Recurrent neural networks are capable of learning the dynamics of an unknown nonlinear system purely from input-output measurements. However, the resulting models do not provide any stability guarantees on the input-output mapping. In this work, we represent a recurrent neural network as a linear time-invariant system with nonlinear disturbances. By introducing constraints on the parameters, we can guarantee finite gain stability and incremental finite gain stability. We apply this identification method to learn the motion of a four-degrees-of-freedom ship that is moving in open water and compare it against other purely learning-based approaches with unconstrained parameters. Our analysis shows that the constrained recurrent neural network has a lower prediction accuracy on the test set, but it achieves comparable results on an out-of-distribution set and respects stability conditions.

If you take the cosmic view of Sellafield, the superannuated nuclear facility in north-west England, its story began long before the Earth took shape. About 9bn years ago, tens of thousands of giant stars ran out of fuel, collapsed upon themselves, and then exploded. Flung out by such explosions, trillions of tonnes of uranium traversed the cold universe and wound up near our slowly materialising solar system. And here, over roughly 20m years, the uranium and other bits of space dust and debris cohered to form our planet in such a way that the violent tectonics of the young Earth pushed the uranium not towards its hot core but up into the folds of its crust. Within reach, so to speak, of the humans who eventually came along circa 300,000BC, and who mined the uranium beginning in the 1500s, learned about its radioactivity in 1896 and started feeding it into their nuclear reactors 70-odd years ago, making electricity that could be relayed to their houses to run toasters and light up Christmas trees. Sellafield compels this kind of gaze into the abyss of deep time because it is a place where multiple time spans – some fleeting, some cosmic – drift in and out of view. Laid out over six square kilometres, Sellafield is like a small town, with nearly a thousand buildings, its own roads and even a rail siding – all owned by the government, and requiring security clearance to visit. Sellafield's presence, at the end of a road on the Cumbrian coast, is almost hallucinatory. Then, having driven through a high-security gate, you're surrounded by towering chimneys, pipework, chugging cooling plants, everything dressed in steampunk. The sun bounces off metal everywhere. In some spots, the air shakes with the noise of machinery. It feels like the most manmade place in the world. Since it began operating in 1950, Sellafield has had different duties. First it manufactured plutonium for nuclear weapons.

Who better to ask about artificial intelligence (AI) than the current darling of the scene, ChatGPT? Its answer ('Machine learning and data science are closely related fields, but they are not the same thing') is a useful starting point. But unpacking the differences between machine learning versus data science requires human effort, for the time being at least. Until the machines take over. If you are new to machine learning, it's worth skipping back to the late 1950s to gain an understanding of its origins.

Risk management in many environmental settings requires an understanding of the mechanisms that drive extreme events. Useful metrics for quantifying such risk are extreme quantiles of response variables conditioned on predictor variables that describe, e.g., climate, biosphere and environmental states. Typically these quantiles lie outside the range of observable data and so, for estimation, require specification of parametric extreme value models within a regression framework. Classical approaches in this context utilise linear or additive relationships between predictor and response variables and suffer in either their predictive capabilities or computational efficiency; moreover, their simplicity is unlikely to capture the truly complex structures that lead to the creation of extreme wildfires. In this paper, we propose a new methodological framework for performing extreme quantile regression using artificial neutral networks, which are able to capture complex non-linear relationships and scale well to high-dimensional data. The ``black box" nature of neural networks means that they lack the desirable trait of interpretability often favoured by practitioners; thus, we unify linear, and additive, regression methodology with deep learning to create partially-interpretable neural networks that can be used for statistical inference but retain high prediction accuracy. To complement this methodology, we further propose a novel point process model for extreme values which overcomes the finite lower-endpoint problem associated with the generalised extreme value class of distributions. Efficacy of our unified framework is illustrated on U.S. wildfire data with a high-dimensional predictor set and we illustrate vast improvements in predictive performance over linear and spline-based regression techniques.

