Vincent Thomas

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Alterations in the proteome of Arabidopsis (Arabidopsis thaliana) leaves during responses to challenge by Pseudomonas syringae pv tomato DC3000 were analyzed using two-dimensional gel electrophoresis. Protein changes characteristic of the establishment of disease, basal resistance, and resistance-gene-mediated resistance were examined by comparing responses(More)
Partially Observable Markov Decision Processes (POMDPs) model sequential decision-making problems under uncertainty and partial observability. Unfortunately, some problems cannot be modeled with state-dependent reward functions, e.g., problems whose objective explicitly implies reducing the uncertainty on the state. To that end, we introduce ρPOMDPs, an(More)
Model-based Bayesian Reinforcement Learning (BRL) allows a sound formalization of the problem of acting optimally while facing an unknown environment, i.e., avoiding the exploration-exploitation dilemma. However, algorithms explicitly addressing BRL suffer from such a combinatorial explosion that a large body of work relies on heuristic algorithms. This(More)
Alterations in the proteome of Arabidopsis thaliana leaves during early responses to challenge by Pseudomonas syringae pv. tomato DC3000 (DC3000) were analysed using two-dimensional (2D) gel electrophoresis. Protein changes characteristic of the establishment of basal resistance and R-gene mediated resistance were examined by comparing responses to DC3000,(More)
The difficulties encountered in sequential decision-making problems under uncertainty are often linked to the large size of the state space. Exploiting the structure of the problem, for example by employing a factored representation, is usually an efficient approach but, in the case of partially observable Markov decision processes, the fact that some state(More)
1. Findings in cognitive psychology and neuropsychology have led to consider the existence of several mnestic systems. This study focuses on a now clearly established distinction between the procedural and the declarative memories. 2. The aim of the present study was to try and determine which of the two acquisition steps (learning and automation) is(More)
Model-based Bayesian Reinforcement Learning (BRL) allows a sound formalization of the problem of acting optimally while facing an unknown environment, i.e., avoiding the exploration-exploitation dilemma. However, algorithms explicitly addressing BRL su er from such a combinatorial explosion that a large body of work relies on heuristic algorithms. This(More)
A simple covalent enzyme-linked immunoassay procedure (CELIA) is described for the routine determination of free and immune complex-bound antibodies in sera. Assays for the latter could not have been performed by adsorption ELISA due to the high ionic strength of the reassociating buffer. For the measurement in human sera of free naturally occurring IgG and(More)