Russian attacks on Ukraine energy sites'particularly depraved', UK PM Starmer says Russia's attacks on Ukraine's energy sector on Monday night - as temperatures dropped to -20C (-4F) - were barbaric and particularly depraved, UK Prime Minister Sir Keir Starmer has said. He made the comments after speaking to US President Donald Trump hours after Russia hit power plants and critical infrastructure in the capital, Kyiv, and elsewhere. The attacks came at the end of a week-long pause that Trump had asked Russia's President Vladimir Putin to observe as a fierce cold swept Ukraine. Trump said on Tuesday that Putin had kept his word and that he would like him to end the war. Top US envoys are meeting negotiators from Russia and Ukraine in Abu Dhabi on Wednesday and Thursday.

Russia has launched its most powerful blow against Ukraine's energy sector so far this year, according to the private energy company, DTEK. The combined missile and drone strikes which targeted power plants and infrastructure in Kyiv and multiple locations left the system operating with serious restrictions, it said. The strikes were launched as temperatures dropped to -20C (-4F) and left more than 1,000 tower blocks in the capital without heating once again and damaged a power plant in the eastern city of Kharkiv beyond repair. President Volodymyr Zelensky said Russia was choosing terror and escalation rather than diplomacy to end this war and called for maximum pressure on Moscow from Ukraine's allies. The attack comes after a so-called energy truce agreed by Donald Trump with Vladimir Putin expired at the weekend.

Hundreds of illegal dumps are operating across England, including at least 11 so-called super sites containing tens of thousands of tonnes of rubbish, a BBC investigation has found. Drone footage showed one of the waste dumps in Over, Gloucestershire. Most sites are in countryside locations, often hidden, and on what should be agricultural land. Police say many are run by organised crime gangs, who are making cash by charging much less than legitimate operators to take and bury waste. How the great outdoors went from an escape from the nine to five to a full-time social media job.

Chris Pratt on new film Mercy: I asked to be locked into an executioner's chair Being locked barefoot in an executioner's chair sounds uncomfortable, but that is what Chris Pratt requested for his latest film, Mercy. More familiar as a wisecracking action hero in blockbusters like Guardians of the Galaxy and Jurassic World, this role is quite a departure for him. He plays homicide detective Chris Raven, who's fighting for his life after being accused of murdering his wife. Raven is an alcoholic who wakes in the chair after a drinking binge, with just 90 minutes to convince an AI judge he's innocent, or he'll be executed immediately. The film is set in real time, so we see Raven defend his case - while enduring a crashing hangover.

Megalithic monuments in the otherworldly Orkney Islands remain a fundamental part of the landscape. Sheep linger at the Stones of Stenness, the remnants of a ceremonial circle. The Stones of Stenness, a brood of lichen-encrusted megaliths in the far north of the British Isles, could be mistaken for a latter-day work of land art, one with ominous overtones. The stones stand between two lochs on the largest of the Orkney Islands, off the northeastern tip of mainland Scotland. Three colossal planks of sandstone, ranging in height from fifteen feet nine inches to eighteen feet eight inches, rise from the grass, along with a smaller stone that has the bent shape of a boomerang. In contrast to the rectilinear blocks at Stonehenge, the Stenness megaliths are thin slabs with angled upper edges, like upside-down guillotine blades. Remnants of a ceremonial circle, they are placed twenty or more feet apart, creating a chasm of negative space. The monoliths in "2001: A Space Odyssey" inevitably come to mind. Given that the stones were erected five thousand years ago by a culture that left no trace of its belief system, it is unwise to project modern aesthetics onto them. Still, they can be seen only with living eyes. During a recent visit to Orkney, I kept returning to Stenness, at all hours and in all weather. On drizzly days, with skies hanging low, the stones resemble ladders to nowhere. In bright sun, hidden colors emerge: streaks of blue against gray; white and green spatters of lichen; yellowish stains indicating the presence of limonite, an iron ore. Pockmarks and brittle edges show the abrading action of millennia of wind and rain. I watched as tourists approached the stones and hesitantly touched them, as if afraid. When I put my own hands on the rock, I felt no obvious emanations, though I did not feel nothing. One evening, I leaned on a fence as the sun went down, the horizon glowing orange against a cobalt sky.

