. % [LMGL LEW FIIR TYFPMWLIH MR ¾REP JSVQ EX LXXT SRPMRIPMFVEV] [MPI] GSQ HSM VWWF EFWXVEGX 8LMW EVXMGPI QE] FI YWIH JSV RSR GSQQIVGMEP TYVTSWIW MR EGGSVHERGI [MXL ;MPI] 8IVQW ERH 'SRHMXMSRW JSV 7IPJ %VGLMZMRK

Abstract. This paper presents a generative model and its estimation allowing to visualize binary data. Our approach is based on the Bernoulli block mixture model and the probabilistic self-organizing maps. This leads to an efficient variant of Generative Topographic Mapping. The obtained method is parsimonious and relevant on real data.

No abstract

Co-clustering leads to parsimony in data visualisation with a number of parameters dramatically reduced in comparison to the dimensions of the data sample. Herein, we propose a new generalized approach for nonlinear mapping by a re-parameterization of the latent block mixture model. The densities modeling the blocks are in an exponential family such that the Gaussian, Bernoulli and Poisson laws are particular cases. The inference of the parameters is derived from the block expectation-maximization algorithm with a Newton-Raphson procedure at the maximization step. Empirical experiments with textual data validate the interest of our generalized model.The authors have very carefully revised the document by following every comments from the editors and the two anonymous reviewers. It has been tried every possible effort to solve each remark that has been addressed. Below a detailed summary of the updates is provided. We would like to thank the editors and the reviewers for their constructive comments on this manuscript and positive support.To Editors:Once more, we would very much like to invite you to revise your paper, seriously taking into account the comments of the reviewers, and to resubmit your revised version by 02/25/2015 (mm/dd/yy). Any revision received after that may be treated as a new submission. Authors' response:The paper has been revised according to the comments and suggestions of reviewer 2.2) To Reviewer #1:The revised manuscript is sufficient to Neurocomputing publication standards, and I suggest accepting this manuscript. Authors' response:Thanks for the positive comments and the opportunity of publishing the document in Neurocomputing.3) To Reviewer #2: Q1. I thank the authors for the revised version of their manuscript. Authors' response:Thanks for the positive comments.Q2. They open the abstract with the statement: "Parametric methods for data visualisation are most of the time founded on an usual mixture model framework." Even a light-hearted revision of existing parametric methods for multivariate data visualization (See, for instance, Lee & Verleysen, 2007) would reveal that this is not the case. Therefore, I think this statement should be either removed or revised. Authors' response:Thanks for this suggestion. Indeed, the term « parametric methods » was meaning « probabilistic methods » or « parametric model » in a statistical framework and could have been read as any methods with parameters on the contrary to svd for instance. This sentence has been removed, and the summary updated for complying with other comments in the review.Q3. Co(Bi)-clustering in general and co(bi)-clustering with visualization-oriented self-organizing models are more adequately introduced in the new version. Authors' response:Thanks for this remark.Q4. I am a bit puzzled by the new introduction "storyline", though. It roughly goes like this:Revision Notes a) -Co-clustering was first proposed in the seventies and some more works [6][7][8][9][10][11] Authors' response:Thanks for this concern. Indeed, af...

The three main obstacles to the elimination of malaria in French Guiana are asymptomatic carriers of Plasmodium vivax, relapses and, to a lesser extent, Plasmodium falciparum. This study aims to assess the impact of PCR-based mass screening and treatment (MSAT) interventions in this malaria-endemic area.Two MSAT interventions were conducted twelve months apart in inhabitants of Saint Georges de l’Oyapock village, which has the highest malaria burden in French Guiana. Symptomatic malaria incidence was also passively monitored through the local health center from 12 months before the first intervention until the end of the second intervention.At the time of the first intervention, malaria prevalence was 6.7% [CI95 5.4-7.9%], including 90% of Plasmodium vivax cases and 10% Plasmodium falciparum (n=1,501 participants). Twelve months later, it had decreased by 53.7% to a value of 2.5% [CI95 2.0-3.9%] (p<0.05; n=1,271 inhabitants), of which 83% and 17% of cases showed Plasmodium vivax and Plasmodium falciparum carriage, respectively. Similarly, the passive malaria detection carried out by the health center during the 12-month surveillance period that followed the first MSAT noted a decrease in symptomatic Plasmodium spp..This study suggests that the implementation of mass PCR testing and the subsequent malaria treatment of positive cases could reduce the prevalence of both symptomatic and asymptomatic malaria infections in the Amazonian context.