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UAVLnQ: An Architecture for Security Analysis of Cyber-Physical Network Behavior in UAV Swarms
No full textInternational audienceThe design and validation of security architectures for Unmanned Aerial Vehicle (UAV) swarms, such as on-board Intrusion Detection Systems (IDS), require simulation platforms that can rapidly prototype complex cyber-physical scenarios. These platforms must simultaneously capture accurate flight dynamics and realistic network behavior while remaining scalable and reproducible. This paper introduces Unmanned Aerial Vehicle Link and Queue (UAVLnQ), an open-source architecture explicitly designed to address these challenges. It integrates ArduPilot Software-in-the-Loop (SITL) with ns-3 through a ZMQ middleware that guarantees synchronization between mobility control and packet-level events. UAVLnQ features an autopilot mission control system for defining and executing complex swarm behaviors. We demonstrate its capabilities in a leader-follower formation under representative cyber-attacks-including command injection, spoofing, and denial of service-providing a validated environment for the development and testing of novel security and resilience solutions. UAVLnQ thus establishes itself as a reproducible and extensible foundation for a new generation of UAV swarm networking and cyber-defense research.</div
Contesting scalability, organizing non-scalable worlds
No full textInternational audienceWhat are the implications of conceiving scalability as a key parameter of social orders and a driver of societal transformation? Alternatively, what does it mean to consider nonscalability? In this paper, we seek to put 'scalability' under arrest, unsettling its self-evidence and mapping its territorialized materialities from below. Scalability implies specific qualities of a scalable world, logics of action and purposes that have unattended implications, shaping a resourcified, simplified, and disposable world. Scalability replaces the plurality of social bonds with homogeneous organizational relationalities predicated on a vertical ontology, such as extraction, domination, and predation. By dissecting the system implied by scalability and attending to its unattended implications, we analyze the violence of scalability and describe how it contributes to the direct or mediated ruination of the world. On this basis, examining what politics of non-scalability in organization studies could mean, we explore some reparative organizational gestures for organizing and inhabiting non-scalable worlds: from celebrating non-innovation, maintenance, and dismantling, to cultivating heterogeneity, regenerating bonds, and embracing counter-performances through friction, sabotage, or even disappearance
Datasheets as ESP Genres: Translation and MT Use by Student-Engineers
No full textInternational audienceThis proposal contributes to a thematic panel on specialized genres, translation practices, and digital tools in English for Specific Purposes (ESP) teaching and learning in European higher education. It focuses on the datasheet, a key engineering genre that remains underexamined in ESP research. The study explores how student engineers use translation, both with and without machine translation (MT), to understand English language technical documents.The research takes place in an apprenticeship-based engineering program at the Conservatoire national des arts et métiers (CNAM) and its engineering school, EICNAM. This alternance context is relevant because students regularly work with datasheets in both coursework and workplace tasks. The dataset includes authentic English datasheets and French translations produced by students under two conditions: unaided translation and MT assisted translation.Analysis draws on an evaluation grid developed by Toudic et al. (2014), which compares learner translations according to criteria such as terminological accuracy, syntactic restructuring, information condensation, cohesion, and genre-related conventions. The grid helps identify recurring strategies and difficulties linked to the specific constraints of datasheet translation.The results show clear contrasts between the two translation modes. MT assisted translations generally improve lexical accuracy and terminological consistency. Unaided translations vary more in quality but show greater interpretation, reformulation, and genre awareness, although they impose higher cognitive demands.The paper discusses how translation and MT literacy can support ESP teaching for engineers. It argues that analytical tools such as the evaluation grid can foster critical engagement with MT output and a deeper understanding of professional genres. More broadly, the study contributes to ongoing discussions about how corpus based and translation-informed approaches can strengthen links between ESP research, classroom practice, and workplace communication
Diffusion-based Annealed Boltzmann Generators : benefits, pitfalls and hopes
No full textSampling configurations at thermodynamic equilibrium is a central challenge in statistical physics. Boltzmann Generators (BGs) tackle it by combining a generative model with a Monte Carlo (MC) correction step to obtain asymptotically unbiased samples from an unnormalized target. Most current BGs use classic MC mechanisms such as importance sampling, which both require tractable likelihoods from the backbone model and scale poorly in high-dimensional, multi-modal targets. We study BGs built on annealed Monte Carlo (aMC), which is designed to overcome these limitations by bridging a simple reference to the target through a sequence of intermediate densities. Diffusion models (DMs) are powerful generative models and have already been incorporated into aMC-based recalibration schemes via the diffusion-induced density path, making them appealing backbones for aMC-BGs. We provide an empirical meta-analysis of DM-based aMC-BGs on controlled multi-modal Gaussian mixtures (varying mode separation, number of modes, and dimension), explicitly disentangling inference effects from learning effects by comparing (i) a perfectly learned DM and (ii) a DM trained from data. Even with a perfect DM, standard integrations using only first-order stochastic denoising kernels fail systematically, whereas second-order denoising kernels can substantially improve performance when covariance information is available. We further propose a deterministic aMC integration based on first-order transport maps derived from DMs, which outperforms the stochastic first-order variant at higher computational cost. Finally, in the learned-DM setting, all DM-aMC variants struggle to produce accurate BGs; we trace the main bottleneck to inaccurate DM log-density estimation
