Publications

A Myhill-Nerode theorem for higher-dimensional automata

Abstract

We establish a Myhill-Nerode type theorem for higher-dimensional automata (HDAs), stating that a language is regular precisely if it has finite prefix quotient. HDAs extend standard automata with additional structure, making it possible to distinguish between interleavings and concurrency. We also introduce deterministic HDAs and show that not all HDAs are determinizable, that is, there exist regular languages that cannot be recognised by a deterministic HDA. Using our theorem, we develop an internal characterisation of deterministic languages.

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Catoids and modal convolution algebras

Abstract

We show how modal quantales arise as convolution algebras $Q^X$ of functions from catoids $X$, that is, multisemigroups with a source map $\ell$ and a target map $r$, into modal quantales $Q$, which can be seen as weight or value algebras. In the tradition of boolean algebras with operators we study modal correspondences between algebraic laws in $X$, $Q$ and $Q^X$. The class of catoids we introduce generalises Schweizer and Sklar’s function systems and object-free categories to a setting isomorphic to algebras of ternary relations, as they are used for boolean algebras with operators and substructural logics. Our results provide a generic construction of weighted modal quantales from such multisemigroups. It is illustrated by many examples. We also discuss how these results generalise to a setting that supports reasoning with stochastic matrices or probabilistic predicate transformers.

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Non-fungible tokens: A review

Abstract

Non Fungible Tokens (NFTs) are among the most promising technologies that have emerged in recent years. NFTs enable the efficient verification and ownership management of digital assets and therefore, offer the means to secure them. NFT is similar to blockchain that was first used by the cryptocurrency and then by numerous other technologies. At first, the NFT concept attracted the attention of the digital art community. However, NFT has the potential to enable a plethora of different applications and sce We present a review of the NFT technology. We describe the basic components of NFTs and how NFTs work. Then, we present and discuss the different applications of the NFTs. Finally, we discuss various challenges that the NFT technology must address in the future.

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Why is the winner the best?

Abstract

International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to investigating what can be learnt from these competitions. Do they really generate scientific progress? What are common and successful participation strategies? What makes a solution superior to a competing method? To address this gap in the literature, we performed a multi-center study with all 80 competitions that were conducted in the scope of IEEE ISBI 2021 and MICCAI 2021. Statistical analyses performed based on comprehensive descriptions of the submitted algorithms linked to their rank as well as the underlying participation strategies revealed common characteristics of winning solutions. These typically include the use of multi-task learning (63%) and/or multi-stage pipelines (61%), and a focus on augmentation (100%), image preprocessing (97%), data curation (79%), and postprocessing (66%). The “typical” lead of a winning team is a computer scientist with a doctoral degree, five years of experience in biomedical image analysis, and four years of experience in deep learning. Two core general development strategies stood out for highly-ranked teams: the reflection of the metrics in the method design and the focus on analyzing and handling failure cases. According to the organizers, 43% of the winning algorithms exceeded the state of the art but only 11% completely solved the respective domain problem. The insights of our study could help researchers (1) improve algorithm development strategies when approaching new problems, and (2) focus on open research questions revealed by this work.

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Leveraging neural koopman operators to learn continuous representations of dynamical systems from scarce data

Abstract

Over the last few years, several works have proposed deep learning architectures to learn dynamical systems from observation data with no or little knowledge of the underlying physics. A line of work relies on learning representations where the dynamics of the underlying phenomenon can be described by a linear operator, based on the Koopman operator theory. However, despite being able to provide reliable long-term predictions for some dynamical systems in ideal situations, the methods proposed so far have limitations, such as requiring to discretize intrinsically continuous dynamical systems, leading to data loss, especially when handling incomplete or sparsely sampled data. Here, we propose a new deep Koopman framework that represents dynamics in an intrinsically continuous way, leading to better performance on limited training data, as exemplified on several datasets arising from dynamical systems.

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A benchmark for toxic comment classification on civil comments dataset

Abstract

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Electricity price forecasting based on order books: A differentiable optimization approach

Abstract

We consider day-ahead electricity price forecasting on the European market. In this market, participants can offer electricity for sale or purchase for a specific price by submitting overnight orders. Market operators determine the market clearing price – the price at which the amount of electricity supplied equals the amount of electricity demanded – using the Euphemia balancing algorithm. euphemia is a quadratic optimization problem that maximizes the social welfare defined as the sum of the supplier surplus and consumer surplus while ensuring a null energy balance. This mechanism deeply influences the price calculation, but has so far been little considered in electricity price forecasting algorithms. Existing models are generally based on identifying relationships between exogenous characteristics (consumption and production forecasts) and the market clearing price to be predicted. A few studies have examined the euphemia mechanism during prediction, by doing costly manual transformations on order books. In this article, we overcome this limitation by considering the pricing mechanism during model training. For this, we use a predict-and-optimize strategy with differentiable optimization. We design a fully differentiable and scalable solving method for the euphemia optimization problem and apply it on real-life data from the European Power Exchange (EPEX). We design different model architectures using our differentiable solver and empirically study the impact of taking into account the optimal calculation of prices within the training of the neural network.

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Peripheral nervous system responses to food stimuli: Analysis using data science approaches

Abstract

In the field of food, as in other fields, the measurement of emotional responses to food and their sensory properties is a major challenge. In the present protocol, we propose a step-by-step procedure that allows a physiological description of odors, aromas, and their hedonic properties. The method rooted in subgroup discovery belongs to the field of data science and especially data mining. It is still little used in the field of food and is based on a descriptive modeling of emotions on the basis of human physiological responses.

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Actes de l’atelier gestion et analyse des données spatiales et temporelles

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Des joueurs sous écrou : Jeux d’argent, carrière déviante et criminalité dans la population carcérale française

Abstract

La pratique des jeux d’argent est sans doute l’activité ludique la plus contrôlée qui soit, notamment parce qu’elle est associée à des problèmes d’addiction, de délinquance et de criminalité. En effet, si le développement actuel des jeux d’argent contribue à la banalisation de cette pratique, elle n’en demeure pas moins considérée comme potentiellement néfaste voire condamnable, au point de conduire directement ou indirectement certains joueurs en prison. L’enquête de terrain que nous avons menée avec les populations carcérales de deux établissements pénitentiaires franais, permet d’abord de relativiser la place que le jeu occupe dans la trajectoire biographique des joueurs détenus, celui-ci étant le plus souvent peru comme une activité récréative et socialisatrice (y compris en détention). En revanche, une proportion non négligeable d’entre eux considèrent que cette pratique a déjà provoqué dans leur vie des probl èmes d’addiction et que ces probl èmes ont un lien direct ou indirect avec le motif de leur incarcération. Grâce aux entretiens réalisés avec ces derniers, nous découvrirons comment ce lien se noue et se dénoue dans la construction de leur récit de vie en tant que joueurs, tout en cherchant à saisir les conditions d’entrée dans ce type de carrière déviante.

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