We consider a variant of the target defense problem where a single defender is tasked to capture a sequence of incoming intruders. The intruders' objective is to breach the target boundary without being captured by the defender. As soon as the current intruder breaches the target or gets captured by the defender, the next intruder appears at a random location on a fixed circle surrounding the target. Therefore, the defender's final location at the end of the current game becomes its initial location for the next game. Thus, the players pick strategies that are advantageous for the current as well as for the future games. Depending on the information available to the players, each game is divided into two phases: partial information and full information phase. Under some assumptions on the sensing and speed capabilities, we analyze the agents' strategies in both phases. We derive equilibrium strategies for both the players to optimize the capture percentage using the notions of engagement surface and capture circle. We quantify the percentage of capture for both finite and infinite sequences of incoming intruders.

This paper presents an automatic approach to creating taxonomies of technical terms based on the Cooperative Patent Classification (CPC). The resulting taxonomy contains about 170k nodes in 9 separate technological branches and is freely available. We also show that a Text-to-Text Transfer Transformer (T5) model can be fine-tuned to generate hypernyms and hyponyms with relatively high precision, confirming the manually assessed quality of the resource. The T5 model opens the taxonomy to any new technological terms for which a hypernym can be generated, thus making the resource updateable with new terms, an essential feature for the constantly evolving field of technological terminology.

Ministers of the European Union of Foreign Affairs have imposed new sanctions on Iranian religious leaders, senior officials and top state media employees over new crackdowns on antigovernment protests and supplying drones to Russia for use in Ukraine. Twenty individuals and one entity were sanctioned on Monday over human rights abuses, while four more people and as many entities were added over the issue of drones. Sanctions include freezing of assets and travel bans to the EU. The state-owned Islamic Republic of Iran Broadcasting had its assets frozen; the EU said the media outlet was a "mouthpiece" for "the violent response to the recent demonstrations in Iran". Iranians have protested against the government's restrictions on their daily lives since late September, following the death of 22-year-old Mahsa Amini, who was arrested for allegedly violating the Islamic Republic's strict dress code.

NASA has shared new photos of the incredible moment the Orion space capsule returned to Earth after flying around the moon. The unmanned Orion capsule splashed down in the Pacific Ocean, west of Baja California, at 09:40 PST (17:40 GMT) on Sunday. Since its launch in mid-November, it has travelled more than 1.4 million miles on a path around the moon and back to Earth. The images show before and after the historic point of impact, which marks the first part of Artemis – NASA's successor to the Apollo programme in the 1960s and 1970s. NASA's Orion Capsule descends toward splash down after a successful uncrewed Artemis 1 Moon Mission on December 11, 2022 seen from aboard the USS Portland in the Pacific Ocean off the coast of Baja California, Mexico Artemis 1 is NASA's uncrewed flight test of the Space Launch System (SLS) rocket and Orion spacecraft, which launched on November 16 from Kennedy Space Center, Merritt Island, Florida.

This content can also be viewed on the site it originates from. On the outskirts of Santa Barbara, California, between the orchards and the ocean, sits an inconspicuous warehouse, its windows tinted brown and its exterior painted a dull gray. The facility has almost no signage, and its name doesn't appear on Google Maps. A small label on the door reads "Google AI Quantum." Inside, the computer is being reinvented from scratch.

At Wander cafe in Auckland's Wynyard Quarter, someone at the next table is listening to Sean Gourley while he is being interviewed about artificial intelligence. After eavesdropping on the chat they get up, walk over to Gourley's table and tell him how scared they are. Gourley says most people think there is a 1% chance of war between China and the United States, but in his universe it is looking more like 50%. US defence and intelligence clients account for a large portion of the business Gourley's San Francisco-based artificial intelligence (AI) company, PrimerAI, does – and right now business is booming. READ MORE: * Keeping up with the machines, new supercomputer will be NZ's most powerful for AI * Kiwis need to think about what they want from the age of AI, report says * 'Google should not be in the business of war', says employee after it drops Pentagon AI contract * While artificial intelligence is tipped to be'as significant as electricity', it's not coming for your job, yet ...

