7.1.1 Angio-Analytics

• Keywords: Health, Cancer, Biomedical imaging
• Scientific Description: This tool allows the pharmacodynamic characterization of anti-vascular effects in anti-cancer treatments. It uses time series of in vivo images provided by intra-vital microscopy. Such in vivo images are obtained owing to skinfold chambers placed on mice skin. The automatized analysis is split up into two steps that were completely performed separately and manually before. The first steps corresponds to image processing to identify characteristics of the vascular network. The last step is the system identification of the pharmacodynamic response and the statistical analysis of the model parameters.
• Functional Description: Angio-Analytics allows the pharmacodynamic characterization of anti-vascular effects in anti-cancer treatments.
• Contact: Thierry Bastogne
• Participant: Thierry Bastogne

7.1.2 ARMADA

• Name: A Statistical Methodology to Select Covariates in High-Dimensional Data under Dependence
• Keywords: Biostatistics, Aggregated methods, High Dimensional Data, Personalized medicine, Variable selection
• Functional Description: Two steps variable selection procedure in a context of high-dimensional dependent data but few observations. First step is dedicated to eliminate dependence between variables (clustering of variables, followed by factor analysis inside each cluster). Second step is a variable selection using by aggregation of adapted methods. <https://hal.archives-ouvertes.fr/hal-02173568>
• News of the Year: This package is a new one.
• URL: https://cran.r-project.org/web/packages/armada/
• Publication: hal-02363338
• Contacts: Aurélie Muller, Anne Gégout-Petit
• Participants: Aurélie Muller, Anne Gégout-Petit

7.1.3 kosel

• Name: Variable Selection by Revisited Knockoffs Procedures
• Keywords: Variable selection, Regression
• Functional Description: Performs variable selection for many types of L1-regularised regressions using the revisited knockoffs procedure. This procedure uses a matrix of knockoffs of the covariates independent from the response variable Y. The idea is to determine if a covariate belongs to the model depending on whether it enters the model before or after its knockoff. The procedure suits for a wide range of regressions with various types of response variables. Regression models available are exported from the R packages 'glmnet' and 'ordinalNet'. Based on the paper linked to via the URL below: Gegout A., Gueudin A., Karmann C. (2019) <arXiv:1907.03153>
• News of the Year: This package is a new one.
• URL: https://cran.r-project.org/web/packages/kosel/kosel.pdf
• Publication: hal-01799914
• Contacts: Clémence Karmann, Aurélie Muller
• Participants: Clémence Karmann, Aurélie Muller, Anne Gégout-Petit

7.1.4 SesIndexCreatoR

• Functional Description: This package allows computing and visualizing socioeconomic indices and categories distributions from datasets of socioeconomic variables (These tools were developed as part of the EquitArea Project, a public health program).
• URL: http://www.equitarea.org/documents/packages_1.0-0/
• Contact: Benoît Lalloué
• Participants: Benoît Lalloué, Jean-Marie Monnez, Nolwenn Le Meur, Severine Deguen

7.1.5 In silico

• Name: In silico design of nanoparticles for the treatment of cancers by enhanced radiotherapy
• Keywords: Bioinformatics, Cancer, Drug development
• Functional Description: To speed up the preclinical development of medical engineered nanomaterials, we have designed an integrated computing platform dedicated to the virtual screening of nanostructured materials activated by X-ray making it possible to select nano-objects presenting interesting medical properties faster. The main advantage of this in silico design approach is to virtually screen a lot of possible formulations and to rapidly select the most promising ones. The platform can currently handle the accelerated design of radiation therapy enhancing nanoparticles and medical imaging nano-sized contrast agents as well as the comparison between nano-objects and the optimization of existing materials.
• Contact: Thierry Bastogne
• Participant: Thierry Bastogne

7.1.6 HSPOR

• Name: Hidden Smooth Polynomial Regression for Rupture Detection
• Keywords: Polynomial regression, Rupture detection
• Functional Description: Several functions that allow by different methods to infer a piecewise polynomial regression model under regularity constraints, namely continuity or differentiability of the link function. The implemented functions are either specific to data with two regimes, or generic for any number of regimes, which can be given by the user or learned by the algorithm.
• News of the Year: This package is a new one
• URL: https://cran.r-project.org/web/packages/HSPOR/
• Contact: Florine Greciet
• Participants: Florine Greciet, Romain Azais, Anne Gégout-Petit

