A new bill moving through the Florida Senate would give homeowners the right to use "reasonable force" to take down drones infringing on their right to privacy, directly conflicting with federal airspace regulations while raising new legal questions regarding how far a person can go to defend their home from surveillance. The bill primarily focuses on further regulating the use of unmanned aircraft systems (UAS) while broadening the scope of locations that are protected from drone flights within the state, such as airports and correctional facilities. Notably, the bill would permit homeowners to use "reasonable force" to stop a drone from infringing on their expectation of privacy. A bill proposed in the Florida Senate would allow homeowners to use "reasonable force" to take down drones infringing on their right to privacy. "No one wants to have a drone sitting over their property, filming what they do for any number of reasons," Florida-based attorney Raul Gastesi told Fox News Digital.

Russia unleashed a barrage of missile and drone strikes on Ukraine as a short-lived Easter ceasefire expired. Russian forces launched 96 drones and three missiles on eastern and southern Ukraine overnight, Ukraine's Air Force reported on Monday. The swift return to major hostilities following a pause declared by Russian President Vladimir Putin comes as the United States struggles to persuade Moscow to agree on a longer-term ceasefire. The overnight assault targeted Ukraine's Kharkiv, Dnipropetrovsk and Cherkasy regions, the Air Force wrote on Telegram. Air defence units intercepted 42 drones and redirected another 47.

World economic and finance chiefs want an off-ramp from the worst global trade crisis in a century. Washington makes for a turbulent backdrop to the spring meetings of the International Monetary Fund and World Bank, headquartered in the U.S. capital as anchors of America's economic and financial clout. President Donald Trump's tariff war hasn't just roiled markets and raised recession fears: it's also called into question U.S. economic and security leadership -- a pillar of the post-World War II global order -- like never before. The stage is set for "one of the most stark and dramatic meetings I can think of in recent history," says Josh Lipsky, senior director of the GeoEconomics Center at the Atlantic Council and former IMF adviser. "You have at this moment a deep challenge to the multilateral rules-based system which the U.S. helped build."

At least three blasts were heard in the Russian-controlled Donetsk region in eastern Ukraine amid an Easter ceasefire declared by Moscow, Russian state news agency TASS reported, citing local "operative services." Ukraine's forces reported nearly 3,000 violations of Russia's own ceasefire pledge, Ukrainian President Volodymyr Zelenskyy said, adding that Kyiv's forces were instructed to mirror the Russian Army's actions. Russia's Ministry of Defence said Ukraine had broken the Easter ceasefire declared by the Kremlin more than a thousand times, claiming that Ukrainian forces shot at Russian positions 444 times. The ministry also said Kremlin forces encountered more than 900 Ukrainian drone attacks during this time. At least three blasts were heard in the Russian-controlled Donetsk region in eastern Ukraine amid an Easter ceasefire declared by Moscow, Russian state news agency TASS reported, citing local "operative services."

In the early hours of Monday, residents in several Ukrainian cities, including the capital Kyiv, were urged by local authorities to go immediately to nearby shelters due to the threat of drone strikes. The BBC has not independently verified the claims by Kyiv and Moscow. US President Donald Trump - who has been pushing for an end to the war - said late on Sunday that "hopefully Russia and Ukraine will make a deal this week". He gave no further details. Russia launched a full-scale invasion of Ukraine on 24 February 2022, and currently controls about 20% Ukraine's territory, including the southern Crimea peninsula annexed by Moscow in 2014.

On a misty Saturday afternoon in Shenzhen's Central Park, a gaggle of teenage girls are sheltering from the drizzle under a concrete canopy. With their bags of crisps piled high in front of them, they crowd around a couple of smartphones to sing along to Mandopop ballads. The sound of their laughter rings out across the surrounding lawn – until it is pierced by a mechanical buzzing sound. A few metres away from the impromptu karaoke session is an "airdrop cabinet", one of more than 40 in Shenzhen that is operated by Meituan, China's biggest food delivery platform. Hungry park-goers can order anything from rice noodles to Subway sandwiches to bubble tea.

