Bayesian functional linear regression with sparse step functions

The functional linear regression model is a common tool to determine the relationship between a scalar outcome and a functional predictor seen as a function of time. This paper focuses on the Bayesian estimation of the support of the coefficient function. To this aim we propose a parsimonious and adaptive decomposition of the coefficient function as a step function, and a model including a prior distribution that we name Bayesian functional Linear regression with Sparse Step functions (Bliss). The aim of the method is to recover areas of time which influences the most the outcome. A Bayes estimator of the support is built with a specific loss function, as well as two Bayes estimators of the coefficient function, a first one which is smooth and a second one which is a step function. The performance of the proposed methodology is analysed on various synthetic datasets and is illustrated on a black P\'erigord truffle dataset to study the influence of rainfall on the production

On the Leakage of Fuzzy Matchers

In a biometric recognition system, the matcher compares an old and a fresh template to decide if it is a match or not. Beyond the binary output (`yes' or `no'), more information is computed. This paper provides an in-depth analysis of information leakage during distance evaluation, with an emphasis on threshold-based obfuscated distance (\textit{i.e.}, Fuzzy Matcher). Leakage can occur due to a malware infection or the use of a weakly privacy-preserving matcher, exemplified by side channel attacks or partially obfuscated designs. We provide an exhaustive catalog of information leakage scenarios as well as their impacts on the security concerning data privacy. Each of the scenarios leads to generic attacks whose impacts are expressed in terms of computational costs, hence allowing the establishment of upper bounds on the security level.Comment: Minor correction

Near-collisions and their Impact on Biometric Security

Biometric recognition encompasses two operating modes. The first one is biometric identification which consists in determining the identity of an individual based on her biometrics and requires browsing the entire database (i.e., a 1:N search). The other one is biometric authentication which corresponds to verifying claimed biometrics of an individual (i.e., a 1:1 search) to authenticate her, or grant her access to some services. The matching process is based on the similarities between a fresh and an enrolled biometric template. Considering the case of binary templates, we investigate how a highly populated database yields near-collisions, impacting the security of both the operating modes. Insight into the security of binary templates is given by establishing a lower bound on the size of templates and an upper bound on the size of a template database depending on security parameters. We provide efficient algorithms for partitioning a leaked template database in order to improve the generation of a master-template-set that can impersonates any enrolled user and possibly some future users. Practical impacts of proposed algorithms are finally emphasized with experimental studies

The understanding of silicon sequential elutriation behaviour

During the fluidization of broad PSD (Particle Size Distribution) powders, elutriation can not be avoided, but has to be process controlled. Batch elutriations of continuous PSD powders were studied in a laboratory scale fluidized bed. The reference sample was metallurgical-grade silicon powder, with non-spherical shape. The smallest elutriable fines, namely superfines (\u3c10 µm) are entrained first. However, the largest elutriable particles (Ut ~ Ug) do not begin to be entrained simultaneously, but only after a delay that is as long as the time required for the superfines to leave the bed, thus inducing sequential elutriation (Figures 1). When no superfines were present, the entrainment was not delayed. This peculiar phenomenon was observed at all of the tested gas velocities (0.05-0.2 m/s). The superfines thus seem to strongly limit the elutriation of the larger elutriable particles. This sequential behaviour is particularly interesting to separate particles according to a small and narrow PSD (Figure 2). These phenomena are related to interparticle interactions within the bed and/or the freeboard and confirm the importance of polydispersity in the elutriation behavior. Thanks to the elutriation mathematical models developed in this study, the behavior that was thought to be explained by Silicon attrition can now be explained by sequential elutriation. Please click Additional Files below to see the full abstract

Traitement par nanofiltration d’effluents aqueux polysiloxaniques : influence des matrices salines

Les siloxanes sont utilisés dans tous les domaines de l’industrie. Depuis quelques années, certains pays ont mis en place des règlements encadrant les rejets aqueux siloxaniques. Dans une perspective de développement durable, les industriels sont à la recherche de technologies de traitement pour maîtriser leurs rejets. La nanofiltration paraît particulièrement adaptée pour les caractéristiques de séparation qu’elle autorise en regard de la taille des molécules visées. Cet article présente donc une étude de la nanofiltration pour le traitement des effluents siloxaniques. Les filtrations sont réalisées en mode frontal avec des effluents représentatifs des rejets industriels, caractérisés par une salinité élevée. L’octaméthylcyclotétrasiloxane (D4), molécule de base de la chimie des silicones, est choisie comme composé cible de l’étude. Des études récentes ont montré une sensibilité importante des performances de la nanofiltration à la présence de sels. Ainsi, cette étude porte une attention particulière à cette problématique. Les résultats montrent effectivement une variation des performances d’épuration en fonction de la salinité. Une diminution de l’abattement en carbone organique total (COT) et de la rétention en D4 est observée lorsque l’effluent est dilué. Les mêmes résultats ont été constatés lorsque la salinité de solutions diluées est augmentée

Machine Learning in Amyotrophic Lateral Sclerosis: Achievements, Pitfalls, and Future Directions