7.1.7 cvmgof

• Keywords: Regression, Test, Estimators
• Scientific Description: Many goodness-of-fit tests have been developed to assess the different assumptions of a (possibly heteroscedastic) regression model. Most of them are "directional" in that they detect departures from a given assumption of the model. Other tests are "global" (or "omnibus") in that they assess whether a model fits a dataset on all its assumptions. cvmgof focuses on the task of choosing the structural part of the regression function because it contains easily interpretable information about the studied relationship. It implements 2 nonparametric "directional" tests and one nonparametric "global" test, all based on generalizations of the Cramer-von Mises statistic.
• Functional Description: cvmgof is an R library devoted to Cramer-von Mises goodness-of-fit tests. It implements three nonparametric statistical methods based on Cramer-von Mises statistics to estimate and test a regression model.
• News of the Year: New version available on CRAN website since Jan 11 2021 Preprint available on HAL since Jan 7 2021
• URL: https://cran.r-project.org/web/packages/cvmgof/index.html
• Publication: hal-03101612v1
• Contacts: Sandie Ferrigno, Romain Azais
• Participants: Sandie Ferrigno, Marie-José Martinez, Romain Azais

7.1.8 starm R

• Name: Spatio-Temporal Autologistic Regression Model, package R
• Keywords: Spatio-temporal, Autologistic model
• Functional Description: Estimation and model selection of the two-time centered autologistic regression model based on Gegout-Petit A., Guerin-Dubrana L., Li S. "A new centered spatio-temporal autologistic regression model. Application to local spread of plant diseases." 2019 <arXiv:1811.06782>. Application for the spatio-temporal modelling of the spread of a disease on a grid over time.
• Contact: Anne Gégout-Petit

8.1.1 Modelling of diffuse low-grade gliomas growth

We are continuing our research on the modelling of the growth of low grade diffuse gliomas. We propose an original MRI-based method to quantify gliomas brain infiltration, easy to implement and to interpret for Neuro-oncologists. The aim is to guide the treatment strategy in giving functional information using only anatomical knowlege and conventional MRI sequences. This work has been the subject of a conference paper 15.

A retrospective survival study over 35 years follow-up has been done 9.

8.1.2 Reconstruction of epigenetic landscapes from single-cell data

The aim is to better understand how living cells make decisions (e.g., differentiation of a stem cell into a particular specialized type), seeing decision-making as an emergent property of an underlying complex molecular network. Indeed, it is now proven that cells react probabilistically to their environment: cell types do not correspond to fixed states, but rather to “potential wells” of a certain energy landscape (representing the energy of the possible states of the cell) that we are trying to reconstruct. A first paper proposing a reconstruction method has been submitted 24 in the framework of an international collaboration (USA, Switzerland, France). Another paper is about to be submitted, dealing more specifically with the inference of the underlying networks.

Joint work with Nan Papili Gao (ETH Zurich), Olivier Gandrillon (ENS Lyon), András Páldi (EPHE, Paris), and Rudiyanto Gunawan (University at Buffalo, New York)

8.3.1 Cramér–von Mises goodness-of-fit tests in regression models

Many goodness-of-fit tests have been developed to assess the different assumptions of a (possibly heteroscedastic) regression model. Most of them are 'directional' in that they detect departures from a given assumption of the model. Other tests are 'global' (or 'omnibus') in that they assess whether a model fits a dataset on all its assumptions. We focus on the task of choosing the structural part of the regression function because it contains easily interpretable information about the studied relationship. We consider 2 nonparametric 'directional' tests and one nonparametric 'global' test, all based on generalizations of the Cramér–von Mises statistic.

To perform these goodness-of-fit tests, we develop the R package cvmgof (https://hal.archives-ouvertes.fr/hal-02014516), an easy-to-use tool for practitioners, available from the Comprehensive R Archive Network (https://CRAN.R-project.org/package=cvmgof). The use of the library is illustrated through a tutorial on real data and simulation studies are carried out in order to show how the package can be exploited to compare the 3 implemented tests. The practitioner can also easily compare the test procedures with different kernel functions, bootstrap distributions, numbers of bootstrap replicates, or bandwidths. A first article 22 has been submitted on this work.

To complete this work, it would be interesting to assess the other assumptions of a regression model such as the functional form of the variance or the additivity of the random error term. It should be noted that this can already be done using Ducharme and Ferrigno test implemented in cvmgof since it is a global test. However, it would be relevant to compare the results obtained from Ducharme and Ferrigno test with the ones obtained from other directional tests, especially developed to assess one of these specific assumptions. The implementation of these directional tests would enrich cvmgof package and offer a complete easy-to-use tool for validating regression models. Moreover, the assessment of the overall validity of the model when using several directional tests could be compared with that done when using only a global test. In particular, the well-known problem of multiple testing could be discussed by comparing the results obtained from multiple test procedures with those obtained when using a global test strategy. Another perspective of this work would be to develop a similar tool for other statistical models widely used in practice such as generalized linear models.

