VILNIUS/STOCKHOLM/LONDON - Ukrainian drones have strayed into Baltic countries' airspace in recent weeks, sowing confusion and raising tensions with Russia at a time when U.S. commitment to NATO's collective security is in question. The airspace incursions have occurred as Ukraine, seeking to land heavier blows on Russia four years after Moscow's full-scale invasion, uses exploding drones to hit Russian Baltic ports that handle nearly 40% of national oil and gas exports. In most cases, Kyiv and the Baltic states have confirmed the stray drones are Ukrainian but have blamed Russia for causing them to deviate from their flight path with the use of electronic defenses that jam or spoof signals. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right.

'Our characters are cybernetic or larger than life,' said Zak London, the founder of Gossip Goblin. 'We adapt to the limits of AI acting.' 'Our characters are cybernetic or larger than life,' said Zak London, the founder of Gossip Goblin. 'We adapt to the limits of AI acting.' 'There are no rules': spotlight on Gossip Goblin as AI film-making enters new era Defying criticisms of'slop' and'theft', the growing culture of AI-powered creativity is attracting interest from Hollywood In a former hemstitching workshop where artisans sewed pleats for Stockholm's 19th-century bourgeoisie, a distinctly 21st-century craft is taking root: AI film-making. One day last week, an actor, director and composer squeezed into a tiny studio booth to record a voiceover for their next AI release. But this had a distinctly homespun feel, the little team fussing over a monologue by a poetic Scottish gorilla inhabiting a transhumanist cyberpunk universe.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' The world's militaries spent $2.88 trillion in 2025, an increase of 2.9 percent from the year before, according to the Stockholm International Peace Research Institute's (SIPRI) latest report. To put that number into perspective, $2.88 trillion amounts to $350 of military spending for each person on the planet. In this visual explainer, Al Jazeera unpacks the rise of global militarisation, including how much each nation spends, which countries sell the most weapons, and how military spending compares with spending on healthcare and education. In 2025, the five biggest military spenders were the United States ($954bn), China ($336bn), Russia ($190bn), Germany ($114bn) and India ($92bn), accounting for more than half (58 percent) of world military spending. The US is by far the biggest spender, as it has been every year since World War II.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' But the calm is deceptive. Two soldiers scour the skies, hands firmly gripping anti-aircraft guns mounted on pick-up trucks parked on a small, dilapidated bridge on a tributary of the Pripyat River. Danger is all around, both in the surrounding land, which still carries the legacy of the 1986 Chornobyl nuclear disaster, with pockets of intense radioactive contamination, and above, where Russian drones and missiles launched from just across the border in Belarus, a short distance to the north, regularly pass overhead. The area is known as the Chornobyl Exclusion Zone (CEZ), a restricted area of approximately 30km (19 miles) in diameter, comparable in size to Luxembourg, established to contain the spread of contamination. Since Russia launched its full-scale invasion of Ukraine on February 24, 2022, briefly occupying the CEZ and the surrounding area, large swaths of it have become militarised, adding another layer of restriction to an already tightly controlled and hazardous environment. Yet despite the CEZ's many dangers, four decades on from the Chornobyl disaster, small communities of scientists, elderly returnees and soldiers have carved out lives among its abandoned buildings, while wildlife thrives in the surrounding forests.

RoboCup is an international competition that promotes and advances robotics and AI through the challenges presented by its various leagues. We got the chance to sit down with Professor Manuela Veloso, one of RoboCup's founders, to find out more about how it all started, how the community has grown over the years, and the vision for the future. I think it would be very interesting to go right back to the beginning and hear how RoboCup got started. What was the initial idea, and how did it get set up? So we are talking about the mid-90s. In terms of the research in those days, it was the beginning of the internet and many AI and computer science researchers were focused on the internet, first on sophisticated search algorithms, on natural language understanding, on information retrieval, and then on software agents and machine learning applied to digital information. From what I recall, there was a smaller group of researchers who were interested in actual, physical robots, and in particular in AI and robotics.