Background: Amyotrophic Lateral Sclerosis (ALS) is a relentlessly progressive neurodegenerative condition with limited therapeutic options at present. Survival from symptom onset ranges from 3 to 5 years depending on genetic, demographic, and phenotypic factors. Despite tireless research efforts, the core etiology of the disease remains elusive and drug development efforts are confounded by the lack of accurate monitoring markers. Disease heterogeneity, late-stage recruitment into pharmaceutical trials, and inclusion of phenotypically admixed patient cohorts are some of the key barriers to successful clinical trials. Machine Learning (ML) models and large international data sets offer unprecedented opportunities to appraise candidate diagnostic, monitoring, and prognostic markers. Accurate patient stratification into well-defined prognostic categories is another aspiration of emerging classification and staging systems.Methods: The objective of this paper is the comprehensive, systematic, and critical review of ML initiatives in ALS to date and their potential in research, clinical, and pharmacological applications. The focus of this review is to provide a dual, clinical-mathematical perspective on recent advances and future directions of the field. Another objective of the paper is the frank discussion of the pitfalls and drawbacks of specific models, highlighting the shortcomings of existing studies and to provide methodological recommendations for future study designs.Results: Despite considerable sample size limitations, ML techniques have already been successfully applied to ALS data sets and a number of promising diagnosis models have been proposed. Prognostic models have been tested using core clinical variables, biological, and neuroimaging data. These models also offer patient stratification opportunities for future clinical trials. Despite the enormous potential of ML in ALS research, statistical assumptions are often violated, the choice of specific statistical models is seldom justified, and the constraints of ML models are rarely enunciated.Conclusions: From a mathematical perspective, the main barrier to the development of validated diagnostic, prognostic, and monitoring indicators stem from limited sample sizes. The combination of multiple clinical, biofluid, and imaging biomarkers is likely to increase the accuracy of mathematical modeling and contribute to optimized clinical trial designs

Q&A: What is human language, when did it evolve and why should we care?

Human language is unique among all forms of animal communication. It is unlikely that any other species, including our close genetic cousins the Neanderthals, ever had language, and so-called sign 'language' in Great Apes is nothing like human language. Language evolution shares many features with biological evolution, and this has made it useful for tracing recent human history and for studying how culture evolves among groups of people with related languages. A case can be made that language has played a more important role in our species' recent (circa last 200,000 years) evolution than have our genes

Selection of time instants and intervals with Support Vector Regression for multivariate functional data

When continuously monitoring processes over time, data is collected along a whole period, from which only certain time instants and certain time intervals may play a crucial role in the data analysis. We develop a method that addresses the problem of selecting a finite and small set of short intervals (or instants) able to capture the information needed to predict a response variable from multivariate functional data using Support Vector Regression (SVR). In addition to improving interpretability, storage requirements, and monitoring cost, feature selection can potentially reduce overfitting by mitigating data autocorrelation. We propose a continuous optimization algorithm to fit the SVR parameters and select intervals and instants. Our approach takes advantage of the functional nature of the data by formulating a new bilevel optimization problem that integrates selection of intervals and instants, tuning of some key SVR parameters and fitting the SVR. We illustrate the usefulness of our proposal in some benchmark data sets

Dinosaurs reveal the geographical signature of an evolutionary radiation

Dinosaurs dominated terrestrial ecosystems across the globe for over 100 million years and provide a classic example of an evolutionary radiation. However, little is known about how these animals radiated geographically to become globally distributed. Here, we use a biogeographical model to reconstruct the dinosaurs’ ancestral locations, revealing the spatial mechanisms that underpinned this 170-million-year-long radiation. We find that dinosaurs spread rapidly initially, followed by a significant continuous and gradual reduction in their speed of movement towards the Cretaceous/Tertiary boundary (66 million years ago). This suggests that the predominant mode of dinosaur speciation changed through time with speciation originally largely driven by geographical isolation—when dinosaurs speciated more, they moved further. This was gradually replaced by increasing levels of sympatric speciation (species taking advantage of ecological opportunities within their existing environment) as terrestrial space became a limiting factor. Our results uncover the geographical signature of an evolutionary radiation

Relational development in children with cleft lip and palate: influence of the waiting period prior to the first surgical intervention and parental psychological perceptions of the abnormality:

BACKGROUND: The birth of a child with a cleft lip, whether or not in association with a cleft palate, is a traumatic event for parents. This prospective, multidisciplinary and multi-centre study aims to explore the perceptions and feelings of parents in the year following the birth of their child, and to analyse parent-child relationships. Four inclusion centres have been selected, differing as to the date of the first surgical intervention, between birth and six months. The aim is to compare results, also distinguishing the subgroups of parents who were given the diagnosis in utero and those who were not. METHODS/DESIGN: The main hypothesis is that the longer the time-lapse before the first surgical intervention, the more likely are the psychological perceptions of the parents to affect the harmonious development of their child. Parents and children are seen twice, when the child is 4 months (T0) and when the child is one year old (T1). At these two times, the psychological state of the child and his/her relational abilities are assessed by a specially trained professional, and self-administered questionnaires measuring factors liable to affect child-parent relationships are issued to the parents. The Alarme Detresse BeBe score for the child and the Parenting Stress Index score for the parents, measured when the child reaches one year, will be used as the main criteria to compare children with early surgery to children with late surgery, and those where the diagnosis was obtained prior to birth with those receiving it at birth. DISCUSSION: The mental and psychological dimensions relating to the abnormality and its correction will be analysed for the parents (the importance of prenatal diagnosis, relational development with the child, self-image, quality of life) and also, for the first time, for the child (distress, withdrawal). In an ethical perspective, the different time lapses until surgery in the different protocols and their effects will be analysed, so as to serve as a reference for improving the quality of information during the waiting period, and the quality of support provided for parents and children by the healthcare team before the first surgical intervention. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT00993993