Combination of ATLAS and CMS searches for Higgs boson pair production at TeV
No full textInternational audienceThis Letter presents a combination of searches for Higgs boson pair (HH) production performed by the ATLAS and CMS Collaborations using proton-proton collision data sets recorded at TeV during the Large Hadron Collider Run 2, corresponding to integrated luminosities ranging between 126 and 140 . The upper limit at the 95% confidence level on the total HH signal strength, defined as the ratio of the measured cross section to the SM prediction, corresponds to 2.5, with an expected value of 1.7 (2.8) assuming the absence (presence) of the standard model (SM) HH signal. The strength of the HH signal is measured to be relative to the SM prediction. The observed significance is found to be 1.1 standard deviations whereas 1.3 are expected for the SM HH signal. Constraints are set on the Higgs boson trilinear self-coupling and on the couplings of two Higgs bosons to two vector bosons, both normalized to the SM predictions and denoted as and , respectively. The observed individual constraints at the 95% confidence level are and , while the expected constraints assuming the presence of the SM HH signal are and
Polarization measurement of and baryons in Ne collisions at GeV
No full textInternational audienceThe first measurement of the polarization of charm baryons by the LHCb experiment recorded in fixed-target mode is presented. The polarization of baryons is studied in collisions of protons, at an energy of 2.51 TeV, incident on a gaseous target of neon, at a nucleon-nucleon center-of-mass energy of GeV. The world's first measurement of separate-charge polarizations for and baryons is performed, determining where the first uncertainty is statistical and the second systematic. The polarization is also measured in intervals of baryon transverse momentum and the Feynman- variable
Runtime Analysis of the Compact Genetic Algorithm on the LeadingOnes Benchmark
No full textInternational audienceThe compact genetic algorithm (cGA) is one of the simplest estimation-of-distribution algorithms (EDAs). Next to the univariate marginal distribution algorithm (UMDA)another simple EDA-, the cGA has been subject to extensive mathematical runtime analyses, often showcasing a similar or even superior performance to competing approaches. Surprisingly though, up to date and in contrast to the UMDA and many other heuristics, we lack a rigorous runtime analysis of the cGA on the LEADINGONES benchmark-one of the most studied theory benchmarks in the domain of evolutionary computation.We fill this gap in the literature by conducting a formal runtime analysis of the cGA on LEADINGONES. For the cGA's single parameter-called the hypothetical population size-at least polylogarithmically larger than the problem size, we prove that the cGA samples the optimum of LEADINGONES with high probability within a number of function evaluations quasi-linear in the problem size and linear in the hypothetical population size. For the best hypothetical population size, our result matches, up to polylogarithmic factors, the typical quadratic runtime that many randomized search heuristics exhibit on LEADINGONES. Our analysis exhibits some noteworthy differences in the working principles of the two algorithms which were not visible in previous works
A Novel Lightweight Multi-Scale Branching Encoder for Few-Shot Image Classification
No full textPreprint version. This manuscript has not yet been submitted to a journal.Few-Shot Learning (FSL) enables neural networks to learn from a small number of labeled examples. This paper presents the Sum12 Multi-Scale Branching Encoder, a lightweight convolutional architecture for Prototypical Networks that achieves near-state-of-the-art results without raising computational cost. The encoder integrates seven convolutional branches with kernel sizes whose sum equals 12, capturing multi-scale information within a unified representation. Initially evaluated on Omniglot, CIFAR-FS, FC100, and miniImageNet, it reliably outperforms most baseline encoders, especially in N-way, low-shot settings. The architecture has been shown to exceed standard benchmark accuracy in non-FSL training on the MedMNIST 2D meta dataset, which comprises of 12 medical image datasets. The Sum12 encoder achieves accuracy akin to or exceeding that of ResNet and AutoML systems while remaining under 7 MB
Understanding the role of turbulence and biofilm on low density microplastic dynamics: An experimental approach towards natural conditions
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Geometry of Sparsity-Inducing Norms
No full textInternational audienceSparse optimization seeks an optimal solution with few nonzero entries. To achieve this, it is common to add to the criterion a penalty term proportional to the -norm, which is recognized as the archetype of sparsity-inducing norms. In this approach, the number of nonzero entries is not controlled a priori. By contrast, in this paper, our motivation is to find an optimal solution with at most~ nonzero coordinates (or for short, -sparse vectors), where is a given sparsity threshold (or ``sparsity budget''). For this purpose, we study the class of generalized -support dual~norms that arise from any given so-called source norm. When added as a penalty term, we provide conditions under which such generalized -support dual~norms promote -sparse solutions. The result follows from an analysis of the exposed faces of closed convex sets generated by -sparse vectors, and of how primal support identification can be deduced from dual information. Finally, we study some of the geometric properties of the unit balls for the -support dual~norms and their dual norms when the source norm belongs to the family of -norms. In particular, we show a striking structural property: every proper face of the unit balls for the -support dual~norms is a hypersimplex, i.e., the convex hull of -valued points with the same \lzero-norm