Google's ultra-private CEO Sundar Pichai is showing me around Googleplex, its California headquarters. A walkway runs along the length of it, passing by a giant dinosaur skeleton, a beach volleyball pitch and dozens of Googlers lunching under the hazy November sun. But it's a laboratory, hidden away at the back of the campus behind some trees, that he is most excited to show me. This is where the invention that Google believes is its secret weapon is being developed. Known as a Tensor Processing Unit (or TPU), it looks like an unassuming little chip but, says Mr Pichai, it will one day power every AI query that goes through Google.

British soldiers will be granted new powers to shoot down drones threatening military bases. The plans, to be unveiled by Defence Secretary John Healey in a speech on Monday, are intended to allow troops to take faster, more decisive action. Four British airbases used by US forces reported mystery drone sightings last year, while drones have disrupted airspace across Europe a number of times in recent months. The new powers will only apply to military sites, but could be extended to civilian locations such as airports. Healey is set to announce the introduction of a kinetic option, first reported by the Daily Telegraph, that would enable British troops or Ministry of Defence (MoD) police to shoot drones posing a threat to a military site in the UK.

Large language models (LLMs) have grown more powerful in language generation, producing fluent text and even imitating personal style. Yet, this ability also heightens the risk of identity impersonation. To the best of our knowledge, no prior work has examined personalized machine-generated text (MGT) detection. In this paper, we introduce \dataset, the first benchmark for evaluating detector robustness in personalized settings, built from literary and blog texts paired with their LLM-generated imitations. Our experimental results demonstrate large performance gaps across detectors in personalized settings: some state-of-the-art models suffer significant drops. We attribute this limitation to the \textit{feature-inversion trap}, where features that are discriminative in general domains become inverted and misleading when applied to personalized text. Based on this finding, we propose \method, a simple and reliable way to predict detector performance changes in personalized settings. \method identifies latent directions corresponding to inverted features and constructs probe datasets that differ primarily along these features to evaluate detector dependence. Our experiments show that \method can accurately predict both the direction and the magnitude of post-transfer changes, showing 85\% correlation with the actual performance gaps. We hope that this work will encourage further research on personalized text detection.

Knowledge conflict often arises in retrieval-augmented generation (RAG) systems, where retrieved documents may be inconsistent with one another or contradict the model's parametric knowledge. Existing benchmarks for investigating the phenomenon have notable limitations, including a narrow focus on the question answering setup, heavy reliance on entity substitution techniques, and a restricted range of conflict types. To address these issues, we propose a knowledge graph (KG)-based framework that generates varied and subtle conflicts between two similar yet distinct contexts, while ensuring interpretability through the explicit relational structure of KGs. Experimental results on our benchmark, MAGIC, provide intriguing insights into the inner workings of LLMs regarding knowledge conflict: both open-source and proprietary models struggle with conflict detection -- especially when multi-hop reasoning is required -- and often fail to pinpoint the exact source of contradictions. Finally, we present in-depth analyses that serve as a foundation for improving LLMs in integrating diverse, sometimes even conflicting, information.

Automated surgical skill assessment (SSA) is a central task in surgical computer vision. Developing robust SSA models is challenging due to the scarcity of skill annotations, which are time-consuming to produce and require expert consensus. Few-shot learning (FSL) offers a scalable alternative enabling model development with minimal supervision, though its success critically depends on effective pre-training. While widely studied for several surgical downstream tasks, pre-training has remained largely unexplored in SSA. In this work, we formulate SSA as a few-shot task and investigate how self-supervised pre-training strategies affect downstream few-shot SSA performance. We annotate a publicly available robotic surgery dataset with Objective Structured Assessment of Technical Skill (OSATS) scores, and evaluate various pre-training sources across three few-shot settings. We quantify domain similarity and analyze how domain gap and the inclusion of procedure-specific data into pre-training influence transferability. Our results show that small but domain-relevant datasets can outperform large scale, less aligned ones, achieving accuracies of 60.16%, 66.03%, and 73.65% in the 1-, 2-, and 5-shot settings, respectively. Moreover, incorporating procedure-specific data into pre-training with a domain-relevant external dataset significantly boosts downstream performance, with an average gain of +1.22% in accuracy and +2.28% in F1-score; however, applying the same strategy with less similar but large-scale sources can instead lead to performance degradation. Code and models are available at https://github.com/anastadimi/ssa-fsl.