Join work with Romain Azaïs (INRIA, ENS Lyon) and Marie-José Martinez (LJK, Université Grenoble Alpes).

8.3.2 The revisited knockoffs method for variable selection in L1-penalized regressions

We consider the problem of variable selection in regression models. In particular, we are interested in selecting explanatory covariates linked with the response variable and we want to determine which covariates are relevant, that is which covariates are involved in the model. In this framework, we deal with L1-penalized regression models. To handle the choice of the penalty parameter to perform variable selection, we develop a new method based on the knockoffs idea. This revisited knockoffs method is general, suitable for a wide range of regressions with various types of response variables. Besides, it also works when the number of observations is smaller than the number of covariates and gives an order of importance of the covariates. Finally, we provide many experimental results to corroborate our method and compare it with other variable selection methods. This work is published in 5 and is implemented in package ‘kosel’.

The next subsections are dedicated to online data analysis

8.3.3 Widening the scope of an eigenvector stochastic approximation process and application to streaming PCA and related methods

Accepted in Journal of Multivariate Analysis in October 2020 8.

We prove the almost sure convergence of processes of Oja type to eigenvectors of the expectation of a random matrix while relaxing the i.i.d. assumptions on the observed random matrices. As an application of this generalization, we can perform the online PCA of a random vector Z when there is a data stream of i.i.d. observations of Z, even when both the metric used M and the expectation of Z are unknown and estimated online. Moreover, in order to update the stochastic approximation process at each step we are no more bound to using only a data mini-batch of observations of Z, but we can use all the previous observations up to the current step without storing them. This is useful not only when dealing with streaming data but also with Big Data as on can process it sequentially as a data stream. In addition, the general framework of this process, unlike other algorithms in the literature, covers also the case of factorial methods related to PCA.

In collaboration with A. Skiredj.

8.3.4 Streaming constrained binary logistic regression with online standardized data

Accepted in "Journal of Applied Statistics" in December 2020 7.

Online learning is a method for analyzing very large datasets ("big data") as well as data streams. In this article, we consider the case of constrained binary logistic regression and show the interest of using processes with an online standardization of the data, in particular to avoid numerical explosions or to allow the use of shrinkage methods. We prove the almost sure convergence of such a process and propose using a piecewise constant step-size such that the latter does not decrease too quickly and does not reduce the speed of convergence. We compare twenty-four stochastic approximation processes with raw or online standardized data on five real or simulated datasets. Results show that, unlike processes with raw data, processes with online standardized data can prevent numerical explosions and yield the best results.

In collaboration with E. Albuisson.

8.3.5 Construction and update of an online ensemble score involving linear discriminant analysis and logistic regression

Submitted in Februray 2021 25, 20.

The present aim is to update, upon arrival of new learning data, the parameters of a score constructed with an ensemble method involving linear discriminant analysis and logistic regression in an online setting, without the need to store all of the previously obtained data. Poisson bootstrap and stochastic approximation processes were used with online standardized data to avoid numerical explosions, the convergence of which has been established theoretically. This empirical convergence of online ensemble scores to a reference "batch" score was studied on five different datasets from which data streams were simulated, comparing six different processes to construct the online scores. For each score, 50 replications using a total of $10N$ observations ($N$ being the size of the dataset) were performed to assess the convergence and the stability of the method, computing the mean and standard deviation of a convergence criterion. A complementary study using $100N$ observations was also performed. The best processes were averaged processes using online standardized data and a piecewise constant step-size.

8.3.6 Change-point detection theresholds in the sequential context

Our work around change-point theresholds for the score-based CUSUM statistic in a sequential context has been published 11. In this paper, we consider the score-based cumulative sum statistic and propose to evaluate the detection performance of somethresholds on simulated data. Three thresholds come from the literature: the Wald constant, the empirical constant, and the conditional empirical instantaneous threshold. Two new thresholds are built by a simulation-based procedure: the first one is instantaneous, the second is a dynamical version of the previous one. The thresholds' performance measured by an estimation of the mean time between false alarm (MTBFA) and the average detection delay (ADD) are evaluated on independent and autocorrelated data for several scenarios, according to the detection objective and the real change in the data. The simulations allow us to compare the difference between the thresholds' results and to see that their performances prove to be robust when a parameter of the prechange regime is poorly estimated or when the data independence assumption is violated. We found also that the conditional empirical threshold is the best at minimizing the detection delay while maintaining the given false alarm rate. However, on real data, we suggest to use the dynamic instantaneous threshold because it is the easiest to build for practical implementation.

Our collaboration with APHP could not succeed because of the great delay in data collection. To apply our algorithms to real data, we turned to some EMG signal data provided by INRS. The study concerns the development of trapezius muscle myalgia in the workplace. We apply change-point detection to characterise different computer activities carried out during an experimental day.

