Cloud computing is a method for accessing and managing computing resources over the internet, providing flexibility, scalability, and cost-efficiency. Cloud computing relies more and more on OS-level virtualization tools such as Docker and Podman, enabling users to create and run containers, which are widely used for application management. Given its significance in cloud infrastructures, it is crucial to have a better understanding of OS-level virtualization performance, especially in tasks related to container management (ex: creation, destruction). In this paper, we conducted benchmarking tests on Docker and Podman to evaluate their performance in various container management scenarios and with different image sizes. The results revealed that Podman excels in quickly instantiating small-sized containers, while Docker demonstrates superior performance with larger-sized containers.

The James Webb Space Telescope is performing beyond our expectations. Its Near Infrared Spectrograph (NIRSpec) provides versatile spectroscopic capabilities in the 0.6-5.3 micrometre wavelength range, where a new window is opening for studying Trans-Neptunian objects in particular. We propose a spectral extraction method for NIRSpec fixed slit observations, with the aim of meeting the superior performance on the instrument with the most advanced data processing. We applied this method on the fixed slit dataset of the guaranteed-time observation program 1231, which targets Plutino 2003 AZ84. We compared the spectra we extracted with those from the calibration pipeline.

With Artificial Intelligence (AI) influencing the decision-making process of sensitive applications such as Face Verification, it is fundamental to ensure the transparency, fairness, and accountability of decisions. Although Explainable Artificial Intelligence (XAI) techniques exist to clarify AI decisions, it is equally important to provide interpretability of these decisions to humans. In this paper, we present an approach to combine computer and human vision to increase the explanation’s interpretability of a face verification algorithm. In particular, we are inspired by the human perceptual process to understand how machines perceive face’s human-semantic areas during face comparison tasks. We use Mediapipe, which provides a segmentation technique that identifies distinct human-semantic facial regions, enabling the machine’s perception analysis. Additionally, we adapted two model-agnostic algorithms to provide human-interpretable insights into the decision-making processes.

Structure-Activity Relationships is a critical aspect of drug design. It enables us to examine ligand interactions and performances towards specific targets, then to design effective drugs for treating diseases or improving existing medical therapies. In this context, we specifically study the activity of ligands towards kinases using the BCR-ABL dataset. The work is dedicated to introduce a refinement method for the activity of molecules. Instead of considering anity as a binary activity, a molecule being either active or inactive, the compounds were partitioned into 4 classes according to their activity: very active, moderately active, slightly active, inactive. This activity is later used to evaluate molecular descriptors called topological pharmacophores [1]. These pharmacophores provide essential information by representing the key structural features of a molecule. Their quality is determined by measuring their "growth-rate" which corresponds to the ratio of active molecules over inactive ones, among the molecules supported by the pharmacophore. In our work, the calculation of the growth-rate is based on the classes of activity that we have created. Consequently, we will obtain three measurements of the growth rate, each one being related to a class of activity. In addition, we proposed to convert the new information of the quality of the pharmacophores into a visual representation called "The Pharmacophore Network" [2]. The latter is a graph whose nodes represent the pharmacophores and edges represent a graph-edit distance that separates them. Our goal was to structure more finely the pharmacophore space and to be able to detect visually interesting areas that can be explored. For this purpose, we integrated colors in this Pharmacophore Network, where each color refers to a class of activity.

Activation of the autonomic nervous system is a primary characteristic of human hedonic responses to sensory stimuli. For smells, general tendencies of physiological reactions have been described using classical statistics. However, these physiological variations are generally not quantified precisely; each psychophysiological parameter has very often been studied separately and individual variability was not systematically considered. The current study presents an innovative approach based on data mining, whose goal is to extract knowledge from a dataset. This approach uses a subgroup discovery algorithm which allows extraction of rules that apply to as many olfactory stimuli and individuals as possible. These rules are described by intervals on a set of physiological attributes. Results allowed both quantifying how each physiological parameter relates to odor pleasantness and perceived intensity but also describing the participation of each individual to these rules. This approach can be applied to other fields of affective sciences characterized by complex and heterogeneous datasets.

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.

In this paper we present a layered approach for multi-agent control problem, decomposed into three stages, each building upon the results of the previous one. First, a high-level plan for a coarse abstraction of the system is computed, relying on parametric timed automata augmented with stopwatches as they allow to efficiently model simplified dynamics of such systems. In the second stage, the high-level plan, based on SMT-formulation, mainly handles the combinatorial aspects of the problem, provides a more dynamically accurate solution. These stages are collectively referred to as the SWA-SMT solver. They are correct by construction but lack a crucial feature: they cannot be executed in real time. To overcome this, we use SWA-SMT solutions as the initial training dataset for our last stage, which aims at obtaining a neural network control policy. We use reinforcement learning to train the policy, and show that the initial dataset is crucial for the overall success of the method.

Hierarchical representations are very convenient tools when working with images. Among them, the $\alpha$-tree is the basis of several powerful hierarchies used for various applications such as image simplifi- cation, object detection, or segmentation. However, it has been demon- strated that these tasks are very sensitive to the noise corrupting the image. While the quality of some $\alpha$-tree applications has been studied, including some with noisy images, the noise impact on the whole struc- ture has been little investigated. Thus, in this paper, we examine the structure of $\alpha$-trees built on images corrupted by some noise with re- spect to the noise level. We compare its effects on constant and natural images, with different kinds of content, and we demonstrate the relation between the noise level and the distribution of every $\alpha$-tree node depth. Furthermore, we extend this study to the node persistence under a given energy criterion, and we propose a novel energy definition that allows assessing the robustness of a region to the noise.