Revealed: Chilling text NASCAR star Greg Biffle's wife sent to her mom just minutes before tragic plane crash'Old age' doesn't kill us... scientists reveal true causes of death Immutable: I can't get enough of Melania, the Real Housewife of Washington, says JAN MOIR The tiny diet change that brought down my sky-high cholesterol WITHOUT statins or drugs. Mike was told he risked a heart attack or stroke. CNBC anchor who slammed Trump's tariffs as'insane' stunned live on air as inflation figures send shockwaves through Wall Street Dramatic bodycam video shows moment suspected kidnapper is arrested after 40 years on the run... as her neighbor thinks arrest is a joke Rob Reiner's'petrified' parting words about son Nick at Conan O'Brien party... and why his haunted A-list friends can't stop talking about it Reiner family bombshell as insiders reveal who is paying for Nick's celebrity lawyer... their secret motive... and who will REALLY inherit $200m fortune Doctors said my hip pain was just tendinitis from sitting all day at work. The real cause may kill me... they had left it far too late Bondi hero is handed $2.5million cheque in his hospital bed - as hero asks unbelievable question Pete Davidson is a dad! Kim Kardashian's ex welcomes first child with model girlfriend Elsie Hewitt Mica Miller's pastor husband is indicted for shocking acts before his wife was killed days after filing for divorce Transportation Secretary Sean Duffy's daughter slams TSA as'unconstitutional' after she was subjected to a pat down for refusing to go through the body scanner Jeffrey Epstein attended dinner with tech billionaires three years after he was convicted of sex crimes - as new photos of the event are released from pedophile's estate Terrifying maps break down exactly who is at risk of new'super flu' exploding across America... as doctors reveal symptoms to really worry about READ MORE: Blind psychic Baba Vanga's world-changing 2025 prophecy feared to occur in just DAYS A blind psychic, who allegedly foretold 9/11 and the Covid pandemic, shared several world-changing prophecies for 2026 before her death nearly 30 years ago. Baba Vanga was a Bulgarian mystic and clairvoyant who became a cult figure among conspiracy theorists after several of her eerie pronouncements were proved true.

What is in the 28-point US plan for Ukraine? 'Ukraine is running out of men, money and time' Can the US get all sides to end the war? Why is Europe opposing Trump's peace plan? Three people, including two crew members of a cargo vessel, were killed in overnight Ukrainian drone attacks on the Russian port of Rostov-on-Don and the town of Bataysk in the country's southern Rostov region, local governor Yury Slyusar said. Russian strikes near Ukraine's Black Sea port of Odesa killed a woman in her car and hit infrastructure.

Proven techniques to escape frustration AI customer service loops, including magic words and confusion tactics that force human transfers.

Generalizing causal knowledge across diverse environments is challenging, especially when estimates from large-scale datasets must be applied to smaller or systematically different contexts, where external validity is critical. Model-based estimators of individual treatment effects (ITE) from machine learning require large sample sizes, limiting their applicability in domains such as behavioral sciences with smaller datasets. We demonstrate how estimation of ITEs with Treatment Agnostic Representation Networks (TARNet; Shalit et al., 2017) can be improved by leveraging knowledge from source datasets and adapting it to new settings via transfer learning (TL-TARNet; Aloui et al., 2023). In simulations that vary source and sample sizes and consider both randomized and non-randomized intervention target settings, the transfer-learning extension TL-TARNet improves upon standard TARNet, reducing ITE error and attenuating bias when a large unbiased source is available and target samples are small. In an empirical application using the India Human Development Survey (IHDS-II), we estimate the effect of mothers' firewood collection time on children's weekly study time; transfer learning pulls the target mean ITEs toward the source ITE estimate, reducing bias in the estimates obtained without transfer. These results suggest that transfer learning for causal models can improve the estimation of ITE in small samples.

Intestinal parasites that still plague us today were all over Roman Britain. Breakthroughs, discoveries, and DIY tips sent every weekday. The soldiers guarding the Roman Empire's northwestern frontier had a real parasite problem. Scientists analyzing the sewer drains from the Roman fort Vindolanda (near Hadrian's Wall in northern England) found three types of intestinal parasites --roundworm,whipworm, and . The findings published in the journal mark the first time that has been documented in Roman Britain.