Inspired by the Kolmogorov-Arnold superposition theorem, Kolmogorov-Arnold Networks (KANs) have recently emerged as an improved backbone for most deep learning frameworks, promising more adaptivity than their multilayer perception (MLP) predecessor by allowing for trainable spline-based activation functions. In this paper, we probe the theoretical foundations of the KAN architecture by showing that it can optimally approximate any Besov function in $B^{s}_{p,q}(\mathcal{X})$ on a bounded open, or even fractal, domain $\mathcal{X}$ in $\mathbb{R}^d$ at the optimal approximation rate with respect to any weaker Besov norm $B^{\alpha}_{p,q}(\mathcal{X})$; where $\alpha < s$. We complement our approximation guarantee with a dimension-free estimate on the sample complexity of a residual KAN model when learning a function of Besov regularity from $N$ i.i.d. noiseless samples. Our KAN architecture incorporates contemporary deep learning wisdom by leveraging residual/skip connections between layers.

We propose a scalable, approximate inference hypernetwork framework for a general model of history-dependent processes. The flexible data model is based on a neural ordinary differential equation (NODE) representing the evolution of internal states together with a trainable observation model subcomponent. The posterior distribution corresponding to the data model parameters (weights and biases) follows a stochastic differential equation with a drift term related to the score of the posterior that is learned jointly with the data model parameters. This Langevin sampling approach offers flexibility in balancing the computational budget between the evaluation cost of the data model and the approximation of the posterior density of its parameters. We demonstrate performance of the hypernetwork on chemical reaction and material physics data and compare it to mean-field variational inference.

Childhood vaccination is a cornerstone of public health, yet disparities in vaccination coverage persist across England. These disparities are shaped by complex interactions among various factors, including geographic, demographic, socioeconomic, and cultural (GDSC) factors. Previous studies mostly rely on cross-sectional data and traditional statistical approaches that assess individual or limited sets of variables in isolation. Such methods may fall short in capturing the dynamic and multivariate nature of vaccine uptake. In this paper, we conducted a longitudinal machine learning analysis of childhood vaccination coverage across 150 districts in England from 2021 to 2024. Using vaccination data from NHS records, we applied hierarchical clustering to group districts by vaccination coverage into low- and high-coverage clusters. A CatBoost classifier was then trained to predict districts' vaccination clusters using their GDSC data. Finally, the SHapley Additive exPlanations (SHAP) method was used to interpret the predictors' importance. The classifier achieved high accuracies of 92.1, 90.6, and 86.3 in predicting districts' vaccination clusters for the years 2021-2022, 2022-2023, and 2023-2024, respectively. SHAP revealed that geographic, cultural, and demographic variables, particularly rurality, English language proficiency, the percentage of foreign-born residents, and ethnic composition, were the most influential predictors of vaccination coverage, whereas socioeconomic variables, such as deprivation and employment, consistently showed lower importance, especially in 2023-2024. Surprisingly, rural districts were significantly more likely to have higher vaccination rates. Additionally, districts with lower vaccination coverage had higher populations whose first language was not English, who were born outside the UK, or who were from ethnic minority groups.

Given a sequence of tokens generated by a language model, we may want to identify the preceding tokens that influence the model to generate this sequence. Performing such token attribution is expensive; a common approach is to ablate preceding tokens and directly measure their effects. To reduce the cost of token attribution, we revisit attention weights as a heuristic for how a language model uses previous tokens. Naive approaches to attribute model behavior with attention (e.g., averaging attention weights across attention heads to estimate a token's influence) have been found to be unreliable. To attain faithful attributions, we propose treating the attention weights of different attention heads as features. This way, we can learn how to effectively leverage attention weights for attribution (using signal from ablations). Our resulting method, Attribution with Attention (AT2), reliably performs on par with approaches that involve many ablations, while being significantly more efficient. To showcase the utility of AT2, we use it to prune less important parts of a provided context in a question answering setting, improving answer quality. We provide code for AT2 at https://github.com/MadryLab/AT2 .