in reasonable computing time (comparing with MCMC). We showed how SPDE is a useful tool in the analysis of species distribution. This modelling could be expanded to the spatio-temporal domain by incorporating an extra term for the temporal effect, using parametric or semiparametric constructions to reflect linear, nonlinear, autoregressive or more complex behaviours. We can conclude that spatial and spatio-temporal Bayesian models are a really interesting approach for the understanding of environmental dynamics, not only because of the possibility to develop and solve more complex problems but also for the easy understanding of the implementation processes

Investigating the transition from normal driving to safety-critical scenarios

Investigation of the correlation between factors associated with crash development has enabled the implementation of methods aiming to avert and control crash causation at various points within the crash sequence (Evans, 2006). Partitioning the crash sequence is important because intricated crash causation sequences can be deconstructed and effective prevention strategies can be suggested (Wu & Thor, 2015). Towards this purpose, Tingvall et al. (2009) documented the so-called integrated safety chain which described the change of crash risk on the basis of a developing sequence of events that led to a collision. This thesis examines the crash sequence development and thus, the transition from normal driving to safety critical scenarios. [Continues.