Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

Current maritime communications mainly rely on satellites having meager transmission resources, hence suffering from poorer performance than modern terrestrial wireless networks. With the growth of transcontinental air traffic, the promising concept of aeronautical ad hoc networking relying on commercial passenger airplanes is potentially capable of enhancing satellite-based maritime communications via air-to-ground and multi-hop air-to-air links. In this article, we conceive space-air-ground integrated networks (SAGINs) for supporting ubiquitous maritime communications, where the low-earth-orbit satellite constellations, passenger airplanes, terrestrial base stations, ships, respectively, serve as the space-, air-, ground- and sea-layer. To meet heterogeneous service requirements, and accommodate the time-varying and self-organizing nature of SAGINs, we propose a deep learning (DL) aided multi-objective routing algorithm, which exploits the quasi-predictable network topology and operates in a distributed manner. Our simulation results based on real satellite, flight, and shipping data in the North Atlantic region show that the integrated network enhances the coverage quality by reducing the end-to-end (E2E) delay and by boosting the E2E throughput as well as improving the path-lifetime. The results demonstrate that our DL-aided multi-objective routing algorithm is capable of achieving near Pareto-optimal performance.

Active learning is a subfield of machine learning that focuses on improving the data collection efficiency of expensive-to-evaluate systems. Especially, active learning integrated surrogate modeling has shown remarkable performance in computationally demanding engineering systems. However, the existence of heterogeneity in underlying systems may adversely affect the performance of active learning. In order to improve the learning efficiency under this regime, we propose the partitioned active learning that seeks the most informative design points for partitioned Gaussian process modeling of heterogeneous systems. The proposed active learning consists of two systematic subsequent steps: the global searching scheme accelerates the exploration of active learning by investigating the most uncertain design space, and the local searching exploits the circumscribed information induced by the local GP. We also propose Cholesky update driven numerical remedies for our active learning to address the computational complexity challenge. The proposed method is applied to numerical simulations and two real-world case studies about (i) the cost-efficient automatic fuselage shape control in aerospace manufacturing; and (ii) the optimal design of tribocorrosion-resistant alloys in materials science. The results show that our approach outperforms benchmark methods with respect to prediction accuracy and computational efficiency.

On Friday, North Atlantic Treaty Organisation's (NATO) defence ministers approved the alliance's first artificial intelligence strategy and the creation of the NATO Innovation Fund. Following the second day of the NATO defence ministerial conference in Brussels, Secretary-General Jens Stoltenberg stated that NATO allies have signed an agreement to establish the alliance's first Innovation Fund. He stated that NATO's new innovation fund will guarantee that organisations do not miss out on the most cutting-edge technology and capabilities that are crucial for its security, reported Anadolu Agency. With approximately $1 billion in funding from 17 NATO member states, the programme will promote research and development on new and disruptive technologies. NATO defence ministers also approved the alliance's first Artificial Intelligence Strategy, which establishes guidelines for the use of AI in accordance with international law.

The North Atlantic Treaty Organization (NATO), the military alliance of 30 countries that border the North Atlantic Ocean, this week announced that it would adopt its first AI strategy and launch a "future-proofing" fund with the goal of investing around $1 billion. Speaking at a news conference, Secretary-General Jens Stoltenberg said that the effort was in response to "authoritarian regimes racing to develop new technologies." NATO's AI strategy will cover areas including data analysis, imagery, cyberdefense, he added. NATO said in a July press release that it was "currently finalizing" its strategy on AI" and that principles of responsible use of AI in defense will be "at the core" of the strategy. Speaking to Politico in March, NATO assistant secretary general for emerging security challenges David van Weel said that the strategy would identify ways to operate AI systems ethically, pinpoint military applications for the technology, and provide a "platform for allies to test their AI to see whether it's up to NATO standards."

NATO defense ministers on Friday approved the alliance's first strategy on artificial intelligence and the establishment of the NATO Innovation Fund. NATO allies signed an agreement on setting up the alliance's first Innovation Fund, Secretary-General Jens Stoltenberg announced following the second day of the NATO defense ministerial meeting. "NATO's new innovation fund will ensure us to not miss out on the latest technology and capabilities that will be critical to our security," he added. The initiative, financed by 17 NATO member states, will support research and development on emerging and disruptive technologies with over $1 billion. NATO defense ministers also adopted the alliance's first Artificial Intelligence Strategy that sets the standards for the use of this technology with the respect of international law.

NATO defence ministers have agreed upon a new master plan to defend against any potential Russian attack on multiple fronts, reaffirming the alliance's core goal of deterring Moscow despite a growing focus on China. The confidential strategy aims to prepare for any simultaneous attack in the Baltic and Black Sea regions that could include nuclear weapons, hacking of computer networks and assaults from space. "We continue to strengthen our alliance with better and modernised plans," NATO Secretary General Jens Stoltenberg said after the meeting on Thursday, which also agreed a $1bn fund to provide seed financing to develop new digital technologies. Officials stressed that they do not believe any Russian attack is imminent. Moscow has denied any aggressive intentions and said it is NATO that risks destabilising Europe with such preparations.

While convolutional neural networks (CNNs) have found wide adoption as state-of-the-art models for image-related tasks, their predictions are often highly sensitive to small input perturbations, which the human vision is robust against. This paper presents Perturber, a web-based application that allows users to instantaneously explore how CNN activations and predictions evolve when a 3D input scene is interactively perturbed. Perturber offers a large variety of scene modifications, such as camera controls, lighting and shading effects, background modifications, object morphing, as well as adversarial attacks, to facilitate the discovery of potential vulnerabilities. Fine-tuned model versions can be directly compared for qualitative evaluation of their robustness. Case studies with machine learning experts have shown that Perturber helps users to quickly generate hypotheses about model vulnerabilities and to qualitatively compare model behavior. Using quantitative analyses, we could replicate users' insights with other CNN architectures and input images, yielding new insights about the vulnerability of adversarially trained models.

A new center at Bigelow Laboratory is using cutting-edge artificial intelligence algorithms to forecast ocean activity, from toxic algal blooms to right whale migration, with the hopes of benefitting both coastal industries and the environment. People are expecting forecasts of all different kinds now, from COVID forecasts to political forecasts," said Nick Record, a senior research scientist at Bigelow Laboratory for Ocean Sciences in East Boothbay. "We're trying to tap into this societal need and demand for forecasts and apply it to ocean systems that we live in and rely on." The ability to accurately forecast complex ocean dynamics alone, such as temperature and salinity, is useful for the industries that use the coastline and the scientists that study it. With artificial intelligence, though, these forecasts will be constantly improving in accuracy even as the climate changes -- and, with it, Maine's ability to adapt to the changing coastline will improve as well.

Trust is ubiquitous, but the understanding, building, and retaining of trust has become a key challenge of our time, with the trust narrative evolving across a dynamic duality. On one hand, concerns around data privacy, security, and the ethical development of artificial intelligence (AI) abound; on the other, the "art of the possible" has been demonstrated through the positive purposes to which data and technology have been applied. Another dynamic has also evolved recently: data literacy. Over the last year, our everyday lives have been dominated by data, heightening levels of awareness, and helping move beyond data ubiquity to make analytics more ubiquitous too. But as people understand more about how organizations are using their data, they are increasingly concerned, bringing trust center stage.