8.4.1 Estimation of reference curves for fetal weight

In Epidemiology, we are working with INSERM to study fetal development in the last two trimesters of pregnancy. Reference or standard curves are required in this kind of biomedical problems. Values which lie outside the limits of these reference curves may indicate the presence of disorder. Data are from the French EDEN mother-child cohort (INSERM). It's a mother-child cohort study investigating the prenatal and early postnatal determinants of child health and development. 2002 pregnant women were recruited before 24 weeks of amenorrhoea in two maternity clinics from middle-sized French cities (Nancy and Poitiers). From May 2003 to September 2006, 1899 newborns were then included. The main outcomes of interest are fetal (via ultra-sound) and postnatal growth, adiposity development, respiratory health, atopy, behaviour and bone, cognitive and motor development. We are studying fetal weight that depends on the gestional age in the second and the third trimesters of mother's pregnancy. Some classical empirical and parametric methods as polynomial are first used to construct these curves. Polynomial regression is one of the most common parametric approach for modelling growth data espacially during the prenatal period. However, some of them requires strong assumptions. So, we propose to work with semi-parametric LMS method, by modifying the response variable (fetal weight) with a Box-cox transformation. A first article detailing these methodologies applied to the data is being written.

Alternative nonparametric methods as Nadaraya-Watson kernel estimation, local polynomial estimation, B-splines or cubic splines are also developed in this context to construct these curves. The practical implementation of these methods required working on smoothing parameters or choice of knots for the different types of nonparametric estimation. In particular, optimal choice of these parameters has been proposed. Then, a first version of an R package has been developed to propose a tool to construct nonparametric reference curves. This should be submitted to CRAN very soon. In addition, a graphical interface (GUI) intended for practitioners has been developed to allow intuitive visualization of the results given by the package.

Join work with Myriam Maumy-Bertrand (IRMA, Université de Strasbourg) and INSERM.

8.4.2 Construction of parsimonious event risk scores by an ensemble method. An illustration for short-term predictions in chronic heart failure patients from the GISSI-HF trial

Submitted in December 2020 27.

Heart failure (HF) is a worldwide major cause of mortality and morbidity for which many predictive scores have been defined. Selecting which explanatory variables to include in a given score is a common difficulty, as a balance must be found between statistical fit and practical application. This article presents a methodology for constructing parsimonious event scores combining a stepwise selection of variables with ensemble scores obtained by aggregation of several scores, using several classifiers, bootstrap samples and various modalities of random selection of variables. The stepwise selection allows constructing a succession of scores with the practitioner able to choose which score best fits his or her needs. The methods proposed herein can be reproduced on any set of variables as long as the training dataset comprises a sufficient number of cases. Three methods were compared in an application to construct parsimonious short-term scores in chronic HF patients. The working sample consisted of 11,411 couples patient-visit dyads from the GISSI-HF database, with 5,595 events and 5,816 non-events. Sixty-two candidate explanatory variables were studied. Focusing on the fastest method, four scores were constructed, yielding out-of-bag AUCs ranging from 0.81 (26 variables) to 0.76 (2 variables). These results are slightly better than those obtained by other scores reported in the literature using a similar number of variables.

In collaboration with E. Albuisson and D. Lucci.

8.4.3 Modeling and estimation of circulating tumor DNA (ctDNA) dynamics for detecting resistance to targeted therapies

Continuation of the ITMO Cancer project, supervised by Nicolas Champagnat, concerning the modeling of circulating tumor DNA (ctDNA) to detect the appearance of resistance to targeted therapies (personalized medicine). After a phase of investigation of possible scenarios in collaboration with Alexandre Harlé of the Institute of Cancerology of Lorraine (ICL), a final model was selected. Based on a mathematical analysis, the members of the project then designed a statistical inference algorithm (learning the parameters of the model, including the genealogical tree of mutations for each patient) which is intended to be validated on real data currently being acquired at the Nancy CHRU. The general idea is to exploit a “variational principle” that allows to explore the discrete space of family trees, of very large size, through a “pivot” space of continuous parameters, easy to optimize (and in reasonable numbers). An article detailing the model and its inference is currently being written.

In collaboration with N. Champagnat and C. Fritsch.

