Toward a knowledge representation model dedicated to the semantic analysis of the sentence

Ce rapport a été rédigé en octobre 2005 mais n'a été publié en tant que rapport INRIA qu'en juillet 2006Ce travail se situe dans le cadre de l'étude de la sémantique du langage naturel. Il s'agit d'une reformulation précise et automatisable du Lexique Génératif de Pustejovsky. Nous proposons un modèle lexical simultanément descriptif de la structure sémantique interne des mots et applicable de manière automatique à la suite d'une analyse syntaxique classique, par exemple celle des grammaires catégorielles. Notre formalisation des règles et nos algorithmes qui permettent de les appliquer sont illustrés par le traitement de la reconstruction métonymique et des usages ambigus de la préposition “avec”. (Ce rapport a été rédigé en octobre 2005 mais n'a été publié qu'en juillet 2006.

Medical Device Design Education: Identifying Problems Through Observation and Hands-On Training

Experiential learning, which may include hands-on learning paired with observation and reflection, has been applied in several industries; however, the impact of experiential learning in design education is not well known. We investigated how the type of simulation-based learning could affect the acquisition of knowledge and the ability to synthesize that understanding into insights for medical design innovation. One workshop included observational learning and the other experiential learning with hands-on training. Each course included 14-16 multidisciplinary undergraduate and graduate students. During both workshops, we measured student comprehension of two procedures— infant resuscitation and management of maternal hemorrhage. We focused on the first two phases of design thinking: “Understanding” and “Defining the Problems”. Although the course focused on “medical device design”, we encouraged students to look beyond the tool to imagine how their design change could impact the entire system. We did not find a significant difference between the scores given to students in the two courses by industry experts. Although the quality of the ideas and execution were similar between both workshops, the instructors noticed that the integration of hands-on training into the second workshop created a higher level of excitement in the class. The methodology and the approach adopted may be relevant to many design problems. In order to better understand the impact of observational learning versus hands-on training, both workshops could be expanded into full quarter classes that allow students to expand their design thinking skills to prototype and test their ideas in the real world

The effect of H₂S on internal dry reforming in biogas fuelled solid oxide fuel cells

Internal dry reforming of methane is envisaged as a possibility to reduce on capital and operation costs of biogas fuelled solid oxide fuel cells (SOFCs) system by using the CO2 present in the biogas. Due to envisaged internal dry reforming, the requirement for biogas upgrading becomes obsolete, thereby simplifying the system complexity and increasing its technology readiness level. However, impurities prevailing in biogas such as H2S have been reported in literature as one of the parameters which affect the internal reforming process in SOFCs. This research has been carried out to investigate the effects of H2S on internal dry reforming of methane on nickel-scandia-stabilised zirconia (Ni-ScSZ) electrolyte supported SOFCs. Results showed that at 800°C and a CH4:CO2 ratio of 2:3, H2S at concentrations as low as 0.125 ppm affects both the catalytic and electric performance of a SOFC. At 0.125 ppm H2S concentration, the CH4 reforming process is affected and it is reduced from over 95% to below 10% in 10 h. Therefore, future biogas SOFC cost reduction seems to become a trade-off between biogas upgrading for CO2 removal and biogas cleaning of impurities to facilitate efficient internal dry reforming

Herodiade

De cada obra s'ha digitalitzat un programa sencer. De la resta s'han digitalitzat les parts que són diferents.Director: Henry DefosseEmpresa: Juan Mestres Calve

Toward a knowledge representation model dedicated to the semantic analysis of the sentence

Ce rapport a été rédigé en octobre 2005 mais n'a été publié en tant que rapport INRIA qu'en juillet 2006Ce travail se situe dans le cadre de l'étude de la sémantique du langage naturel. Il s'agit d'une reformulation précise et automatisable du Lexique Génératif de Pustejovsky. Nous proposons un modèle lexical simultanément descriptif de la structure sémantique interne des mots et applicable de manière automatique à la suite d'une analyse syntaxique classique, par exemple celle des grammaires catégorielles. Notre formalisation des règles et nos algorithmes qui permettent de les appliquer sont illustrés par le traitement de la reconstruction métonymique et des usages ambigus de la préposition “avec”. (Ce rapport a été rédigé en octobre 2005 mais n'a été publié qu'en juillet 2006.

Manon

De cada obra s'ha digitalitzat un programa sencer. De la resta s'han digitalitzat les parts que són diferents.Director: Henry DefosseEmpresa: Juan Mestres Calve

Leveraging Analog Quantum Computing with Neutral Atoms for Solvent Configuration Prediction in Drug Discovery

We introduce quantum algorithms able to sample equilibrium water solvent molecules configurations within proteins thanks to analog quantum computing. To do so, we combine a quantum placement strategy to the 3D Reference Interaction Site Model (3D-RISM), an approach capable of predicting continuous solvent distributions. The intrinsic quantum nature of such coupling guarantees molecules not to be placed too close to each other, a constraint usually imposed by hand in classical approaches. We present first a full quantum adiabatic evolution model that uses a local Rydberg Hamiltonian to cast the general problem into an anti-ferromagnetic Ising model. Its solution, an NP-hard problem in classical computing, is embodied into a Rydberg atom array Quantum Processing Unit (QPU). Following a classical emulator implementation, a QPU portage allows to experimentally validate the algorithm performances on an actual quantum computer. As a perspective of use on next generation devices, we emulate a second hybrid quantum-classical version of the algorithm. Such a variational quantum approach (VQA) uses a classical Bayesian minimization routine to find the optimal laser parameters. Overall, these Quantum-3D-RISM (Q-3D-RISM) algorithms open a new route towards the application of analog quantum computing in molecular modelling and drug design

A two-phase method for extracting explanatory arguments from Bayesian networks

Errors in reasoning about probabilistic evidence can have severe consequences. In the legal domain a number of recent miscarriages of justice emphasises how severe these consequences can be. These cases, in which forensic evidence was misinterpreted, have ignited a scientific debate on how and when probabilistic reasoning can be incorporated in (legal) argumentation. One promising approach is to use Bayesian networks (BNs), which are well-known scientific models for probabilistic reasoning. For non-statistical experts, however, Bayesian networks may be hard to interpret. Especially since the inner workings of Bayesian networks are complicated, they may appear as black box models. Argumentation models, on the contrary, can be used to show how certain results are derived in a way that naturally corresponds to everyday reasoning. In this paper we propose to explain the inner workings of a BN in terms of arguments. We formalise a two-phase method for extracting probabilistically supported arguments from a Bayesian network. First, from a Bayesian network we construct a support graph, and, second, given a set of observations we build arguments from that support graph. Such arguments can facilitate the correct interpretation and explanation of the relation between hypotheses and evidence that is modelled in the Bayesian network