Recent weeks have seen a significant escalation of military tensions in and around the Baltics. Lithuania, Latvia and Estonia, which are all NATO members, now experience regular incursions into their airspace by Ukrainian drones. According to both Kyiv and the Baltic capitals, those drones, en route to hit targets in western Russia, get diverted by Russian electronic jamming and end up entering these countries' territories. In early May, several stray unmanned aircraft crashed in Latvia, one of them damaging an oil storage facility. Those developments triggered a political crisis in Latvia and led to the collapse of its government.

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.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Russia strikes Ukraine's Odesa port, kills railway worker in Zaporizhia Russian drones have attacked Ukraine's main Black Sea port in the southern city of Odesa and a railway in the region of Zaporizhia, killing a train driver, according to Ukraine's Deputy Prime Minister Oleksii Kuleba. The overnight attacks damaged the infrastructure of the Odesa port, including berths, warehouses, railway infrastructure and port operators' facilities, Kuleba said in a statement on X on Wednesday. Kuleba said this is "another proof of terrorism, Russia is at war against peaceful people, against those who were simply doing their job and keeping the country moving". Russia also launched several drones and missiles on a flight path near the disused Chornobyl nuclear plant, elevating the risk of a significant accident, according to Ukraine's top state prosecutor. This comes as Ukraine prepares to mark the 40th anniversary of the 1986 Chornobyl disaster on Sunday.

Plus, Mouse: PI for Hire arrives and Hades 2 hits PS5 and Xbox Series X/S. Welcome to our latest roundup of what's going on in the indie game space. Once again, there are some neat new games for you to check out this weekend. We've got a bunch of updates and announcements for upcoming titles to tell you about too. There have been a bunch of solid indie showcases lately (and highlights from another one to tell you about below).

The natural gradient method is widely used in statistical optimization, but its standard formulation assumes a Euclidean parameter space. This paper proposes an inversion-free stochastic natural gradient method for probability distributions whose parameters lie on a Riemannian manifold. The manifold setting offers several advantages: one can implicitly enforce parameter constraints such as positive definiteness and orthogonality, ensure parameters are identifiable, or guarantee regularity properties of the objective like geodesic convexity. Building on an intrinsic formulation of the Fisher information matrix (FIM) on a manifold, our method maintains an online approximation of the inverse FIM, which is efficiently updated at quadratic cost using score vectors sampled at successive iterates. In the Riemannian setting, these score vectors belong to different tangent spaces and must be combined using transport operations. We prove almost-sure convergence rates of $O(\log{s}/s^α)$ for the squared distance to the minimizer when the step size exponent $α>2/3$. We also establish almost-sure rates for the approximate FIM, which now accumulates transport-based errors. A limited-memory variant of the algorithm with sub-quadratic storage complexity is proposed. Finally, we demonstrate the effectiveness of our method relative to its Euclidean counterparts on variational Bayes with Gaussian approximations and normalizing flows.

'100 Video Calls Per Day': Models Are Applying to Be the Face of AI Scams Dozens of Telegram channels reviewed by WIRED include job listings for "AI face models." The (mostly) women who land these gigs are likely being used to dupe victims out of their money. "I can speak fluent English, I can speak good Chinese, I also speak Russian and Turkish," the glamorous, 24-year-old Uzbekistani woman explains in a selfie-style video made for recruiters. Angel had arrived in the Cambodian city of Sihanoukville that day, she said, and was ready to start work immediately. Those impressive language skills, however, have likely been put to use as part of elaborate " pig-butchering " scams targeting Americans.

Sign language translation (SL T) addresses the problem of translating information from a sign language in video to a spoken language in text. Existing studies, while showing progress, are often limited to narrow domains and/or few sign languages and struggle with open-domain tasks. In this paper, we push forward the frontier of SL T by scaling pretraining data, model size, and number of translation directions. We perform large-scale SL T pretraining on different data including 1) noisy multilingual Y ouTube SL T data, 2) parallel text corpora, and 3) SL T data augmented by translating video captions to other languages with off-the-shelf machine translation models. We unify different pretraining tasks with task-specific prompts under the encoder-decoder architecture, and initialize the SL T model with pretrained (m/By)T5 models across model sizes. SL T pretraining results on How2Sign and FLEURS-ASL#0 (ASL to 42 spoken languages) demonstrate the significance of data/model scaling and cross-lingual cross-modal transfer, as well as the feasibility of zero-shot SL T. We finetune the pretrained SL T models on 5 downstream open-domain SL T benchmarks covering 5 sign languages. Experiments show substantial quality improvements over the vanilla baselines, surpassing the previous state-of-the-art (SOT A) by wide margins.