Dynamics of agrarian landscapes in Western Thailand : Agro-ecological zonation and agricultural transformations in Kanjanaburi Province: hypotheses for improving farming systems sustainability

Ce document traite de la zonation agroécologique à petite échelle, comme outil essentiel dans la recherche orientée sur les systèmes agraires en vue du développement. Ces systèmes sont définis comme modes d'exploitation adaptés à l'environnement (naturel et humain) y compris échanges de produit et patrimoine culturel; l'étude comprend systèmes de production et de culture, et types d'utilisation des sols. Les diverses relations entre éléments sont analysées dans l'espace et le temps de façon à dégager la dynamique des transformations. Le projet a fait intervenir des équipes pluridisciplinaires comprenant agronomes et spécialistes des ressources naturelles en sociologie et télédétection; le tout aux niveaux de la parcelle et de l'exploitation agricole. Le texte, qui comporte un glossaire technique précis, est illustré de six clichés en couleurs (cultures de maïs, cotonnier, manioc, manguiers) et d'une image digitale en couleurs d'une partie de l'ouest de la Thaïlande vue du satellite Landsat-T

Knowledge-aware Complementary Product Representation Learning

Learning product representations that reflect complementary relationship plays a central role in e-commerce recommender system. In the absence of the product relationships graph, which existing methods rely on, there is a need to detect the complementary relationships directly from noisy and sparse customer purchase activities. Furthermore, unlike simple relationships such as similarity, complementariness is asymmetric and non-transitive. Standard usage of representation learning emphasizes on only one set of embedding, which is problematic for modelling such properties of complementariness. We propose using knowledge-aware learning with dual product embedding to solve the above challenges. We encode contextual knowledge into product representation by multi-task learning, to alleviate the sparsity issue. By explicitly modelling with user bias terms, we separate the noise of customer-specific preferences from the complementariness. Furthermore, we adopt the dual embedding framework to capture the intrinsic properties of complementariness and provide geometric interpretation motivated by the classic separating hyperplane theory. Finally, we propose a Bayesian network structure that unifies all the components, which also concludes several popular models as special cases. The proposed method compares favourably to state-of-art methods, in downstream classification and recommendation tasks. We also develop an implementation that scales efficiently to a dataset with millions of items and customers

Permutation entropy and irreversibility in gait kinematic time series from patients with mild cognitive decline and early alzheimer’s dementia

Gait is a basic cognitive purposeful action that has been shown to be altered in late stages of neurodegenerative dementias. Nevertheless, alterations are less clear in mild forms of dementia, and the potential use of gait analysis as a biomarker of initial cognitive decline has hitherto mostly been neglected. Herein, we report the results of a study of gait kinematic time series for two groups of patients (mild cognitive impairment and mild Alzheimer’s disease) and a group of matched control subjects. Two metrics based on permutation patterns are considered, respectively measuring the complexity and irreversibility of the time series. Results indicate that kinematic disorganisation is present in early phases of cognitive impairment; in addition, they depict a rich scenario, in which some joint movements display an increased complexity and irreversibility, while others a marked decrease. Beyond their potential use as biomarkers, complexity and irreversibility metrics can open a new door to the understanding of the role of the nervous system in gait, as well as its adaptation and compensatory mechanismsThis research was funded through the Premio del Ilustre Colegio Profesional de Fisioterapeutas de la Comunidad De Madrid, prize number ICPFM-IX-201

Multiple testing for SNP-SNP interactions

Most genetic diseases are complex, i.e. associated to combinations of SNPs rather than individual SNPs. In the last few years, this topic has often been addressed in terms of SNP-SNP interaction patterns given as expressions linked by logical operators. Methods for multiple testing in high-dimensional settings can be applied when many SNPs are considered simultaneously. However, another less well-known multiple testing problem arises within a fixed subset of SNPs when the logic expression is chosen optimally. In this article, we propose a general asymptotic approach for deriving the distribution of the maximally selected chi-square statistic in various situations. We show how this result can be used for testing logic expressions - in particular SNP-SNP interaction patterns - while controlling for multiple comparisons. Simulations show that our method provides multiple testing adjustment when the logic expression is chosen such as to maximize the statistic. Its benefit is demonstrated through an application to a real dataset from a large population-based study considering allergy and asthma in KORA. An implementation of our method is available from the Comprehensive R Archive Network (CRAN) as R package 'SNPmaxsel'