Wilfrid Besnardeau et Francine Mora-Lebrun (éd.), Le Roman d’Énéas

Le Roman d’Éneas a été publié une première fois en 1891, suivant la méthode stemmatique et visant franchement la reconstruction (Jean-Jacques Salverda de Grave [éd.], Énéas, Halle, Niemeyer [Bibliotheca Normannica, 4], 1891), puis une seconde fois par le même savant, à la suite d’un revirement méthodologique spectaculaire, dans la collection fondée par Mario Roques (Jean-Jacques Salverda de Grave [éd.], Énéas. Roman du xiie siècle, Paris, Honoré Champion [Classiques français du Moyen Âge, 44 ..

Entropy-Based Logic Explanations of Neural Networks

Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods as they leverage human-understandable symbols (i.e. concepts) to predict class memberships. However, most of these approaches focus on the identification of the most relevant concepts but do not provide concise, formal explanations of how such concepts are leveraged by the classifier to make predictions. In this paper, we propose a novel end-to-end differentiable approach enabling the extraction of logic explanations from neural networks using the formalism of First-Order Logic. The method relies on an entropy-based criterion which automatically identifies the most relevant concepts. We consider four different case studies to demonstrate that: (i) this entropy-based criterion enables the distillation of concise logic explanations in safety-critical domains from clinical data to computer vision; (ii) the proposed approach outperforms state-of-the-art white-box models in terms of classification accuracy and matches black box performances

Entropy-based Logic Explanations of Neural Networks

Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods as they leverage human-understandable symbols (i.e. concepts) to predict class memberships. However, most of these approaches focus on the identification of the most relevant concepts but do not provide concise, formal explanations of how such concepts are leveraged by the classifier to make predictions. In this paper, we propose a novel end-to-end differentiable approach enabling the extraction of logic explanations from neural networks using the formalism of First-Order Logic. The method relies on an entropy-based criterion which automatically identifies the most relevant concepts. We consider four different case studies to demonstrate that: (i) this entropy-based criterion enables the distillation of concise logic explanations in safety-critical domains from clinical data to computer vision; (ii) the proposed approach outperforms state-of-the-art white-box models in terms of classification accuracy

Entropy-Based Logic Explanations of Neural Networks

Explainable artificial intelligence has rapidly emerged since lawmakers have started requiring interpretable models for safety-critical domains. Concept-based neural networks have arisen as explainable-by-design methods as they leverage human-understandable symbols (i.e. concepts) to predict class memberships. However, most of these approaches focus on the identification of the most relevant concepts but do not provide concise, formal explanations of how such concepts are leveraged by the classifier to make predictions. In this paper, we propose a novel end-to-end differentiable approach enabling the extraction of logic explanations from neural networks using the formalism of First-Order Logic. The method relies on an entropy-based criterion which automatically identifies the most relevant concepts. We consider four different case studies to demonstrate that: (i) this entropy-based criterion enables the distillation of concise logic explanations in safety-critical domains from clinical data to computer vision; (ii) the proposed approach outperforms state-of-the-art white-box models in terms of classification accuracy and matches black box performances

seismic assessment of masonry towers by means of nonlinear static procedures

Abstract The paper presents some FE analyses conducted through advanced techniques for the seismic assessment of existing masonry towers. Traditionally, masonry towers were conceived to withstand vertical loads only, but in recent years national and international standards have imposed the evaluation of their structural performance even in presence of horizontal forces. Eight towers are chosen deliberately very different in geometry and a number of linear and non-linear structural analyses are performed. Their behavior under horizontal loads (mimicking earthquake actions) is investigated by means of traditional static nonlinear procedures. Modal participations in pushover analyses are considered in order to have an insight into the role played by the higher modes in the local and global behavior of the structure. A non-conventional pushover approach, hereafter called EPA-Extended Pushover Analysis-based on modal combinations is utilized. Some comparative analyses between standard and non-conventional pushover are reported. It is found that EPA is reasonable when the contribution of higher modes is significant. EPA turns out to have a reasonable resemblance in terms of capacity and deformation pattern with non-linear dynamic results, highlighting the significant role of the higher modes in such structures, where for instance the presence of the bell cell can play a certain role. Further vulnerability considerations for the towers deduced by the performed analyses are also reported