8.4.4 A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncology

We propose a new methodology for selecting and ranking covariates associated with a variable of interest in a context of high-dimensional data under dependence but few observations. The methodology successively intertwines the clustering of covariates, decorrelation of covariates using Factor Latent Analysis, selection using aggregation of adapted methods and finally ranking. A simulation study shows the interest of the decorrelation inside the different clusters of covariates. We first apply our method to transcriptomic data of 37 patients with advanced non-small-cell lung cancer who have received chemotherapy, to select the transcriptomic covariates that explain the survival outcome of the treatment. Secondly, we apply our method to 79 breast tumor samples to define patient profiles for a new metastatic biomarker and associated gene network in order to personalize the treatments. This work is published in 2 and is implemented in R package ‘ARMADA’.

In collaboration with T. Boukhobza and H. Dumond from CRAN and B. Bastien from biopharmaceutical industry Transgene.

8.4.5 Project linked with the COVID 19 pandemic

Pierre Vallois is the scientific coordinator of the seroprevalence study COVAL Nancy held in Nancy in July 2020 in collaboration with CHRU de Nancy (CIC épidémiologie clinique and Laboratoire de Virologie).

Background. The World Health Organisation recommends monitoring the circulation of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We aimed to estimate anti–SARS-CoV-2 total immunoglobulin (IgT) antibody seroprevalence and describe symptom profiles and in vitro seroneutralization in Nancy, France, in spring 2020.

Methods. Individuals were randomly sampled from electoral lists and invited with household members over 5 years old to be tested for anti–SARS-CoV-2 (IgT, i.e. IgA/IgG/IgM) antibodies by ELISA (Bio-rad). Serum samples were classified according to seroneutralization activity 50 % (NT50) on Vero CCL-81 cells. Age- and sex-adjusted seroprevalence was estimated. Subgroups were compared by chi-square or Fisher exact test and logistic regression.

Results. Among 2006 individuals, 43 were SARS-CoV-2–positive; the raw seroprevalence was 2.1 % (95 % confidence interval 1.5 to 2.9), with adjusted metropolitan and national standardized seroprevalence 2.5 % (1.8 to 3.3) and 2.3 % (1.7 to 3.1). Seroprevalence was highest for 20- to 34-year-old participants (4.7 % [2.3 to 8.4]), within than out of socially deprived area (2.5 % vs 1 %, P=0.02) and with than without intra-family infection (p<10-6). Moreover, 25 % (23 to 27) of participants presented at least one COVID-19 symptom associated with SARS-CoV-2 positivity (p<10-13), with anosmia or ageusia highly discriminant (odds ratio 27.8 [13.9 to 54.5]), associated with dyspnea and fever. Among the SARS-CoV-2-positives, 16.3 % (6.8 to 30.7) were asymptomatic. For 31 of these individuals, positive seroneutralization was demonstrated in vitro.

Conclusions. In this population of very low anti-SARS-CoV-2 antibody seroprevalence, a beneficial effect of the lockdown can be assumed, with frequent SARS-CoV-2 seroneutralization among IgT-positive patients.

10.1.1 Participation in other international programs

In Fall 2020, Bruno Scherrer was invited for 4 months in Berkeley to participate to Simons Institute Programme on the Theory of Reinforcement Learning. Due to the Covid constraints, the semester was eventually hold online.

10.2.1 Visits of international scientists

Juhyun Park (Lancaster University) visited Nancy for one week in the framework of her collaboration with A. Gégout-Petit on statistical test for paired distribution.

11.1.1 Journal

• Ulysse Herbach was a guest editor for the journal “Mathematical Biosciences and Engineering” (special edition “Cells as dynamical systems”).

11.1.2 Invited talks

• Anne Gégout-Petit was invited to a plenary communication in “Journées de Statistique”, Nice, France.
• Ulysse Herbach was invited to a plenary communication in conference “Interplay between Oncology, Mathematics and Numerics”, Paris, France.

11.1.3 Research administration

• Anne Gégout-Petit is the head of “Institut Élie Cartan de Lorraine” (mathematics laboratory of Université de Lorraine) since September 1st.

11.2.1 Teaching

Bruno Scherrer and Ulysse Herbach excepted, BIGS members have teaching obligations at "Université Lorraine" and are teaching at least 192 hours each year. They teach probability and statistics at different levels (Licence, Master, Engineering school). Many of them have pedagogical responsibilities.

