Jean-Paul Thorbjornsen is a leader of THORChain, a blockchain that is not supposed to have any leaders--and is reeling from a series of expensive controversies. We can do whatever we want," Jean-Paul Thorbjornsen tells me from the pilot's seat of his Aston Martin helicopter. As we fly over suburbs outside Melbourne, Australia, it's becoming clear that doing whatever he wants is Thorbjornsen's MO. Upper-middle-class homes give way to vineyards, and Thorbjornsen points out our landing spot outside a winery. "They're going to ask for a shot now," he says, used to the attention drawn by his luxury helicopter, emblazoned with the tail letters "BTC" for bitcoin (the price tag of $5 million in Australian dollars--$3.5 million in US dollars today--was perhaps reasonable for someone who claims a previous crypto project made more than AU$400 million, although he also says those funds were tied up in the company). Thorbjornsen is a founder of THORChain, a blockchain through which users can swap ...

Science Space Solar System Mars NASA's Mars Reconnaissance Orbiter snaps 100,000th image A high school student suggested the steep sand dunes of Syrtis Major for the milestone image. Breakthroughs, discoveries, and DIY tips sent every weekday. NASA's Mars Reconnaissance Orbiter (MRO) officially went into service above the Red Planet in November 2006. The spacecraft has since spent nearly 20 years circling Earth's closest neighbor, studying its geology and identifying icy evidence of a once watery world . After already sending back more than 450 terabits of data over the course of its ongoing mission, the orbiter recently passed a major milestone: its 100,000th image of the Martian surface.

This work addresses the full-information output regulation problem for nonlinear systems, assuming the states of both the plant and the exosystem are known. In this setting, perfect tracking or rejection is achieved by constructing a zero-regulation-error manifold $π(w)$ and a feedforward input $c(w)$ that render such manifold invariant. The pair $(π(w), c(w))$ is characterized by the regulator equations, i.e., a system of PDEs with an algebraic constraint. We focus on accurately solving the regulator equations introducing a physics-informed neural network (PINN) approach that directly approximates $π(w)$ and $c(w)$ by minimizing the residuals under boundary and feasibility conditions, without requiring precomputed trajectories or labeled data. The learned operator maps exosystem states to steady state plant states and inputs, enables real-time inference and, critically, generalizes across families of the exosystem with varying initial conditions and parameters. The framework is validated on a regulation task that synchronizes a helicopter's vertical dynamics with a harmonically oscillating platform. The resulting PINN-based solver reconstructs the zero-error manifold with high fidelity and sustains regulation performance under exosystem variations, highlighting the potential of learning-enabled solvers for nonlinear output regulation. The proposed approach is broadly applicable to nonlinear systems that admit a solution to the output regulation problem.

Text-to-audio models are a type of generative model that produces audio output in response to a given textual prompt. Although level generators and the properties of the functional content that they create (e.g., playability) dominate most discourse in procedurally generated content (PCG), games that emotionally resonate with players tend to weave together a range of creative and multimodal content (e.g., music, sounds, visuals, narrative tone), and multimodal models have begun seeing at least experimental use for this purpose. However, it remains unclear what exactly such models generate, and with what degree of variability and fidelity: audio is an extremely broad class of output for a generative system to target. Within the PCG community, expressive range analysis (ERA) has been used as a quantitative way to characterize generators' output space, especially for level generators. This paper adapts ERA to text-to-audio models, making the analysis tractable by looking at the expressive range of outputs for specific, fixed prompts. Experiments are conducted by prompting the models with several standardized prompts derived from the Environmental Sound Classification (ESC-50) dataset. The resulting audio is analyzed along key acoustic dimensions (e.g., pitch, loudness, and timbre). More broadly, this paper offers a framework for ERA-based exploratory evaluation of generative audio models.

Table 13: ANT comparisons when trained with small car as target on xView in multi-query setting.

Moreover, query results (e.g., detected search and rescue activity in a particular region) are highly informative about the locations of target objects in other regions, for example, due to spatial

Is Trump losing patience with Putin? Will sanctions against Russian oil giants hurt Putin? Russian forces have spread rapidly through Pokrovsk, the city in Ukraine's east where the warring sides have concentrated their manpower and tactical ingenuity during the past week, in what may be a final culmination of a 21-month battle. Geolocated footage placed Russian troops in central, northern and northeastern Pokrovsk, said the Institute for the Study of War (ISW), a Washington-based think tank. It set its sights on the city almost two years ago, after capturing Avdiivka, 39km (24 miles) to the east.

Table 13: ANT comparisons when trained with small car as target on xView in multi-query setting.

Moreover, query results (e.g., detected search and rescue activity in a particular region) are highly informative about the locations of target objects in other regions, for example, due to spatial

Abstract--This work addresses object identification under known dynamics in unmanned aerial vehicle applications, where learning and classification are combined through a physics-informed residual neural network. The proposed framework leverages physics-informed learning for state mapping and state-derivative prediction, while a softmax layer enables multi-class confidence estimation. Quadcopter, fixed-wing, and helicopter aerial vehicles are considered as case studies. The results demonstrate high classification accuracy with reduced training time, offering a promising solution for system identification problems in domains where the underlying dynamics are well understood. I. INTRODUCTION The increasing deployment of unmanned aerial vehicles (UA Vs) in civilian and defense sectors has elevated the importance of dynamic modeling and intent inference for tasks such as control, classification, and anomaly detection. Traditional approaches to UA V identification rely primarily on visual, radio-frequency, or pattern-based features, which are vulnerable in contested or adversarial environments [1], [2].