Ominous warning for humanity as birds suddenly adopt'unsettling' behavior Meghan is accused of'giggling as model stumbles on the catwalk': More Paris Fashion Week disasters emerge, including awkward moment with Kristin Scott Thomas More girls are starting their periods younger than ever before - scientists think they've finally found what's causing it The TRUTH to the doting mother who slaughtered her children and husband told by those she'd been quietly tormenting for years Insiders confirm what everyone suspects about Taylor Swift and Blake Lively... the private apology... and how any future friendship hangs on one humiliating condition Outrage as Baltimore's Dem mayor spends $164k of taxpayer cash on ultra-luxurious new SUV I have no sympathy for them - but this disturbing new trend isn't the answer: JANA HOCKING Taylor Swift reveals truth behind raunchy song about Travis Kelce's manhood Trump stuns CNN reporter as he muses about Ghislaine Maxwell pardon: 'I haven't heard that name in so long' Revealed: Which slimming jab REALLY works best. The doctors' ultimate expert guide on which to pick, how to save money, beat every side effect... and what you need to know about the'golden dose' Functioning alcoholics hide in plain sight... so are YOU one? Trump brands NFL's Bad Bunny Super Bowl halftime show selection'absolutely ridiculous' The troubled background of delivery man stabbed by Mark Sanchez... as he launches million-dollar lawsuit and sparks civil war at Fox People who don't like animals are more likely to have dark personality traits, study finds On-screen psychopaths such as Patrick Bateman in'American Psycho' and Villanelle in ' Killing Eve ' are often depicted hurting animals. Now, a study suggests this character flaw is not just the stuff of fiction. Scientists in Serbia have found a link between psychopathy and the belief that animals aren't as worthy as humans.

Clash of the White House titans: Two of Trump's most powerful lieutenants go to WAR with each other - after vicious leak sent shockwaves The troubled background of delivery man stabbed by Mark Sanchez... as he launches million-dollar lawsuit and sparks civil war at Fox Ominous warning for humanity as birds suddenly adopt'unsettling' behavior The TRUTH to the doting mother who slaughtered her children and husband told by those she'd been quietly tormenting for years Brazilian fashion influencer Junior Dutra dies at age 31 after alleged'fox eyes' procedure complications I've seen AI try to ESCAPE labs. The apocalypse is already here... and our children will be the first victims Trump brands NFL's Bad Bunny Super Bowl halftime show selection'absolutely ridiculous' Investigators reveal there is'no evidence' of arson after horror blaze destroyed South Carolina judge's beachfront home Functioning alcoholics hide in plain sight... so are YOU one? It sounds like the start of a sci-fi film, but scientists have shown that AI can design brand-new infectious viruses the first time. Experts at Stanford University in California used'Evo' - an AI tool that creates genomes from scratch. Amazingly, the tool was able to create viruses that are able to infect and kill specific bacteria.

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This material may not be published, broadcast, rewritten, or redistributed. Quotes displayed in real-time or delayed by at least 15 minutes. Market data provided by Factset . Powered and implemented by FactSet Digital Solutions . Mutual Fund and ETF data provided by Refinitiv Lipper .

OSAKA - Emperor Naruhito and Empress Masako on Monday visited the World Expo in the city of Osaka for the second time. At the Future of Life pavilion, which envisions a society 50 years from now, the Imperial couple viewed exhibits featuring androids and robots. The emperor showed his fascination at an android modeled after Natsume Soseki (1867-1916), saying that the Japanese novelist continues to live on. The emperor and the empress commented that the androids looked very human, according to Japanese roboticist Hiroshi Ishiguro, a professor at the University of Osaka, who guided the couple. At the U.N. Pavilion, run by the United Nations, the Imperial couple watched a video introducing the world body with solemn expressions.

Can Ukraine restore its pre-war borders? Why are Tomahawk missiles for Ukraine a'red line' for Russia? Is Russia testing NATO with aerial incursions in Europe? The UN's International Atomic Energy Agency (IAEA) said that "two rounds of shelling struck around 1.25 km" [less than a mile] from the perimeter of Ukraine's Zaporizhzhia Nuclear Power Plant on Monday afternoon. IAEA chief Rafael Grossi warned the attacks came as the plant has been running on emergency diesel generators for almost two weeks after losing its external power source.