Classical mixture models (MMs) are widely used tractable proposals for approximate inference settings such as variational inference (VI) and importance sampling (IS). Recently, mixture models with negative coefficients, called subtractive mixture models (SMMs), have been proposed as a potentially more expressive alternative. However, how to effectively use SMMs for VI and IS is still an open question as they do not provide latent variable semantics and therefore cannot use sampling schemes for classical MMs. In this work, we study how to circumvent this issue by designing several expectation estimators for IS and learning schemes for VI with SMMs, and we empirically evaluate them for distribution approximation. Finally, we discuss the additional challenges in estimation stability and learning efficiency that they carry and propose ways to overcome them. Code is available at: https://github.com/april-tools/delta-vi.

Protecting individual privacy is essential across research domains, from socio-economic surveys to big-tech user data. This need is particularly acute in healthcare, where analyses often involve sensitive patient information. A typical example is comparing treatment efficacy across hospitals or ensuring consistency in diagnostic laboratory calibrations, both requiring privacy-preserving statistical procedures. However, standard equivalence testing procedures for differences in proportions or means, commonly used to assess average equivalence, can inadvertently disclose sensitive information. To address this problem, we develop differentially private equivalence testing procedures that rely on simulation-based calibration, as the finite-sample distribution is analytically intractable. Our approach introduces a unified framework, termed DP-TOST, for conducting differentially private equivalence testing of both means and proportions. Through numerical simulations and real-world applications, we demonstrate that the proposed method maintains type-I error control at the nominal level and achieves power comparable to its non-private counterpart as the privacy budget and/or sample size increases, while ensuring strong privacy guarantees. These findings establish a reliable and practical framework for privacy-preserving equivalence testing in high-stakes fields such as healthcare, among others.

We propose an active learning algorithm for linear system identification with optimal centered noise excitation. Notably, our algorithm, based on ordinary least squares and semidefinite programming, attains the minimal sample complexity while allowing for efficient computation of an estimate of a system matrix. More specifically, we first establish lower bounds of the sample complexity for any active learning algorithm to attain the prescribed accuracy and confidence levels. Next, we derive a sample complexity upper bound of the proposed algorithm, which matches the lower bound for any algorithm up to universal factors. Our tight bounds are easy to interpret and explicitly show their dependence on the system parameters such as the state dimension.

Higgs Boson breakthrough was UK triumph, but British physics faces'catastrophic' cuts When the Nobel Prize in Physics was announced in Stockholm in October 2013, the world was watching. Among the names read out was Prof Peter Higgs, the British theorist who, nearly half a century earlier, had predicted the existence of a particle believed to hold the cosmos together - the Higgs boson. The announcement, broadcast live from Sweden, was what many scientists had hoped for since a year earlier, when experiments at CERN had finally confirmed Higgs's theory by discovering the Higgs boson - hailed as one of the biggest discoveries in a generation. At the time Higgs, who has since passed away, said in a statement: I hope this recognition of fundamental science will help raise awareness of the value of blue-sky research. Blue-sky research asks questions to understand the universe, rather than design new products.

We study mean estimation for a Gaussian distribution with identity covariance in $\mathbb{R}^d$ under a missing data scheme termed realizable $ε$-contamination model. In this model an adversary can choose a function $r(x)$ between 0 and $ε$ and each sample $x$ goes missing with probability $r(x)$. Recent work Ma et al., 2024 proposed this model as an intermediate-strength setting between Missing Completely At Random (MCAR) -- where missingness is independent of the data -- and Missing Not At Random (MNAR) -- where missingness may depend arbitrarily on the sample values and can lead to non-identifiability issues. That work established information-theoretic upper and lower bounds for mean estimation in the realizable contamination model. Their proposed estimators incur runtime exponential in the dimension, leaving open the possibility of computationally efficient algorithms in high dimensions. In this work, we establish an information-computation gap in the Statistical Query model (and, as a corollary, for Low-Degree Polynomials and PTF tests), showing that algorithms must either use substantially more samples than information-theoretically necessary or incur exponential runtime. We complement our SQ lower bound with an algorithm whose sample-time tradeoff nearly matches our lower bound. Together, these results qualitatively characterize the complexity of Gaussian mean estimation under $ε$-realizable contamination.