We discuss two kinds of semantics relevant to Computer Vision (CV) systems - Visual Semantics and Lexical Semantics. While visual semantics focus on how humans build concepts when using vision to perceive a target reality, lexical semantics focus on how humans build concepts of the same target reality through the use of language. The lack of coincidence between visual and lexical semantics, in turn, has a major impact on CV systems in the form of the Semantic Gap Problem (SGP). The paper, while extensively exemplifying the lack of coincidence as above, introduces a general, domain-agnostic methodology to enforce alignment between visual and lexical semantics.

Time-critical control applications typically pose stringent connectivity requirements for communication networks. The imperfections associated with the wireless medium such as packet losses, synchronization errors, and varying delays have a detrimental effect on performance of real-time control, often with safety implications. This paper introduces multi-service edge-intelligence as a new paradigm for realizing time-critical control over wireless. It presents the concept of multi-service edge-intelligence which revolves around tight integration of wireless access, edge-computing and machine learning techniques, in order to provide stability guarantees under wireless imperfections. The paper articulates some of the key system design aspects of multi-service edge-intelligence. It also presents a temporal-adaptive prediction technique to cope with dynamically changing wireless environments. It provides performance results in a robotic teleoperation scenario. Finally, it discusses some open research and design challenges for multi-service edge-intelligence.

Out in the deep waters of the Gulf of Mexico, a young woman named Rachel clings to the side of an oil rig. The wind whips her auburn hair into a wild tangle, and ocean spray drenches her jeans, but she climbs on, determined to uncover evidence of illegal drilling. When she arrives on board, however, she finds something far more sinister at play. This is a snippet of Oil and Darkness, a horror movie set on an oil rig. It features environmental activist Rachel, guilt-ridden rig foreman Jack, and shady corporate executive Ryan, who has been conducting dangerous research on a "new type of highly flammable oil." It's the kind of movie you could swear you caught the second half of once while late-night channel-hopping or dozed blearily through on a long-haul flight.

Russian attacks have turned the eastern Ukrainian city of Bakhmut into "burnt ruins", President Volodymyr Zelenskyy has said, while Ukraine's military has reported missile, rocket and drone attacks in multiple parts of the country that have killed civilians and destroyed critical infrastructure. Zelenskyy said on Saturday that the situation "remains very difficult" in several front-line cities in eastern Ukraine's Donetsk and Luhansk provinces. For a long time, there is no living place left on the land of these areas that have not been damaged by shells and fire," Zelenskyy said in his nightly video address, naming cities that have again found themselves under sustained Russian barrages. "The occupiers actually destroyed Bakhmut, another Donbas city that the Russian army turned into burnt ruins," he said. Zelenskyy also said that more than 1.5 million people were without power in the southern Ukrainian city of Odesa after a night attack by drones.

Reasoning is central to human intelligence. However, fallacious arguments are common, and some exacerbate problems such as spreading misinformation about climate change. In this paper, we propose the task of logical fallacy detection, and provide a new dataset (Logic) of logical fallacies generally found in text, together with an additional challenge set for detecting logical fallacies in climate change claims (LogicClimate). Detecting logical fallacies is a hard problem as the model must understand the underlying logical structure of the argument. We find that existing pretrained large language models perform poorly on this task. In contrast, we show that a simple structure-aware classifier outperforms the best language model by 5.46% on Logic and 4.51% on LogicClimate. We encourage future work to explore this task as (a) it can serve as a new reasoning challenge for language models, and (b) it can have potential applications in tackling the spread of misinformation. Our dataset and code are available at https://github.com/causalNLP/logical-fallacy

Out in the deep waters of the Gulf of Mexico, a young woman named Rachel clings to the side of an oil rig. The wind whips her auburn hair into a wild tangle, and ocean spray drenches her jeans, but she climbs on, determined to uncover evidence of illegal drilling. When she arrives on board, however, she finds something far more sinister at play. This is a snippet of Oil and Darkness, a horror movie set on an oil rig. It features environmental activist Rachel, guilt-ridden rig foreman Jack, and shady corporate executive Ryan, who has been conducting dangerous research on a "new type of highly flammable oil." It's the kind of movie you could swear you caught the second half of once while late-night channel-hopping or dozed blearily through on a long-haul flight.