• A. Gégout-Petit: Head of the Master 2 "Ingénierie Mathématique pour la science des données (Mathematical Engineering for data science)", Université de Lorraine
• T. Bastogne is in charge research master program "Santé Numérique et Imagerie Médicale" with the Faculty of Medicine, Université de Lorraine, France
• Master: S. Ferrigno, Experimental designs, 4.5h, M1, fourth year of EEIGM, Université de Lorraine, France
• Master: S. Ferrigno, Data analyzing and mining, 63h, M2, third year of Ecole des Mines, Université de Lorraine, France
• Master: S.Ferrigno, Modeling and forecasting, 43h, M1, second year of Ecole des Mines, Université de Lorraine, France
• Master: S.Ferrigno, Training projects, 18h, M1/M2, second and third year of Ecole des Mines, Université de Lorraine, France
• Master: A. Muller-Gueudin, Probability and Statistics, 160h, second year of ENSEM and ENSAIA, University of Lorraine, France.
• Master: A. Muller-Gueudin, Scientific calculation with Matlab, 20h, second year of ENSAIA, University of Lorraine, France.
• Master: A.Gégout-Petit, Statistics, modeling, 15h, future teacher, Université de Lorraine, France
• Master: A.Gégout-Petit, Statistics, modeling, data analysis, 80h, master in applied mathematics, Université de Lorraine, France
• Master: S. Wantz-Mézières, Learning and analysis of medical data, 36h, with J.M. Moureaux, Master SNIM, Université de Lorraine, France
• Licence: S. Wantz-Mézières, Applied mathematics for management, financial mathematics, Probability and Statistics, 160h, I.U.T. (L1/L2/L3)
• Licence: S. Wantz-Mézières, Probability, 100h, first year in Telecom Nancy engineering school (initial and apprenticeship cursus)
• Licence: A. Muller-Gueudin, Statistics, 60h, first year of ENSAIA, University of Lorraine, France.
• Licence: S. Ferrigno, Descriptive and inferential statistics, 60h, L2, second year of EEIGM, Université de Lorraine, France
• Licence: S. Ferrigno, Statistical modeling, 60h, L2, second year of EEIGM, Université de Lorraine, France
• Licence: S. Ferrigno, Mathematical and computational tools, 20h, L3, third year of EEIGM, Université de Lorraine, France
• Licence: S. Ferrigno, Training projects, 20h, L1/L3, first, second and third year of EEIGM, Université de Lorraine, France

11.2.2 Supervision

Defended PhD thesis

• PhD: Florine Greciet, "Modèles markoviens déterministes par morceaux cachés pour la propagation de fissures", grant CIFRE SAFRAN AIRCRAFT ENGINES, Advisors : R. Azaïs, A. Gégout-Petit, Université de Lorraine, defense on January, 2020.

PhD thesis

• PhD: Pauline Guyot, "Modélisation et Simulation de l’Electrocardiogramme d’un Patient Numérique", Grant : CIFRE-Cybernano. Advisors: T. Bastogne, E. H. Djermoune.
• PhD: Nassim Shaki, "Détection de rupture dans des signaux multivariés pour la prédiction d’événement redouté à partir de paramètres physiologiques recueillis par capteurs connectés après greffe pulmonaire", grant Inria-Cordis. Advisors: A. Gégout-Petit, S. Wantz-Mézières, M. d'Ortho.
• PhD: Nino Vieillard, "Deep Reinforcement Learning", CIFRE grand with Google Brain Paris. Advisors: B. Scherrer, M. Geist.

Post-doctoral positions

• Benoît Lalloué, contract research engineer for two years, RHU Fight RF, supervised by Jean-Marie Monnez.
• Postdoc: Emma Horton, Telomer Modelling, grant LUE GEENAGE. Advisors: A. Gégout-Petit, D. Villemonais. Emma was hired CR Inria at Bordeaux Sud-Ouest (ASTRAL team)

Other

• Master: all BIGS members regularly supervise project and internship of master IMOI students.
• Engineering school: all BIGS members regularly supervise projects of “École des Mines”, ENSEM, EEIGM or Télécom-Nancy students.

11.2.3 Juries

• Anne Gégout-Petit wrote the report and participated to the jury of the Phd defense of Titin Agustin NENGSIH, Strasbourg University, March 16th.
• Anne Gégout-Petit wrote the report and participated to the jury of the HDR defense of Maud Delattre, Paris-Saclay University, November 6th.
• Anne Gégout-Petit is member of the “Jury du prix de thèse AMIES”.
• Bruno Scherrer participated to the jury of the Phd defense of Matthieu Guillot, G-SCOP lab, Grenoble INP, July 3rd.
• Bruno Scherrer participated to the jury of the Phd defense of Rituraj Kaushik, July 23rd.

11.3.1 Education

• Sandie Ferrigno: Advisor of a group of students (EEIGM), "La main à la Pâte" project, elementary schools, Nancy, January-June 2020.
• Sandie Ferrigno: Advisor of a group of students (EEIGM), "Energies renouvelables", "La main à la Pâte" project, Institut médico-éducatif (IME), Commercy, January 2020.
• Sandie Ferrigno: Advisor of a group of students (EEIGM), "L'Astronomie", Cgénial project, Collège Paul Verlaine, Malzéville, January 2020.
• Sandie Ferrigno: Advisor of a group of students (EEIGM), "Le Chocolat", Cgénial project, Collège de la Craffe, Nancy, January 2020.

