NBC anchor Savannah Guthrie's mother has been abducted, sheriff suspects The mother of US news anchor Savannah Guthrie has been abducted and didn't go willingly from her home, Arizona law enforcement officials suspect. Nancy Guthrie, the 84-year-old mother of the NBC News host, was last seen in her house outside Tucson, Arizona, on Saturday evening. Her family reported her missing a day later. When authorities arrived, the scene of Nancy Guthrie's property caused grave concern, Pima County Sheriff Chris Nanos said. He did not provide a possible motive and, while there was no initial indication Nancy Guthrie could have been targeted because of her name, the sheriff said we can't dismiss that. I believe she was abducted, yes, Sheriff Nanos told CBS, the BBC's US partner.

Elon Musk's SpaceX is taking over his artificial intelligence (AI) start-up, as the billionaire continues to unify some of his many business interests. SpaceX confirmed the deal to acquire xAI, a smaller firm known for its Grok chatbot, posting a memo from Musk about the merger on its website. In the note, Musk said the combination would form an innovation engine putting AI, rockets, space-based internet, and media under one roof. Terms of the deal were not disclosed. However, a source familiar said it valued xAI at $125bn (£91bn) and SpaceX at $1tn, making it the most valuable private company ever.

Students originally from overseas attend entrance exam preparation classes for high school advancement at YSC Global School in the city of Fussa, Tokyo, on Jan. 22. As policies related to foreign nationals are expected to be a major issue in Sunday's Lower House election in Japan, some are calling for improvements to Japanese language education for the children of foreign residents. In 2010, Youth Support Center, a nonprofit organization in the city of Fussa, Tokyo, established YSC Global School to provide Japanese language education and support for high school entry for children and young people with foreign roots, tailored to their proficiency levels. The school offers a total of 14 face-to-face and online courses and annually admits about 250 to 300 children from countries such as China, the Philippines and Nepal. Limited classrooms and instructors, however, hinder its ability to accommodate more students. In a time of both misinformation and too much information, quality journalism is more crucial than ever.

Elon Musk said on Monday that SpaceX has acquired his artificial intelligence startup, xAI, in a record-setting deal that unifies the billionaire's AI and space ambitions by combining the rocket-and-satellite company with the maker of the Grok chatbot. The deal, first reported last week, represents one of the most ambitious tie-ups in the technology sector yet, combining a space-and-defense contractor with a fast-growing AI developer whose costs are largely driven by chips, data centers and energy. It could also bolster SpaceX's data-center ambitions as Musk competes with rivals such as Alphabet's Google, Meta, Amazon-backed Anthropic and OpenAI in the AI sector. In a time of both misinformation and too much information, quality journalism is more crucial than ever. By subscribing, you can help us get the story right. With your current subscription plan you can comment on stories.

The latest batch of Jeffrey Epstein files shed light on the convicted sex offender's ties to Silicon Valley--and Peter Thiel's exacting approach to food. Peter Thiel--the billionaire venture capitalist, PayPal, and Palantir cofounder, and outspoken commentator on all matters relating to the "Antichrist"--appears at least 2,200 times in the latest batch of files released by the Department of Justice related to convicted sex offender and disgraced financier Jeffrey Epstein . The tranche of records demonstrate how Epstein managed to cultivate an extensive network of wealthy and influential figures in Silicon Valley. A number of them, including Thiel, continued to interact with Epstein even after his 2008 guilty plea for solicitation of prostitution and of procurement of minors to engage in prostitution. The new files show that Thiel arranged to meet with Epstein several times between 2014 and 2017.

Barnsley has struggled with unemployment and deprivation since the coal pits closed. Barnsley has struggled with unemployment and deprivation since the coal pits closed. Barnsley rebranded UK's first'tech town' as US giants join AI push In 2002 Barnsley toyed with a redesign as a Tuscan hill village as it sought out a brighter post-industrial future. In 2021 it adopted the airily vague slogan "the place of possibilities". Now it is trying a different image: Britain's first "tech town".