RegTech allows companies to better manage regulatory compliance, but it is also a valuable tool to create added value and innovatively grow the business. But for this to be possible, companies need to implement specific technologies, which are the basic conditions for developing a good RegTech system, such as blockchain, AI, and RPA. When building a business and imagining strategies and ways to grow, one cannot help but take into account the regulations and operational and procedural requirements established by the legislature for a given industry, including when it comes to RegTech and regulations. Often, regulation is considered as an element that hinders or slows down the development of the business. In other words, one often perceives the conflict, the opposition between regulation and the development and growth of an entrepreneurial project.

Recent work has shown that machine learning (ML) models can be trained to accurately forecast the dynamics of unknown chaotic dynamical systems. Short-term predictions of the state evolution and long-term predictions of the statistical patterns of the dynamics (``climate'') can be produced by employing a feedback loop, whereby the model is trained to predict forward one time step, then the model output is used as input for multiple time steps. In the absence of mitigating techniques, however, this technique can result in artificially rapid error growth. In this article, we systematically examine the technique of adding noise to the ML model input during training to promote stability and improve prediction accuracy. Furthermore, we introduce Linearized Multi-Noise Training (LMNT), a regularization technique that deterministically approximates the effect of many small, independent noise realizations added to the model input during training. Our case study uses reservoir computing, a machine-learning method using recurrent neural networks, to predict the spatiotemporal chaotic Kuramoto-Sivashinsky equation. We find that reservoir computers trained with noise or with LMNT produce climate predictions that appear to be indefinitely stable and have a climate very similar to the true system, while reservoir computers trained without regularization are unstable. Compared with other regularization techniques that yield stability in some cases, we find that both short-term and climate predictions from reservoir computers trained with noise or with LMNT are substantially more accurate. Finally, we show that the deterministic aspect of our LMNT regularization facilitates fast hyperparameter tuning when compared to training with noise.

Deep neural operators can learn nonlinear mappings between infinite-dimensional function spaces via deep neural networks. As promising surrogate solvers of partial differential equations (PDEs) for real-time prediction, deep neural operators such as deep operator networks (DeepONets) provide a new simulation paradigm in science and engineering. Pure data-driven neural operators and deep learning models, in general, are usually limited to interpolation scenarios, where new predictions utilize inputs within the support of the training set. However, in the inference stage of real-world applications, the input may lie outside the support, i.e., extrapolation is required, which may result to large errors and unavoidable failure of deep learning models. Here, we address this challenge of extrapolation for deep neural operators. First, we systematically investigate the extrapolation behavior of DeepONets by quantifying the extrapolation complexity via the 2-Wasserstein distance between two function spaces and propose a new behavior of bias-variance trade-off for extrapolation with respect to model capacity. Subsequently, we develop a complete workflow, including extrapolation determination, and we propose five reliable learning methods that guarantee a safe prediction under extrapolation by requiring additional information -- the governing PDEs of the system or sparse new observations. The proposed methods are based on either fine-tuning a pre-trained DeepONet or multifidelity learning. We demonstrate the effectiveness of the proposed framework for various types of parametric PDEs. Our systematic comparisons provide practical guidelines for selecting a proper extrapolation method depending on the available information, desired accuracy, and required inference speed.

Recent advances in safety-critical risk-aware control are predicated on apriori knowledge of the disturbances a system might face. This paper proposes a method to efficiently learn these disturbances online, in a risk-aware context. First, we introduce the concept of a Surface-at-Risk, a risk measure for stochastic processes that extends Value-at-Risk -- a commonly utilized risk measure in the risk-aware controls community. Second, we model the norm of the state discrepancy between the model and the true system evolution as a scalar-valued stochastic process and determine an upper bound to its Surface-at-Risk via Gaussian Process Regression. Third, we provide theoretical results on the accuracy of our fitted surface subject to mild assumptions that are verifiable with respect to the data sets collected during system operation. Finally, we experimentally verify our procedure by augmenting a drone's controller and highlight performance increases achieved via our risk-aware approach after collecting less than a minute of operating data.