11.3.2 Interventions

• Sophie Wantz-Mézières was part of the organization of a thematic and multidisciplinary week “Neurosciences, Neuro-oncologie et Numérique” for students from Télécom-Nancy and Faculté de Médecine de Nancy, janvier 2020.
• Bruno Scherrer made detailed simulations of the reform for the retirement system that has been considered by Philippe's government in France 28.

International journals

• 1 article Jean-BaptisteJ.-B. Barbry, Anne-SophieA.-S. Poinsard, ThierryT. Bastogne and OlivierO. Balland. Short-term effects of ocular 2% dorzolamide, 0.5% timolol or 0.005% latanoprost on the anterior segment architecture in healthy cats: a prospective study. Open Veterinary Journal 2020
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• 2 articleBérangèreB. Bastien, TahaT. Boukhobza, HélèneH. Dumond, AnneA. Gégout-Petit, AurélieA. Muller-Gueudin and CharlèneC. Thiébaut. A statistical methodology to select covariates in high-dimensional data under dependence. Application to the classification of genetic profiles in oncologyJournal of Applied Statistics2021, 23
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• 3 articleAurélienA. Buessler, TaharT. Chouihed, KévinK. Duarte, AdrienA. Bassand, MatthieuM. Huot-Marchand, YannickY. Gottwalles, AliceA. Pénine, EliesE. André, LionelL. Nace, DéborahD. Jaeger, MasatakeM. Kobayashi, StefanoS. Coiro, PatrickP. Rossignol and NicolasN. Girerd. Accuracy of Several Lung Ultrasound Methods for the Diagnosis of Acute Heart Failure in the ED: A Multicenter Prospective StudyChest1571January 2020, 99-110
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• 4 article MarieM. Ferrua, EtienneE. Minvielle, AudeA. Fourcade, BenoîtB. Lalloué, ClaudeC. Sicotte, MarioM. Di Palma and OlivierO. Mir. How to Design a Remote Patient Monitoring System? A French Case Study BMC Health Services Research 20 1 December 2020
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• 5 article AnneA. Gégout-Petit, AurélieA. Gueudin-Muller and ClémenceC. Karmann. The revisited knockoffs method for variable selection in L 1 -penalized regressions Communications in Statistics - Simulation and Computation July 2020
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• 6 articlePaulineP. Guyot, El-HadiE.-H. Djermoune, BrunoB. Chenuel and ThierryT. Bastogne. A signal demodulation-based method for the early detection of Cheyne-Stokes respirationPLoS ONE153March 2020, e0221191
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• 7 article BenoîtB. Lalloué, Jean-MarieJ.-M. Monnez and ElianeE. Albuisson. Streaming constrained binary logistic regression with online standardized data Journal of Applied Statistics 2021
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• 8 articleJean-MarieJ.-M. Monnez and AbderrahmanA. Skiredj. Widening the scope of an eigenvector stochastic approximation process and application to streaming PCA and related methodsJournal of Multivariate Analysis182March 2021, 19
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• 9 articleTiphaineT. Obara, MarieM. Blonski, CyrilC. Brzenczek, SophieS. Mézières, YannY. Gaudeau, CelsoC. Pouget, GuillaumeG. Gauchotte, AntoineA. Verger, GuillaumeG. Vogin, Jean-MarieJ.-M. Moureaux, HuguesH. Duffau, FabienF. Rech and LucL. Taillandier. Adult diffuse low-grade gliomas: 35-year experience at the Nancy France neurooncology unitFrontiers in Oncology10October 2020, 574679
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• 10 article PatrickP. Rossignol, KévinK. Duarte, NicolasN. Girerd, MoezM. Karoui, John J.V.J. McMurray, KarlK. Swedberg, DirkD. Veldhuisen, StuartS. Pocock, KennethK. Dickstein, FaiezF. Zannad and BertramB. Pitt. Cardiovascular risk associated with serum potassium in the context of mineralocorticoid receptor antagonist use in patients with heart failure and left ventricular dysfunction European Journal of Heart Failure January 2020
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• 11 articleNassimN. Sahki, AnneA. Gégout-Petit and SophieS. Wantz-Mézières. Performance study of change‐point detection thresholds for cumulative sum statistic in a sequential contextQuality and Reliability Engineering International1-21July 2020, 21
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International peer-reviewed conferences