Interpretable Neural-Symbolic Concept Reasoning

Deep learning methods are highly accurate, yet their opaque decision process prevents them from earning full human trust. Concept-based models aim to address this issue by learning tasks based on a set of human-understandable concepts. However, state-of-the-art concept-based models rely on high-dimensional concept embedding representations which lack a clear semantic meaning, thus questioning the interpretability of their decision process. To overcome this limitation, we propose the Deep Concept Reasoner (DCR), the first interpretable concept-based model that builds upon concept embeddings. In DCR, neural networks do not make task predictions directly, but they build syntactic rule structures using concept embeddings. DCR then executes these rules on meaningful concept truth degrees to provide a final interpretable and semantically-consistent prediction in a differentiable manner. Our experiments show that DCR: (i) improves up to +25% w.r.t. state-of-the-art interpretable concept-based models on challenging benchmarks (ii) discovers meaningful logic rules matching known ground truths even in the absence of concept supervision during training, and (iii), facilitates the generation of counterfactual examples providing the learnt rules as guidance

Fragments Generated During Liquid Hydrogen Tank Explosions

Liquid hydrogen (LH2) may be employed to transport large quantities of pure hydrogen or be stored onboard of ships, airplanes and trains fuelled by hydrogen, thanks to its high density compared to gaseous compressed hydrogen. LH2 is a cryogenic fluid with an extremely low boiling point (-253°C at atmospheric pressure) that must be stored in double-walled vacuum insulated tanks to limit the boil-off formation. There is limited knowledge on the consequences of LH2 tanks catastrophic rupture. In fact, the yield of the consequences of an LH2 tank explosion (pressure wave, fragments and fireball) depend on many parameters such as tank dimension, filling degree, and tank internal conditions (temperature and pressure) prior the rupture. Only two accidents provoked by the rupture of an LH2 tank occurred in the past and a couple of experimental campaigns focussed on this type of accident scenario were carried out for LH2. The aim of this study is to analyse one of the LH2 tank explosion consequences namely the fragments. The longest horizontal and vertical ranges of the fragments thrown away from the blast wave are estimated together with the spatial distribution around the tank. Theoretical models are adopted in this work and validated with the experimental results. The proposed models can aid the risk analysis of LH2 storage technologies and provide critical insights to plan a prevention and mitigation strategy and improve the safety of hydrogen applications

Modelling of Fireballs Generated After the Catastrophic Rupture of Hydrogen Tanks

The interest towards hydrogen skyrocketed in the last years. Thanks to its potential as an energy carrier, hydrogen will be soon handled in public and densely populated areas. Therefore, accurate models are necessary to predict the consequences of unwanted scenarios. These new models should be employed in the consequence analysis, a phase of risk assessment, and thus aid the selection, implementation, and optimization of effective risk-reducing measures. This will increase safety of hydrogen technologies and therefore favour their deployment on a larger scale. Hydrogen is known to be an extremely flammable gas with a low radiation flame compared to hydrocarbons. However, luminous fireballs were generated after the rupture of both compressed gaseous and liquid hydrogen tanks in many experiments. Moreover, it was demonstrated that conventional empirical correlations, initially developed for hydrocarbon fuels, underestimate both dimension and duration of hydrogen fireballs recorded during small-scale tests (Ustolin and Paltrinieri, 2020). The aim of this study is to obtain an analysis of hydrogen fireballs to provide new critical insights for consequence analysis. A comparison among different correlations is conducted when predicting fireball characteristics during the simulation of past experiments where both gaseous and liquid hydrogen tanks were intentionally destroyed. All the models employed in this study are compared with the experimental results for validation purposes. Specific models designed for hydrogen can support the design of hydrogen systems and increasing their safety and promote their future distribution