Policy targets evolve faster than the Couple Model Intercomparison Project cycles, complicating adaptation and mitigation planning that must often contend with outdated projections. Climate model output emulators address this gap by offering inexpensive surrogates that can rapidly explore alternative futures while staying close to Earth System Model (ESM) behavior. We focus on emulators designed to provide inputs to impact models. Using monthly ESM fields of near-surface temperature, precipitation, relative humidity, and wind speed, we show that deep generative models have the potential to model jointly the distribution of variables relevant for impacts. The specific model we propose uses score-based diffusion on a spherical mesh and runs on a single mid-range graphical processing unit. We introduce a thorough suite of diagnostics to compare emulator outputs with their parent ESMs, including their probability densities, cross-variable correlations, time of emergence, or tail behavior. We evaluate performance across three distinct ESMs in both pre-industrial and forced regimes. The results show that the emulator produces distributions that closely match the ESM outputs and captures key forced responses. They also reveal important failure cases, notably for variables with a strong regime shift in the seasonal cycle. Although not a perfect match to the ESM, the inaccuracies of the emulator are small relative to the scale of internal variability in ESM projections. We therefore argue that it shows potential to be useful in supporting impact assessment. We discuss priorities for future development toward daily resolution, finer spatial scales, and bias-aware training. Code is made available at https://github.com/shahineb/climemu.

Learning graphical conditional independence structures from nonlinear, continuous or mixed data is a central challenge in machine learning and the sciences, and many existing methods struggle to scale to thousands of samples or hundreds of variables. We introduce two basis-expansion tools for scalable causal discovery. First, the Basis Function BIC (BF-BIC) score uses truncated additive expansions to approximate nonlinear dependencies. BF-BIC is theoretically consistent under additive models and extends to post-nonlinear (PNL) models via an invertible reparameterization. It remains robust under moderate interactions and supports mixed data through a degenerate-Gaussian embedding for discrete variables. In simulations with fully nonlinear neural causal models (NCMs), BF-BIC outperforms kernel- and constraint-based methods (e.g., KCI, RFCI) in both accuracy and runtime. Second, the Basis Function Likelihood Ratio Test (BF-LRT) provides an approximate conditional independence test that is substantially faster than kernel tests while retaining competitive accuracy. Extensive simulations and a real-data application to Canadian wildfire risk show that, when integrated into hybrid searches, BF-based methods enable interpretable and scalable causal discovery. Implementations are available in Python, R, and Java.

We present self-speculative masked diffusions, a new class of masked diffusion generative models for discrete data that require significantly fewer function evaluations to generate samples. Standard masked diffusion models predict factorized logits over currently masked positions. A number of masked positions are then sampled, however, the factorization approximation means that sampling too many positions in one go leads to poor sample quality. As a result, many simulation steps and therefore neural network function evaluations are required to generate high-quality data. We reduce the computational burden by generating non-factorized predictions over masked positions. This is achieved by modifying the final transformer attention mask from non-causal to causal, enabling draft token generation and parallel validation via a novel, model-integrated speculative sampling mechanism. This results in a non-factorized predictive distribution over masked positions in a single forward pass. We apply our method to GPT2 scale text modelling and protein sequences generation, finding that we can achieve a ~2x reduction in the required number of network forward passes relative to standard masked diffusion models.

The introduction of machine learning methods has led to significant advances in automation, optimization, and discoveries in various fields of science and technology. However, their widespread application faces a fundamental limitation: the transfer of models between data domains generally lacks a rigorous mathematical justification. The key problem is the lack of formal criteria to guarantee that a model trained on one type of data will retain its properties on another.This paper proposes a solution to this problem by formalizing the concept of analogy between data sets and models using first-order logic and Hoare logic.We formulate and rigorously prove a theorem that sets out the necessary and sufficient conditions for analogy in the task of knowledge transfer between machine learning models. Practical verification of the analogy theorem on model data obtained using the Monte Carlo method, as well as on MNIST and USPS data, allows us to achieving F1 scores of 0.84 and 0.88 for convolutional neural networks and random forests, respectively.The proposed approach not only allows us to justify the correctness of transfer between domains but also provides tools for comparing the applicability of models to different types of data.The main contribution of the work is a rigorous formalization of analogy at the level of program logic, providing verifiable guarantees of the correctness of knowledge transfer, which opens new opportunities for both theoretical research and the practical use of machine learning models in previously inaccessible areas.