Abstract
Context. Near-infrared interferometry has become a powerful tool for studying the orbital and atmospheric parameters of substellar companions. Aims. We aim to reveal the nature of the reddest known substellar companion HD 206893 B by studying its near-infrared colors and spectral morphology and by investigating its orbital motion. Methods. We fit atmospheric models for giant planets and brown dwarfs and perform spectral retrievals with petitRADTRANS and ATMO on the observed GRAVITY, SPHERE, and GPI spectra of HD 206893 B. To recover its unusual spectral features, first and foremost its extremely red near-infrared color, we include additional extinction by high-altitude dust clouds made of enstatite grains in the atmospheric model fits. However, forsterite, corundum, and iron grains predict similar extinction curves for the grain sizes considered here. We also infer the orbital parameters of HD 206893 B by combining the similar to 100 mu as precision astrometry from GRAVITY with data from the literature and constrain the mass and position of HD 206893 C based on the Gaia proper motion anomaly of the system. Results. The extremely red color and the very shallow 1.4 mu m water absorption feature of HD 206893 B can be fit well with the adapted atmospheric models and spectral retrievals. By comparison with AMES-Cond evolutionary tracks, we find that only some atmosphericmodels predict physically plausible objects. Altogether, our analysis suggests an age of similar to 3-300 Myr and a mass of similar to 5-30 M-Jup for HD 206893 B, which is consistent with previous estimates but extends the parameter space to younger and lower-mass objects. The GRAVITY astrometry points to an eccentric orbit (e = 0.29(-0.11)(+0.06)) with a mutual inclination of <34.4 deg with respect to the debris disk of the system. Conclusions. While HD 206893 B could in principle be a planetary-mass companion, this possibility hinges on the unknown influence of the inner companion on the mass estimate of 10(-4)(+5) M-Jup from radial velocity and Gaia as well as a relatively small but significant Argus moving group membership probability of similar to 61%. However, we find that if the mass of HD 206893 B is M-Jup, then the inner companion HD 206893 C should have a mass between similar to 8-15 M-Jup. Finally, further spectroscopic or photometric observations at higher signal-to-noise and longer wavelengths are required to learn more about the composition and dust cloud properties of HD 206893 B.

Abstract

Abstract
SARS-CoV-2 has presented direct and indirect challenges to the scientific community. One of the most prominent indirect challenges advents from the mandatory use of face masks in a large number of countries. Face recognition methods struggle to perform identity verification with similar accuracy on masked and unmasked individuals. It has been shown that the performance of these methods drops considerably in the presence of face masks, especially if the reference image is unmasked. We propose FocusFace, a multi-task architecture that uses contrastive learning to be able to accurately perform masked face recognition. The proposed architecture is designed to be trained from scratch or to work on top of state-of-the-art face recognition methods without sacrificing the capabilities of a existing models in conventional face recognition tasks. We also explore different approaches to design the contrastive learning module. Results are presented in terms of masked-masked (MM) and unmasked-masked (U-M) face verification performance. For both settings, the results are on par with published methods, but for M-M specifically, the proposed method was able to outperform all the solutions that it was compared to. We further show that when using our method on top of already existing methods the training computational costs decrease significantly while retaining similar performances. The implementation and the trained models are available at GitHub.

Abstract
The direct power purchase by large consumers (DPLC) is an important part of the reform of the electricity market, and the development of renewable energy has led to a trend of decentralization on the supply side. Blockchain, as an emerging distributed database technology, has a good application prospect in the context of the current energy Internet construction. The article first introduces the principle of blockchain technology in detail and analyzes its application value in electricity trading. Starting from the traditional direct purchase transaction model, a framework for direct purchase of electricity for large consumers is proposed. Combining the characteristics of direct power purchase transactions, the distributed consensus mechanism is researched and improved, the smart contract is designed in combination with the transaction process, and the communication protocol and interaction relationship at each level are analyzed from the overall system architecture. Finally, the challenges faced by the system in practical application are analyzed, which provides ideas for follow-up research.

Abstract
This survey paper discusses opportunities and threats of using artificial intelligence (AI) technology in the manufacturing sector with consideration for offensive and defensive uses of such technology. It starts with an introduction of Industry 4.0 concept and an understanding of AI use in this context. Then provides elements of security principles and detection techniques applied to operational technology (OT) which forms the main attack surface of manufacturing systems. As some intrusion detection systems (IDS) already involve some AI-based techniques, we focus on existing machine-learning and data-mining based techniques in use for intrusion detection. This article presents the major strengths and weaknesses of the main techniques in use. We also discuss an assessment of their relevance for application to OT, from the manufacturer point of view. Another part of the paper introduces the essential drivers and principles of Industry 4.0, providing insights on the advent of AI in manufacturing systems as well as an understanding of the new set of challenges it implies. AI-based techniques for production monitoring, optimisation and control are proposed with insights on several application cases. The related technical, operational and security challenges are discussed and an understanding of the impact of such transition on current security practices is then provided in more details. The final part of the report further develops a vision of security challenges for Industry 4.0. It addresses aspects of orchestration of distributed detection techniques, introduces an approach to adversarial/robust AI development and concludes with human-machine behaviour monitoring requirements.

Abstract
We address classification of distributional data, where units are described by histogram or interval-valued variables. The proposed approach uses a linear discriminant function where distributions or intervals are represented by quantile functions, under specific assumptions. This discriminant function allows defining a score for each unit, in the form of a quantile function, which is used to classify the units in two a priori groups, using the Mallows distance. There is a diversity of application areas for the proposed linear discriminant method. In this work we classify the airline companies operating in NY airports based on air time and arrival/departure delays, using a full year flights.