• 12 inproceedings NinoN. Vieillard, TadashiT. Kozuno, BrunoB. Scherrer, OlivierO. Pietquin, RémiR. Munos and MatthieuM. Geist. Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning NeurIPS - 34th Conference on Neural Information Processing Systems Vancouver / Online, Canada December 2020
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• 13 inproceedings NinoN. Vieillard, BrunoB. Scherrer, OlivierO. Pietquin and MatthieuM. Geist. Momentum in Reinforcement Learning Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) 2020, Palermo, Italy. PMLR : Volume 108. Copyright 2020 by the author(s). AISTATS 2020 - 23rd International Conference on Artificial Intelligence and Statistics 108 Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS) 2020 Palermo / Virtual, Italy 2020
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Conferences without proceedings

• 14 inproceedings LevyL. Batista, MathieuM. Milhem, ThierryT. Bastogne, FabienF. Clanché, GabinG. Personeni, Jean-PhilippeJ.-P. Jehl and GérômeG. Gauchard. A data-driven classification solution for the timed-up and go test in risk falling assessment EMBC 2020 - 42nd Engineering in Medicine and Biology Conference Montréal, Canada July 2020
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• 15 inproceedings CyrilC. Brzenczek, SophieS. Wantz-Mézières, YannY. Gaudeau, MarieM. Blonski, FabienF. Rech, TiphaineT. Obara, Jean-MarieJ.-M. Moureaux and LucL. Taillandier. An original MRI-based method to quantify the diffuse low-grade glioma brain infiltration 10th International Conference on Image Processing Theory, Tools and Applications, IPTA’20 Paris, France November 2020
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• 16 inproceedings JeanneJ. Deleforterie, LucieL. Hassler and ThierryT. Bastogne. A dendrogram clustering of lipid nanoparticles 15th annual event of the ETPN – European Technology Platform on Nanomedicine, ETPN2020 Heraklion, Greece October 2020
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• 17 inproceedings LucieL. Hassler and ThierryT. Bastogne. Approche bayésienne du Quality-by-Design appliquée à un bioprocédé d’extraction de principe actif 5th Bioproduction Congress Lyon, France September 2020
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• 18 inproceedings YaëlY. Kolasa, ThierryT. Bastogne, Jean-PhilippeJ.-P. Georges and SylvainS. Kubler. Quality-by-design-engineered pBFT consensus configuration for medical device development EMBC 2020 - 42nd Engineering in Medicine and Biology Conference Montreal, Canada July 2020
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• 19 inproceedings YaelY. Kolasa, EliottE. Gandiole and ThierryT. Bastogne. Quality-by-design development of a patient mobility e-monitoring system 2nd EAI International Conference on Wearables in Healthcare, EAI HealthWear 2020 Virtual, France 2020
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• 20 inproceedings BenoîtB. Lalloué, Jean-MarieJ.-M. Monnez and ElianeE. Albuisson. Convergence d'un score d'ensemble en ligne : étude empirique 52e Journées de Statistique Nice, France https://jds2020.sciencesconf.org/ July 2020
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Doctoral dissertations and habilitation theses

• 21 thesis FlorineF. Greciet. Piecewise polynomial regression for crack propagation Université de Lorraine January 2020
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Reports & preprints

• 22 misc RomainR. Azaïs, SandieS. Ferrigno and Marie-JoséM.-J. Martinez. cvmgof: an R package for Cramér-von Mises goodness-of-fit tests in regression models January 2021
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• 23 misc ThierryT. Bastogne. Supplementary material iQbD: a TRL-indexed Quality-by-Design Paradigm for Medical Device Development September 2020
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• 24 misc Nan PapiliN. Gao, OlivierO. Gandrillon, AndrásA. Páldi, UlysseU. Herbach and RudiyantoR. Gunawan. Universality of cell differentiation trajectories revealed by a reconstruction of transcriptional uncertainty landscapes from single-cell transcriptomic data February 2021
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• 25 misc BenoîtB. Lalloué, Jean-MarieJ.-M. Monnez and ElianeE. Albuisson. Construction and update of an online ensemble score involving linear discriminant analysis and logistic regression February 2021
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• 26 misc BenoîtB. Lalloué and Jean-MarieJ.-M. Monnez. Ensemble methods and online learning for creation and update of prognostic scores in HF patients November 2020
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• 27 misc BenoîtB. Lalloué, Jean-MarieJ.-M. Monnez, DonataD. Lucci and ElianeE. Albuisson. Construction of parsimonious event risk scores by an ensemble method. An illustration for short-term predictions in chronic heart failure patients from the GISSI-HF trial. December 2020
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• 28 report BrunoB. Scherrer. Simulations de carrières et retraites à points dans 3 cadres macro-économiques: modèle du gouvernement Philippe (âge-pivot bloqué), modèle du gouvernement Philippe corrigé (âge-pivot glissant), modèle Destinie2 (avec revalorisation de la fonction publique) INRIA March 2020
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