Could Ukraine hold a presidential election right now? Will Europe use frozen Russian assets to fund war? How can Ukraine rebuild China ties? 'Ukraine is running out of men, money and time' Russian forces began a "massive attack" on Ukraine on Monday night, killing three people and targeting 13 regions with 650 drones and 30 missiles, Ukrainian President Volodymyr Zelenskyy said in a post on X. Those killed in the overnight attack included a four-year-old girl in the central Zhytomyr region, Governor Vitalii Bunechko said on Telegram.

Reliable estimation of feature contributions in machine learning models is essential for trust, transparency and regulatory compliance, especially when models are proprietary or otherwise operate as black boxes. While permutation-based methods are a standard tool for this task, classical implementations rely on repeated random permutations, introducing computational overhead and stochastic instability. In this paper, we show that by replacing multiple random permutations with a single, deterministic, and optimal permutation, we achieve a method that retains the core principles of permutation-based importance while being non-random, faster, and more stable. We validate this approach across nearly 200 scenarios, including real-world household finance and credit risk applications, demonstrating improved bias-variance tradeoffs and accuracy in challenging regimes such as small sample sizes, high dimensionality, and low signal-to-noise ratios. Finally, we introduce Systemic Variable Importance, a natural extension designed for model stress-testing that explicitly accounts for feature correlations. This framework provides a transparent way to quantify how shocks or perturbations propagate through correlated inputs, revealing dependencies that standard variable importance measures miss. Two real-world case studies demonstrate how this metric can be used to audit models for hidden reliance on protected attributes (e.g., gender or race), enabling regulators and practitioners to assess fairness and systemic risk in a principled and computationally efficient manner.

We provide practical, efficient, and nonparametric methods for auditing the fairness of deployed classification and regression models. Whereas previous work relies on a fixed-sample size, our methods are sequential and allow for the continuous monitoring of incoming data, making them highly amenable to tracking the fairness of real-world systems. We also allow the data to be collected by a probabilistic policy as opposed to sampled uniformly from the population. This enables auditing to be conducted on data gathered for another purpose. Moreover, this policy may change over time and different policies may be used on different subpopulations. Finally, our methods can handle distribution shift resulting from either changes to the model or changes in the underlying population. Our approach is based on recent progress in anytime-valid inference and game-theoretic statistics---the ``testing by betting'' framework in particular. These connections ensure that our methods are interpretable, fast, and easy to implement. We demonstrate the efficacy of our approach on three benchmark fairness datasets.

Federated Learning (FL) is a novel approach enabling several clients holding sensitive data to collaboratively train machine learning models, without centralizing data. The cross-silo FL setting corresponds to the case of few ($2$--$50$) reliable clients, each holding medium to large datasets, and is typically found in applications such as healthcare, finance, or industry. While previous works have proposed representative datasets for cross-device FL, few realistic healthcare cross-silo FL datasets exist, thereby slowing algorithmic research in this critical application. In this work, we propose a novel cross-silo dataset suite focused on healthcare, FLamby (Federated Learning AMple Benchmark of Your cross-silo strategies), to bridge the gap between theory and practice of cross-silo FL.FLamby encompasses 7 healthcare datasets with natural splits, covering multiple tasks, modalities, and data volumes, each accompanied with baseline training code. As an illustration, we additionally benchmark standard FL algorithms on all datasets.Our flexible and modular suite allows researchers to easily download datasets, reproduce results and re-use the different components for their research.

Nicknamed Dracula's Chivito, the disk is 1,000 light-years away from Earth. Breakthroughs, discoveries, and DIY tips sent every weekday. Scientists are leaving space fans with one more treat before the year comes to a close. Using the Hubble Space Telescope, astronomers captured a stunning image of the largest protoplanetary disk ever observed, which just happens to be shaped like a giant celestial sandwich. The massive formation of dust and gas, which astronomers call Dracula's Chivito, resides about 1,000 light-years from Earth and spans roughly 400 billion miles.

Deep neural networks set the state-of-the-art across many tasks in computer vision, but their generalization ability to simple image distortions is surprisingly fragile. In contrast, the mammalian visual system is robust to a wide range of perturbations. Recent work suggests that this generalization ability can be explained by useful inductive biases encoded in the representations of visual stimuli throughout the visual cortex. Here, we successfully leveraged these inductive biases with a multi-task learning approach: we jointly trained a deep network to perform image classification and to predict neural activity in macaque primary visual cortex (V1) in response to the same natural stimuli. We measured the out-of-distribution generalization abilities of our resulting network by testing its robustness to common image distortions.

Breakthroughs, discoveries, and DIY tips sent every weekday. From Iceland's spectacular fire and ice landscapes to Yemen's otherworldly Socotra dragon trees, our home planet hosts a diverse lineup of jaw-dropping scenery. The 12th annual International Landscape Photographer of the Year award honor professional and amateur photographers who venture far and wide to capture nature's beauty. Why do we have five fingers and toes? Breakthroughs, discoveries, and DIY tips sent every weekday.

Supervised classification techniques use training samples to find classification rules with small expected 0-1 loss. Conventional methods achieve efficient learning and out-of-sample generalization by minimizing surrogate losses over specific families of rules. This paper presents minimax risk classifiers (MRCs) that do not rely on a choice of surrogate loss and family of rules. MRCs achieve efficient learning and out-of-sample generalization by minimizing worst-case expected 0-1 loss w.r.t.

Three killed after Russia launches'massive' attack across Ukraine Russia carried out a massive overnight attack on several Ukrainian cities, President Volodymyr Zelensky has said, a day after he warned of strikes over the Christmas period. At least three people were killed, according to Ukrainian officials, including a four-year-old child, while energy infrastructure was also targeted, leaving several regions without power. Russia launched 635 drones and 38 missiles, Ukraine's air force said, adding that 621 of them were downed. Zelensky said people simply want to be with their families, at home, and safe in the run-up to Christmas, and said the strikes sent an extremely clear signal about Russia's priorities despite ongoing peace talks. He added that Russian President Vladimir Putin still cannot accept that he must stop killing.

Why do we have five fingers and toes? It all goes back to our fishy ancestors. The answer to why we have five fingers and toes is surprisingly difficult to suss out. Breakthroughs, discoveries, and DIY tips sent every weekday. The popular nursery rhyme is an early childhood memory for many of us.