泰雷兹集团

Security surveillance is characterised by substantial cognitive challenges to operators. Scantracker is a mixed-reality gaze-aware support tool that alerts surveillance operators to neglected cameras, attentional tunnelling, and vigilance decrements. Initial research efforts were conducted in simulated environments to examine the effects of Scantracker on surveillance performance; however, this tool has yet to be deployed and tested in a real-world operational environment. In the current study, we tested Scantracker in an airport operations centre to assess the feasibility of its integration and to collect expert feedback regarding its operational relevance. Operators used Scantracker voluntarily during their work shift, while gaze data enabled system notifications. They provided ratings on perceived utility, workload and ergonomic quality along with qualitative feedback on their experience. The pattern of results highlights the potential of Scantracker to support surveillance operators and demonstrates the value of user-centred field testing for developing intelligent monitoring assistants.

With the support of the national program on measurements, standards, and evaluation of quantum technologies MetriQs-France, a part of the French national quantum strategy, the BACQ project is dedicated to application-oriented benchmarks for quantum computing. The consortium gathering THALES, EVIDEN, an Atos business, CEA, CNRS, TERATEC, and LNE aims at establishing performance evaluation criteria of reference, meaningful for industry users.

This paper addresses key challenges in the development of autonomous landing systems, focusing on dataset limitations for supervised training of Machine Learning (ML) models for object detection. Our main contributions include: (1) Enhancing dataset diversity, by advocating for the inclusion of new sources such as BingMap aerial images and Flight Simulator, to widen the generation scope of an existing dataset generator used to produce the dataset LARD; (2) Refining the Operational Design Domain (ODD), addressing issues like unrealistic landing scenarios and expanding coverage to multi-runway airports; (3) Benchmarking ML models for autonomous landing systems, introducing a framework for evaluating object detection subtask in a complex multi-instances setting, and providing associated open-source models as a baseline for AI models' performance.

We introduce NetDiff, a node-conditioned denoising diffusion model that generates directional link topologies and a two-slot transmit/receive parity for mobile ad hoc networks. Directional antennas can yield high throughput but require globally consistent link decisions under sector, interference, connectivity, and half-duplex constraints. NetDiff improves global coherence with Absolute Cross-Attentive Modulation (ACAM) tokens, which provide permutation-invariant global signals and help the model match graph-level counts (e.g., density and sector usage). We also propose partial diffusion to update an existing topology with a small number of denoising steps, enabling fast reconfiguration under mobility. NetDiff reaches over 95 \% of target performance with constant inference time, outperforms heuristic and omnidirectional baselines, and improves over a strong diffusion graph-transformer baseline on key metrics.

This paper proposes representing finite-energy signals observed within a given bandwidth as parameters of a probability distribution and employing the information-geometric framework to compute the Fisher–Rao distance between these signals, considered as distributions. The observations are described by their discrete Fourier transforms, which are modelled as complex Gaussian vectors with known diagonal covariance matrices and parametrised means. These parameters define a coordinate system on a statistical manifold. We investigate the possibility of deriving closed-form expressions for the Fisher–Rao distance. We employ established results from the Riemannian geometry of the multivariate normal model and extend the analysis to complex Gaussian variables representing the finite-energy signal observations. Expressions for the Christoffel symbols and the geodesic tensor equations in the Fisher metric are derived, leading to geodesic equations expressed as second-order differential equations. Although these equations depend on the parametric model, they combine the magnitude and phase of the signal and their gradients with respect to the parameters. Two cases are examined: (1) the general case for any finite-energy signal observed in a given bandwidth and (2) the observation of a finite-energy signal with a known magnitude spectrum and unknown phases and attenuation coefficient. The manifold of finite-energy signals corresponds to the manifold of the multivariate normal model with a known diagonal covariance matrix, while the set of finite-energy signals with a known magnitude spectrum constitutes a submanifold. Closed-form expressions for the Fisher–Rao distances are obtained for both cases. We show that the submanifold is not geodesic, because the Fisher–Rao distance measured on the submanifold exceeds that on the entire manifold.