We present the first algorithms for generalized linear contextual bandits under shuffle differential privacy and joint differential privacy. While prior work on private contextual bandits has been restricted to linear reward models -- which admit closed-form estimators -- generalized linear models (GLMs) pose fundamental new challenges: no closed-form estimator exists, requiring private convex optimization; privacy must be tracked across multiple evolving design matrices; and optimization error must be explicitly incorporated into regret analysis. We address these challenges under two privacy models and context settings. For stochastic contexts, we design a shuffle-DP algorithm achieving $\tilde{O}(d^{3/2}\sqrt{T}/\sqrt{\varepsilon})$ regret. For adversarial contexts, we provide a joint-DP algorithm with $\tilde{O}(d\sqrt{T}/\sqrt{\varepsilon})$ regret -- matching the non-private rate up to a $1/\sqrt{\varepsilon}$ factor. Both algorithms remove dependence on the instance-specific parameter $κ$ (which can be exponential in dimension) from the dominant $\sqrt{T}$ term. Unlike prior work on locally private GLM bandits, our methods require no spectral assumptions on the context distribution beyond $\ell_2$ boundedness.

Multimodal learning combines information from multiple data modalities to improve predictive performance. However, modalities often contribute unequally and in a data dependent way, making it unclear which data modalities are genuinely informative and to what extent their contributions can be trusted. Quantifying modality level importance together with uncertainty is therefore central to interpretable and reliable multimodal learning. We introduce conformal Shapley intervals, a framework that combines Shapley values with conformal inference to construct uncertainty-aware importance intervals for each modality. Building on these intervals, we propose a modality selection procedure with a provable op-timality guarantee: conditional on the observed features, the selected subset of modalities achieves performance close to that of the optimal subset. We demonstrate the effectiveness of our approach on multiple datasets, showing that it provides meaningful uncertainty quantification and strong predictive performance while relying on only a small number of informative modalities.

Diffusion models and flow matching have demonstrated remarkable success in text-to-image generation. While many existing alignment methods primarily focus on fine-tuning pre-trained generative models to maximize a given reward function, these approaches require extensive computational resources and may not generalize well across different objectives. In this work, we propose a novel alignment framework by leveraging the underlying nature of the alignment problem -- sampling from reward-weighted distributions -- and show that it applies to both diffusion models (via score guidance) and flow matching models (via velocity guidance). The score function (velocity field) required for the reward-weighted distribution can be decomposed into the pre-trained score (velocity field) plus a conditional expectation of the reward. For the alignment on the diffusion model, we identify a fundamental challenge: the adversarial nature of the guidance term can introduce undesirable artifacts in the generated images. Therefore, we propose a finetuning-free framework that trains a guidance network to estimate the conditional expectation of the reward. We achieve comparable performance to finetuning-based models with one-step generation with at least a 60% reduction in computational cost. For the alignment on flow matching, we propose a training-free framework that improves the generation quality without additional computational cost.

The performance of machine learning surrogates is critically dependent on data quality and quantity. This presents a major challenge, as high-fidelity (HF) data is often scarce and computationally expensive to acquire, while low-fidelity (LF) data is abundant but less accurate. To address this data-scarcity problem, we develop a probabilistic multi-fidelity surrogate framework based on generative transfer learning. We employ a normalizing flow (NF) generative model as the backbone, which is trained in two phases: (i) the NF is first pretrained on a large LF dataset to learn a probabilistic forward model; (ii) the pretrained model is then fine-tuned on a small HF dataset, allowing it to correct for LF-HF discrepancies via knowledge transfer. To relax the dimension-preserving constraint of standard bijective NFs, we integrate surjective (dimension-reducing) layers with standard coupling blocks. This architecture enables learned dimension reduction while preserving the ability to train with exact likelihoods. The resulting surrogate provides fast probabilistic predictions with quantified uncertainty and significantly outperforms LF-only baselines while using fewer HF evaluations. We validate the approach on a reinforced concrete slab benchmark, combining many coarse-mesh (LF) simulations with a limited set of fine-mesh (HF) simulations. The proposed model achieves probabilistic predictions with HF accuracy, demonstrating a practical path toward data-efficient, generative AI-driven surrogates for complex engineering systems. Email address: David.Barajas-Solano@pnnl.gov (David Barajas-Solano) Introduction High-fidelity (HF) computer modeling using discretization schemes such as the finite elements (FE) method provides a rigorous framework for analyzing and predicting the behavior of complex engineering systems.