Change Acceleration and Detection

A novel sequential change detection problem is proposed, in which the change should be not only detected but also accelerated. Specifically, it is assumed that the sequentially collected observations are responses to treatments selected in real time. The assigned treatments not only determine the pre-change and post-change distributions of the responses, but also influence when the change happens. The problem is to find a treatment assignment rule and a stopping rule that minimize the expected total number of observations subject to a user-specified bound on the false alarm probability. The optimal solution to this problem is obtained under a general Markovian change-point model. Moreover, an alternative procedure is proposed, whose applicability is not restricted to Markovian change-point models and whose design requires minimal computation. For a large class of change-point models, the proposed procedure is shown to achieve the optimal performance in an asymptotic sense. Finally, its performance is found in two simulation studies to be close to the optimal, uniformly with respect to the error probability

Some topics in sequential analysis

Sequential analysis refers to the statistical theory and methods that can be applied to situations where the sample size is not fixed in advance. Instead, the data are collected sequentially over time, and the sampling is stopped according to a pre-specified stopping rule as soon as the accumulated information is deemed sufficient. The goal of this adaptive approach is to reach a reliable decision as soon as possible. This dissertation investigates two problems in sequential analysis. In the first problem, assuming that data are collected sequentially from independent streams, we consider the simultaneous testing of multiple hypotheses. We start with the class of procedures that control the classical familywise error probabilities of both type I and type II under two general setups: when the number of signals (correct alternatives) is known in advance, and when we only have a lower and an upper bound for it. Then we continue to study two generalized error metrics: under the first one, the probability of at least k mistakes, of any kind, is controlled; under the second, the probabilities of at least k1 false positives and at least k2 false negatives are simultaneously controlled. For each formulation, the optimal expected sample size is characterized, to a first-order asymptotic approximation as the error probabilities vanish, and a novel multiple testing procedure is proposed and shown to be asymptotically efficient under every signal configuration. In the second problem, we propose a generalization of the Bayesian sequential change detection problem, where the change is a latent event that should be not only detected but also accelerated. It is assumed that the sequentially collected observations are responses to treatments selected in real time. The assigned treatments not only determine the distribution of responses before and after the change, but also influence when the change happens. The problem is to find a treatment assignment rule and a stopping rule to minimize the average total number of observations subject to a bound on the false-detection probability. We propose an intuitive solution, which is easy to implement and achieves for a large class of change-point models the optimal performance up to a first-order asymptotic approximation. A simulation study suggests the almost exact optimality of the proposed scheme under a Markovian change-point model

Characterization of plant growth-promoting rhizobacteria from perennial ryegrass and genome mining of novel antimicrobial gene clusters

Background Plant growth-promoting rhizobacteria (PGPR) are good alternatives for chemical fertilizers and pesticides, which cause severe environmental problems worldwide. Even though many studies focus on PGPR, most of them are limited in plant-microbe interaction studies and neglect the pathogens affecting ruminants that consume plants. In this study, we expand the view to the food chain of grass-ruminant-human. We aimed to find biocontrol strains that can antagonize grass pathogens and mammalian pathogens originated from grass, thus protecting this food chain. Furthermore, we deeply mined into bacterial genomes for novel biosynthetic gene clusters (BGCs) that can contribute to biocontrol. Results We screened 90 bacterial strains from the rhizosphere of healthy Dutch perennial ryegrass and characterized seven strains (B. subtilis subsp. subtilis MG27, B. velezensis MG33 and MG43, B. pumilus MG52 and MG84, B. altitudinis MG75, and B. laterosporus MG64) that showed a stimulatory effect on grass growth and pathogen antagonism on both phytopathogens and mammalian pathogens. Genome-mining of the seven strains discovered abundant BGCs, with some known, but also several potential novel ones. Further analysis revealed potential intact and novel BGCs, including two NRPSs, four NRPS-PKS hybrids, and five bacteriocins. Conclusion Abundant potential novel BGCs were discovered in functional protective isolates, especially in B. pumilus, B. altitudinis and Brevibacillus strains, indicating their great potential for the production of novel secondary metabolites. Our report serves as a basis to further identify and characterize these compounds and study their antagonistic effects against plant and mammalian pathogens