Inspired by biology, spiking neural networks (SNNs) process information via discrete spikes over time, offering an energy-efficient alternative to the classical computing paradigm and classical artificial neural networks (ANNs). In this work, we analyze the representational power of SNNs by viewing them as sequence-to-sequence processors of spikes, i.e., systems that transform a stream of input spikes into a stream of output spikes. We establish the universal representation property for a natural class of spike train functions. Our results are fully quantitative, constructive, and near-optimal in the number of required weights and neurons. The analysis reveals that SNNs are particularly well-suited to represent functions with few inputs, low temporal complexity, or compositions of such functions. The latter is of particular interest, as it indicates that deep SNNs can efficiently capture composite functions via a modular design. As an application of our results, we discuss spike train classification. Overall, these results contribute to a rigorous foundation for understanding the capabilities and limitations of spike-based neuromorphic systems.

The main goal of generative modeling is to use finitely many samples from a distribution to construct a sampling scheme capable of generating new samples from the same distribution. Among the families of existing generative models, flow matching (FM) [23, 24] is notable for its flexibility and simplicity. Given a target probability distribution, FM utilizes a parametric model (e.g., neural network) to learn the velocity vector field that defines a deterministic, continuous transformation (a normalizing flow) and transports a source probability distribution (e.g., standard Gaussian) to the target distribution. While the population formulation of FM often exhibits appealing structure--sometimes even admitting gradient-field velocities--practical models are trained on finite datasets and therefore optimize empirical objectives. This empirical setting substantially alters the geometry of the learned velocity field and the energetic properties of the resulting sampler. These notes aim to clarify how empirical FM behaves, how it differs from its population counterpart, and what implicit biases arise in the learned sampling dynamics. From now on, we assume that all the probability distributions/measures (except the empirical distribution) of the random variables considered are absolutely continuous (i.e., they have densities with respect to the Lebesgue measure), in which case we shall abuse the notation and use the same symbol to denote both the distribution and the density. To maintain the flow of the main text, we defer discussion of related work and all proofs of the theoretical results to the appendix.

We introduce Stackelberg Learning from Human Feedback (SLHF), a new framework for preference optimization. SLHF frames the alignment problem as a sequential-move game between two policies: a Leader, which commits to an action, and a Follower, which responds conditionally on the Leader's action. This approach decomposes preference optimization into a refinement problem for the Follower and an optimization problem against an adversary for the Leader. Unlike Reinforcement Learning from Human Feedback (RLHF), which assigns scalar rewards to actions, or Nash Learning from Human Feedback (NLHF), which seeks a simultaneous-move equilibrium, SLHF leverages the asymmetry of sequential play to capture richer preference structures. The sequential design of SLHF naturally enables inference-time refinement, as the Follower learns to improve the Leader's actions, and these refinements can be leveraged through iterative sampling. We compare the solution concepts of SLHF, RLHF, and NLHF, and lay out key advantages in consistency, data sensitivity, and robustness to intransitive preferences. Experiments on large language models demonstrate that SLHF achieves strong alignment across diverse preference datasets, scales from 0.5B to 8B parameters, and yields inference-time refinements that transfer across model families without further fine-tuning.

Modern generative models hold great promise for accelerating diverse tasks involving the simulation of physical systems, but they must be adapted to the specific constraints of each domain. Significant progress has been made for biomolecules and crystalline materials. Here, we address amorphous materials (glasses), which are disordered particle systems lacking atomic periodicity. Sampling equilibrium configurations of glass-forming materials is a notoriously slow and difficult task. This obstacle could be overcome by developing a generative framework capable of producing equilibrium configurations with well-defined likelihoods. In this work, we address this challenge by leveraging an equivari-ant Riemannian stochastic interpolation framework which combines Riemannian stochastic interpolant and equivariant flow matching. Our method rigorously incorporates periodic boundary conditions and the symmetries of multi-component particle systems, adapting an equivariant graph neural network to operate directly on the torus. Our numerical experiments on model amorphous systems demonstrate that enforcing geometric and symmetry constraints significantly improves generative performance.

Survival analysis encompasses a broad range of methods for analyzing time-to-event data, with one key objective being the comparison of survival curves across groups. Traditional approaches for identifying clusters of survival curves often rely on computationally intensive bootstrap techniques to approximate the null hypothesis distribution. While effective, these methods impose significant computational burdens. In this work, we propose a novel approach that leverages the k-means and log-rank test to efficiently identify and cluster survival curves. Our method eliminates the need for computationally expensive resampling, significantly reducing processing time while maintaining statistical reliability. By systematically evaluating survival curves and determining optimal clusters, the proposed method ensures a practical and scalable alternative for large-scale survival data analysis. Through simulation studies, we demonstrate that our approach achieves results comparable to existing bootstrap-based clustering methods while dramatically improving computational efficiency. These findings suggest that the log-rank-based clustering procedure offers a viable and time-efficient solution for researchers working with multiple survival curves in medical and epidemiological studies.