A propaganda song to get drivers on board even topped Sweden's hit music chart. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A police officer tries to direct traffic as Sweden switches to driving on the other side of the road over night. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . On September 3, 1967, at 4:50 a.m., a lone Swedish taxi driver stopped his car on the street in the light of early dawn.

Science Space Deep Space Exoplanets Sorry, aliens didn't build Dyson spheres around these dwarf stars Searching for E.T. is full of disappointments. More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. The Dyson sphere was first proposed by physicist Freeman Dyson in 1960. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy .

Sweden's armed forces have asked the government to expropriate a Russia-owned property near a naval base on security grounds. The military requested the seizure of the beach plot near Muskö base in Stockholm archipelago, citing the threat from Russian drones and the ownership situation of the property. Swedish reports say a businessman with links to the Russian state, has been building a luxury house on Muskö island - although his wife, who holds both Russian and Swedish citizenship, is the registered owner. Two years ago, Sweden ended two centuries of military non-alignment and joined Nato because of Russia's full-scale invasion of Ukraine. In its statement, the military said the location had unique conditions for strengthening the defence of Muskö Naval Base, particularly against drones. The development of Russian drone warfare has changed the threat landscape for Sweden, the military added.

When you purchase through links in our articles, we may earn a small commission. The Sweden-based Lovable announced a big series C investment, underscoring the market has confidence in vibe-coding for companies big and small. PCWorld supports the basic vibe-coding concept. In fact, one of our writers says he " utterly loves it ." It turns out there's a AI development platform called Lovable--think vibe-coding with serious enterprise scaffolding behind it--and now the same investor that owns PCWorld is also an investor in Lovable. On Wednesday, the Sweden-based Lovable announced that it raised $400 million in series C funding, and one of the new backers includes Regent, the same private equity firm that owns Foundry, whose portfolio includes PCWorld, Macworld, TechCrunch and other tech publications.

Image caption, More than 2,000 images were taken of Ruth's skin I don't mind having my photo taken - triple checked and filtered for Instagram - but 70 cameras pointing at me while I'm down to my knickers is a bit daunting. A robotic voice tells me to stay still and close my eyes as I stand in a huge curved scanner while classical music plays in the background. With a flash of light, 2,000 photographs are taken of my body, in the hope of capturing every mark, freckle and mole to analyse for different skin cancers. This full-body scan is happening at a sci-fi-style clinic in Manchester city centre, with me wearing a dressing gown and hexagon-shaped rubber slippers. The millions of data points collected will create a 3D avatar of my body using AI.

Environment Animals Wildlife Birds No, crows aren't cleaning up cigarette butts in Sweden More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. A French historical theme park debuted a similar idea in 2018. Breakthroughs, discoveries, and DIY tips sent six days a week. By signing up, you confirm you are 16+, will receive newsletters and promotional content and agree to our Terms of Use and acknowledge the data practices in our Privacy Policy . Mounting evidence shows the jet black birds display an intelligence rarely seen in other animals.

Equivariant network architectures are a well-established tool for predicting invariant or equivariant quantities. However, almost all learning problems considered in this context feature a global symmetry, i.e. each point of the underlying space is transformed with the same group element, as opposed to a local gauge symmetry, where each point is transformed with a different group element, exponentially enlarging the size of the symmetry group. We use gauge equivariant networks to predict topological invariants (Chern numbers) of multiband topological insulators for the first time. The gauge symmetry of the network guarantees that the predicted quantity is a topological invariant. A major technical challenge is that the relevant gauge equivariant networks are plagued by instabilities in their training, severely limiting their usefulness. In particular, for larger gauge groups the instabilities make training impossible. We resolve this problem by introducing a novel gauge equivariant normalization layer which stabilizes the training. Furthermore, we prove a universal approximation theorem for our model. We train on samples with trivial Chern number only but show that our model generalizes to samples with non-trivial Chern number and provide various ablations of our setup.

AI-generated music is becoming increasingly common and increasingly difficult to recognise. Here are the tell-tale signs that reveal whether a song is AI-generated – and what this development means for the music industry.

We propose a Koopman-enhanced physics-informed neural network (K--PINN) framework for parameter inference and forecasting in nonlinear epidemic models. This method combines Koopman operator theory and physics-informed learning. It maps epidemic states into a latent observable space where the dynamics evolve approximately linearly while satisfying the governing epidemic equations through automatic differentiation. This integration improves interpretability, parameter identifiability, and long-term predictive stability. We apply the proposed framework to a normalized SEIRSD epidemic model and evaluate it using synthetic monkeypox (Mpox) data and real-world datasets from Germany, Morocco, and Sweden for the SARS-CoV-2 virus. Synthetic trajectories are generated using a structure-preserving, nonstandard finite difference scheme to ensure reliable training data. Numerical results demonstrate that K--PINN achieves more accurate parameter estimation, trajectory reconstruction, and long-term forecasting than classical PINNs and Koopman-EDMD approaches. These results suggest that K--PINN is an effective machine learning framework for epidemic modeling that can be extended to more complex systems.

Gaussian processes with stationary kernels on bounded domains exhibit inflated posterior variance near the boundary. Despite being a long-recognized artifact in geostatistics and a source of over-exploration in Bayesian optimization, the causes and effects of boundary-induced acquisition bias are underexplored. We trace the root cause to a simple geometric mechanism: the truncation of the kernel correlation neighborhood at the domain boundary creates an observation-independent distortion that worsens with dimensionality. We show how this distortion manifests across three acquisition classes: variance maximization concentrates selections at the corners, whereas negative integrated posterior variance and expected predictive information gain move selections inward to axis-aligned interior shells. These patterns arise without reference to any objective function, meaning that acquisition behavior can be dominated by kernel geometry rather than the desired task-specific uncertainty. To quantify this, we introduce a function-free selection-profile diagnostic for arbitrary acquisitions, kernels, and bounded-domain geometries.