Stratified incomplete local simplex tests for curvature of nonparametric multiple regression

Principled nonparametric tests for regression curvature in $\mathbb{R}^{d}$ are often statistically and computationally challenging. This paper introduces the stratified incomplete local simplex (SILS) tests for joint concavity of nonparametric multiple regression. The SILS tests with suitable bootstrap calibration are shown to achieve simultaneous guarantees on dimension-free computational complexity, polynomial decay of the uniform error-in-size, and power consistency for general (global and local) alternatives. To establish these results, a general theory for incomplete $U$-processes with stratified random sparse weights is developed. Novel technical ingredients include maximal inequalities for the supremum of multiple incomplete $U$-processes

Exploring plant-microbe interactions of the rhizobacteria Bacillus subtilis and Bacillus mycoides by use of the CRISPR-Cas9 system

Bacillus subtilis HS3 and Bacillus mycoides EC18 are two rhizosphere-associated bacteria with plant growth-promoting activity. The CRISPR-Cas9 system was implemented to study various aspects of plant-microbe interaction mechanisms of these two environmental isolates. The results show that fengycin and surfactin are involved in the antifungal activity of B. subtilis HS3. Moreover, this strain emits several other volatile organic compounds than 2,3-butanediol, contributing to plant growth promotion. Confocal laser scanning microscopy observations of the GFP-labelled strain showed that HS3 selectively colonizes root hairs of grass (Lolium perenne) in a hydroponic system. For B. mycoides EC18, we found that the wild-type EC18 strain and a Delta asbA (petropectin-deficient) mutant, but not the Delta dhbB (bacillibactin-deficient) and ADKO (asbA and dhbB double knockout) mutants, can increase the plant biomass and total chlorophyll. All the mutant strains have a reduced colonization capability on Chinese cabbage (Brassica rapa) roots, at the root tip and root hair region compared with the wild-type strain. These results indicate that the siderophore, bacillibactin, is involved in the plant growth promoting activity and could affect the root colonization of B. mycoides. Collectively, the CRISPR-Cas9 system we developed for environmental isolates is broadly applicable and will facilitate deciphering the mechanisms of Bacillus-plant interactions. (c) 2018 The Authors

Chaine de generation et d'analyse d'interferences spatio-temporelles entre signaux lasers en regime picoseconde et mesure de leurs distorsions dans des fibres optiques

SIGLEAvailable from INIST (FR), Document Supply Service, under shelf-number : TD 80880 / INIST-CNRS - Institut de l'Information Scientifique et TechniqueFRFranc

Truncated LinUCB for Stochastic Linear Bandits

This paper considers contextual bandits with a finite number of arms, where the contexts are independent and identically distributed $d$-dimensional random vectors, and the expected rewards are linear in both the arm parameters and contexts. The LinUCB algorithm, which is near minimax optimal for related linear bandits, is shown to have a cumulative regret that is suboptimal in both the dimension $d$ and time horizon $T$, due to its over-exploration. A truncated version of LinUCB is proposed and termed "Tr-LinUCB", which follows LinUCB up to a truncation time $S$ and performs pure exploitation afterwards. The Tr-LinUCB algorithm is shown to achieve $O(d\log(T))$ regret if $S = Cd\log(T)$ for a sufficiently large constant $C$, and a matching lower bound is established, which shows the rate optimality of Tr-LinUCB in both $d$ and $T$ under a low dimensional regime. Further, if $S = d\log^{\kappa}(T)$ for some $\kappa>1$, the loss compared to the optimal is a multiplicative $\log\log(T)$ factor, which does not depend on $d$. This insensitivity to overshooting in choosing the truncation time of Tr-LinUCB is of practical importance