Plants perform in a specific Eh–pH spectrum and they rely on various processes to ensure their homeostasis, which plays a central role in their defense. The effects of multiple stresses, all translated into oxidative stress into the plant, and the capacity of the latter to respond to these stresses results in specific Eh–pH states in plants. We reviewed plant-invertebrate pests and plant-pathogens interactions under a Eh–pH homeostasis perspective by extensively analyzing the literature, which converges and supports a set of hypotheses. We report examples showing how the development and attacks of pests are correlated to spatio-temporal variations of Eh–pH in plants. We provide evidence-based discussion on how Eh–pH homeostasis can open a new perspective on plant health, and help unravel and disentangle the many Genotype x Environment x Management x Pest and Pathogen interactions. We propose an original perspective on energy allocation and growth-defense tradeoff by plants based on the Eh–pH homeostasis model. Finally, we show how Eh–pH conditions in the rhizosphere are the results of multiple interactions between the root system and microorganisms. Based on this, we hypothesize that soil suppressiveness is derived from soil structure leading to diverse Eh–pH niches that harbor a diversity of microorganisms. The Eh–pH homeostasis model proposed herein is central to soil and plant health. An Eh–pH perspective could become a very powerful tool to develop a “one health approach” unifying a large range of bio-physical processes in a very coherent and consistent manner.

Convolutional neural networks (CNNs) are deep learning frameworks which are well known for their notable performance in classification tasks. Hence, many skeleton-based action recognition and segmentation (SBARS) algorithms benefit from them in their designs. However, a shortcoming of such applications is the general lack of spatial relationships between the input features in such data types. Besides, non-uniform temporal scalings are a common issue in skeleton-based data streams which leads to having different input sizes even within one specific action category. In this work, we propose a novel deep-aligned convolutional neural network (DACNN) to tackle the above challenges for the particular problem of SBARS. Our network is designed by introducing a new type of filters in the context of CNNs which are trained based on their alignments to the local subsequences in the inputs. These filters result in efficient predictions as well as learning interpretable patterns in the data. Also, our DACNN framework can incrementally expand its deep structure based on the learning progress, which makes it flexible regarding different SBARS datasets. We empirically evaluate our framework on real-world benchmarks showing that the proposed DACNN algorithm obtains a competitive performance compared to the state of the art while benefiting from a less complicated yet more interpretable model.

Convolutional neural networks (CNNs) are deep learning frameworks which are well-known for their notable performance in classification tasks. Hence, many skeleton-based action recognition and segmentation (SBARS) algorithms benefit from them in their designs. However, a shortcoming of such applications is the general lack of spatial relationships between the input features in such data types. Besides, non-uniform temporal scalings is a common issue in skeleton-based data streams which leads to having different input sizes even within one specific action category. In this work, we propose a novel deep-aligned convolutional neural network (DACNN) to tackle the above challenges for the particular problem of SBARS. Our network is designed by introducing a new type of filters in the context of CNNs which are trained based on their alignments to the local subsequences in the inputs. These filters result in efficient predictions as well as learning interpretable patterns in the data. We empirically evaluate our framework on real-world benchmarks showing that the proposed DACNN algorithm obtains a competitive performance compared to the state-of-the-art while benefiting from a less complicated yet more interpretable model.

L’Europe est dans l’actualite a la fois pour des raisons de commemoration et pour l’urgence de reflexions nouvelles sur son avenir. Les questions budgetaires et financieres sont au centre de nombreuses problematiques (souverainete, integration, solidarite, harmonisation des politiques, avenir de l’euro…). M. Jean Arthuis, ancien ministre de l’Economie et des Finances de 1995 a 1997, et actuellement president de la commission du budget du Parlement europeen, nous livre ses reflexions originales, sinceres et decapantes sur le budget europeen.

In the hospitality industry, an allotment is a block of pre-negociated “rooms” which have been bought by a tour operator. In the context of campsites, allotments represent a significant share of the mobile homes sales. For the campsite owner, dealing with tour operator allotment requests is a poisoned chalice. On one hand these pre-booked sales are more or less a guarantee of selling a good share of its inventory. On the other hand, the discount level is so high that selling the whole inventory through allotments could potentially ruin the business. Hence, a balance must be found between allotment contracts and estimated direct sales to final customers (at full price, or lightly discounted price). The purpose of the present paper is to show that the stochastic optimisation problem at stake is highly combinatorial and that algorithmic approaches relying on continuous relaxations of the demand (“bid price”) behave poorly. For multi-site allotment optimisation with service level requirements from tour operators, we developed a Lagrange decomposition technique based on local Markov Decision Process solvers that outperforms classic "fluid displacement" approaches. We provide experimental results on instances with 200 campsites 20,000 mobile homes and 15 tour operators inspired from a leading European actor of the campsite industry.

This chapter deals with the issues associated with the autonomy of vehicle fleets, as well as some of the dimensions provided by an Artificial Intelligence (AI) solution. This presentation is developed using the example of a suppression of enemy air defense mission carried out by a group of Unmanned Combat Air Vehicles (UCAV). The environment of the Mission Management System (MMS) includes the theatre of operations, vehicle sub-systems and the MMS of other UCAV. An MMS architecture, organized around a database, including reactive and deliberative layers is described in detail. The deliberative layer includes a distributed mission planner developed using constraint programming and an agent framework. Experimental results demonstrate that the MMS is able, in a bounded time, to carry out missions, to activate the contingent behaviors, to decide whether to plan or not. Some research directions remain open in this application domain of AI.

Euroview Animation is developing a methodology for teaching CGI within the Communications Department of University of Navarre based on the participation of the students in actual productions. We base our educational methodology in the real hands-on experience: our students learn through their participation in the production of a real feature film. The production process of the film inspires and determines the learning process of our students. We consider that our project can be valuable for other educators in CGI because: -our method has real consequences: our first feature film, Flying Heroes, will come out in theaters in 2008 -as soon as we know, this is the first experience of this requirements in a university and in the industry -this kind of project offers many possibilities of collaboration between universities.

This chapter deals with the issues associated with the autonomy of vehicle fleets, as well as some of the dimensions provided by an Artificial Intelligence (AI) solution. This presentation is developed using the example of a suppression of enemy air defense mission carried out by a group of Unmanned Combat Air Vehicles (UCAV). The environment of the Mission Management System (MMS) includes the theatre of operations, vehicle sub-systems and the MMS of other UCAV. An MMS architecture, organized around a database, including reactive and deliberative layers is described in detail. The deliberative layer includes a distributed mission planner developed using constraint programming and an agent framework. Experimental results demonstrate that the MMS is able, in a bounded time, to carry out missions, to activate the contingent behaviors, to decide whether to plan or not. Some research directions remain open in this application domain of AI.