In this paper we study continuum-marginal optimal transport. Given a time-continuous family of probability marginals, the problem is to recover the minimum-energy velocity field whose flow reproduces every marginal. This problem is the continuum limit of the classical two-marginal Benamou--Brenier formulation, and also the deterministic limit of the Nelson problem of stochastic optimal transport. We propose a practical mesh-free solver for this problem. The weak continuity equation is embedded in a reproducing kernel Hilbert space, yielding a sample-only objective that requires no spatial discretization. The velocity is parametrized by any linear-in-parameters dictionary or neural network, and is optimized by mini-batch stochastic methods. Synthetic experiments confirm that the method achieves accurate drift recovery and marginal consistency. The same computational framework also applies to the stochastic Nelson problem.

Don't let the Artemis II astronauts have all the fun. 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 moon is bombarded by meteoroids the size of ping-pong balls every day. Breakthroughs, discoveries, and DIY tips sent six days a week. Establishing a long-term human presence on the moon is a daunting challenge.

Diffusion models have achieved state-of-the-art performance in generative modeling tasks across various domains. Prior works on time series diffusion models have primarily focused on developing conditional models tailored to specific forecasting or imputation tasks. In this work, we explore the potential of taskagnostic, unconditional diffusion models for several time series applications. We propose TSDiff, an unconditionally-trained diffusion model for time series. Our proposed self-guidance mechanism enables conditioning TSDiff for downstream tasks during inference, without requiring auxiliary networks or altering the training procedure. We demonstrate the effectiveness of our method on three different time series tasks: forecasting, refinement, and synthetic data generation. First, we show that TSDiff is competitive with several task-specific conditional forecasting methods (predict). Second, we leverage the learned implicit probability density of TSDiff to iteratively refine the predictions of base forecasters with reduced computational overhead over reverse diffusion (refine). Notably, the generative performance of the model remains intact -- downstream forecasters trained on synthetic samples from TSDiff outperform forecasters that are trained on samples from other state-of-the-art generative time series models, occasionally even outperforming models trained on real data (synthesize).

A robotic sub explored a hidden world 1,300 meters under Antarctica. 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. Breakthroughs, discoveries, and DIY tips sent six days a week. The Antarctic sea, where glaciers drift across the surface. What kind of world lies 1,300 meters below the surface?

The Download: DeepSeek's latest AI breakthrough, and the race to build world models Plus: China has blocked Meta's $2 billion acquisition of AI startup Manus. On Friday, Chinese AI firm DeepSeek released a preview of V4, its long-awaited new flagship model. Notably, the model can process much longer prompts than its last generation, thanks to a new design that handles large amounts of text more efficiently. While the model remains open source, its performance matches leading closed-source rivals from Anthropic, OpenAI, and Google. Here are three ways V4 could shake up AI . AI systems have already gained impressive mastery over the digital world, but the physical world remains humanity's domain.

What are Russia's gains from the Iran war? 'We are not losers; we are winners' Ukrainian officials say Russian drones have again attacked the southern port city of Odesa, injuring at least 11 people, including two children, and damaging homes and important infrastructure. Odesa Governor Oleh Kiper said the attack affected three districts, hitting residential buildings, vehicles and civilian facilities, including a hotel, warehouses and funicular railway. Windows shattered in many buildings and the port area sustained damage. Law enforcement agencies are documenting the latest war crimes committed by Russia against the peaceful population of [the] Odesa region," Kiper said. Russian attacks killed one person in the southeastern Zaporizhzhia region, according to Governor Ivan Fedorov. "A 59-year-old man died as a result of an enemy attack on the Zaporizhzhia region," Fedorov wrote on Telegram. A Ukrainian drone attack killed an employee at the Zaporizhzhia nuclear power plant, which was captured by Russian forces and is shut down. "A driver was killed today when a Ukrainian Armed Forces drone struck the transport department at the Zaporizhzhia Nuclear Power Plant," said a statement from plant managers who were installed by Russia. Regional governor Fedorov said Russian forces launched 629 strikes across 45 settlements in the region in a single day, with at least 50 reports of damage to homes and infrastructure. Russian officials reported Ukrainian drone attacks in the Belgorod border region, where at least one person was killed and four women injured, alongside damage to buildings and vehicles. The attacks come as diplomatic efforts to end the war remain stalled. Donald Trump said on Sunday that he has had "good conversations" with Presidents Vladimir Putin and Volodymyr Zelenskyy. "We're working on the Russia situation, Russia and Ukraine, and hopefully we're going to get it," Trump said on Fox News. "I do have conversations with him, and I do have conversations with President Zelenskyy, and good conversations," he said. "The hatred between President Putin and President Zelenskyy is ridiculous.

Datacentres could require at least 6GW of capacity by 2030 under government plans to expand AI infrastructure. Datacentres could require at least 6GW of capacity by 2030 under government plans to expand AI infrastructure. Sun 26 Apr 2026 03.00 EDTLast modified on Sun 26 Apr 2026 03.01 EDT One vision of the UKâ s future involves a decarbonised economy powered by clean, renewable energy. Another involves making the UK an AI superpower. The government departments responsible for these two visions do not appear to have agreed on their numbers.