Combination of convex theories: Modularity,deduction completeness,and explanation

Decision procedures are key components of theorem provers and constraint satisfaction systems. Their modular combination is of prime interest for building efficient systems, but their effective use is often limited by poor interface capabilities, when such procedures only provide a simple “sat/unsat” answer. In this paper, we develop a framework to design cooperation schemas between such procedures while maintaining modularity of their interfaces. First, we use the framework to specify and prove the correctness of classic combination schemas by Nelson–Oppen and Shostak. Second, we introduce the concept of deduction complete satisfiability procedures, we show how to build them for large classes of theories, then we provide a schema to modularly combine them. Third, we consider the problem of modularly constructing explanations for combinations by re-using available proof-producing procedures for the component theories.     

An Automatic Correction of Ma’s Thinning Algorithm Based on <Emphasis Type="Italic">P</Emphasis>-simple Points

The notion of P-simple points was introduced by Bertrand to conceive parallel thinning algorithms. In ‘A 3D fully parallel thinning algorithm for generating medial faces’ (Pattern Recogn. Lett. 16:83–87, 1995), Ma proposed an algorithm for which there are objects whose topology is not preserved. In this paper, we propose a new application of P-simple points: to automatically correct Ma’s algorithm.     

Co-constructing with stakeholders a role-playing game to initiate collective management of erosive runoff risks at the watershed scale

Erosive runoff is a recurring problem and is a source of sometimes deadly muddy floods in the Pays de Caux (France). The risk results from a conjunction of natural factors and human activity. Efficient actions against runoff in agricultural watersheds are well known. However they are still difficult to implement as they require co-operation between stakeholders. Local actors thus need tools to help them understand the collective consequences of their individual decisions and help to initiate a process of negotiation between them. We decided to use a participatory approach called companion modelling (ComMod), and, in close collaboration with one of the first group of local stakeholders, to create a role-playing game (RPG) to facilitate negotiations on the future management of erosive runoff. This paper describes and discusses the development of the RPG and its use with other groups of local stakeholders within the framework of two game sessions organized by two different watershed management committees. During the joint construction step, stakeholders shared their viewpoints about the environment, agents, rules, and how to model runoff in preparation for the creation of the RPG. During the RPG sessions, two groups of eight players, including farmers, mayors and watershed advisors, were confronted with disastrous runoff in a fictive agricultural watershed. Results showed that they managed to reduce runoff by 20–50% by engaging a dialogue about grass strips, storage ponds and management of the intercrop period. However, further progress is still needed to better control runoff through the implementation of better agricultural practices because, during the RPG sessions, the watershed advisors did not encourage farmers to do so. Because of the complexity of management problems, results of jointly constructing the game and the RPG sessions showed that modelling and simulation can be a very useful way of accompanying the collective learning process. This new way of working was welcomed by the participants who expressed their interest in organizing further RPG sessions.     

Measurement of oocyte temporal maturation process by means of a simple optical micro-system

We present a simple optical micro-system used to measure the transmission spectra of oocytes in order to qualify their maturation stage. Two applications of the device are possible: (i) the evaluation of the maturation stages of oocytes, and (ii) the development of a fertilization indicator. For the first application, GV, MI and MII oocytes were also analysed. Transmission spectra allow the 3 maturation stages to be identified but cannot be used to estimate the maturity of an unknown oocyte. Oocytes are subject to continuous development. This is why spectral separation of the 3 maturation stages cannot be made although they can be visually identified. However, the visual observation remains biologist-dependent. We therefore investigated the temporal maturation evolution of the oocytes in terms of transmission spectra and probability analysis. Results show that oocytes to be fertilized should not only be chosen in the MII stage, but also at the right time during the MII stage. This particular aspect requires further investigation. However, spectral measurements could be used as a technique for monitoring the maturation evolution of the oocytes. Fertilized oocytes exhibiting fertilization abnormalities were also tested. The device proved to be an efficient fertilization indicator.     

Learning decision trees with taxonomy of propositionalized attributes

We consider the problem of exploiting a taxonomy of propositionalized attributes in order to learn compact and robust classifiers. We introduce propositionalized attribute taxonomy guided decision tree learner (PAT-DTL), an inductive learning algorithm that exploits a taxonomy of propositionalized attributes as prior knowledge to generate compact decision trees. Since taxonomies are unavailable in most domains, we also introduce propositionalized attribute taxonomy learner (PAT-Learner) that automatically constructs taxonomy from data. PAT-DTL uses top-down and bottom-up search to find a locally optimal cut that corresponds to the literals of decision rules from data and propositionalized attribute taxonomy. PAT-Learner propositionalizes attributes and hierarchically clusters the propositionalized attributes based on the distribution of class labels that co-occur with them to generate a taxonomy. Our experimental results on UCI repository data sets show that the proposed algorithms can generate a decision tree that is generally more compact than and is sometimes comparably accurate to those produced by standard decision tree learners.     

An Efficient Newton-Krylov Implementation of the Constrained Runs Scheme for Initializing on a Slow Manifold

The long-term dynamic behavior of many dynamical systems evolves on a low-dimensional, attracting, invariant slow manifold, which can be parameterized by only a few variables (“observables”). The explicit derivation of such a slow manifold (and thus, the reduction of the long-term system dynamics) is often extremely difficult or practically impossible. For this class of problems, the equation-free framework has been developed to enable performing coarse-grained computations, based on short full model simulations. Each full model simulation should be initialized so that the full model state is consistent with the values of the observables and close to the slow manifold. To compute such an initial full model state, a class of constrained runs functional iterations was proposed (Gear and Kevrekidis, J. Sci. Comput. 25(1), 17–28, 2005; Gear et al., SIAM J. Appl. Dyn. Syst. 4(3), 711–732, 2005). The schemes in this class only use the full model simulator and converge, under certain conditions, to an approximation of the desired state on the slow manifold. In this article, we develop an implementation of the constrained runs scheme that is based on a (preconditioned) Newton-Krylov method rather than on a simple functional iteration. The functional iteration and the Newton-Krylov method are compared in detail using a lattice Boltzmann model for one-dimensional reaction-diffusion as the full model simulator. Depending on the parameters of the lattice Boltzmann model, the functional iteration may converge slowly or even diverge. We show that both issues are largely resolved by using the Newton-Krylov method, especially when a coarse grid correction preconditioner is incorporated.     

The impact of false warnings on partial and full lane departure warnings effectiveness and acceptance in car driving

In the past, lane departure warnings (LDWs) were demonstrated to improve driving behaviours during lane departures but little is known about the effects of unreliable warnings. This experiment focused on the influence of false warnings alone or in combination with missed warnings and warning onset on assistance effectiveness and acceptance. Two assistance unreliability levels (33 and 17%) and two warning onsets (partial and full lane departure) were manipulated in order to investigate interaction. Results showed that assistance, regardless unreliability levels and warning onsets, improved driving behaviours during lane departure episodes and outside of these episodes by favouring better lane-keeping performances. Full lane departure and highly unreliable warnings, however, reduced assistance efficiency. Drivers’ assistance acceptance was better for the most reliable warnings and for the subsequent warnings. The data indicate that imperfect LDWs (false warnings or false and missed warnings) further improve driving behaviours compared to no assistance.

Practitioner Summary: This study revealed that imperfect lane departure warnings are able to significantly improve driving performances and that warning onset is a key element for assistance effectiveness and acceptance. The conclusion may be of particular interest for lane departure warning designers.