Drone games put Ukraine's best military pilots to the test TRUSKAVETS, Ukraine - In the sky over western Ukraine, a bullet-shaped P1-SUN interceptor drone dived toward its target as dozens of soldiers looked on. A cheer went up as it cut through a tow line from another drone to a balloon, which drifted away. Ukraine's most skilled military drone pilots squared off this week not against Russia, but against each other in a competition to win bragging rights and state-of-the-art hardware for their units. Drone technology has transformed the war in Ukraine. Young men using video game consoles to operate strike drones packed with explosives -- sometimes from command centers far behind the front line -- are deeply feared by enemy soldiers.

Countries including Japan see the ability to control chips, data centers and AI models as directly related to national resilience in a landscape dominated by U.S. and Chinese technology. Sakura Internet's chief said the company may need to hike its capital spending by nearly seven times its initial plan to keep up with artificial intelligence demand in Japan. The data center operator is eyeing an allocation of as much as ¥20 billion to ¥30 billion ($125 million to $190 million) this fiscal year, founder and CEO Kunihiro Tanaka said. That's above the ¥4.4 billion in the Osaka-based company's official capital expenditure plan announced last month. "AI server usage rates are 80% to 90%," Tanaka, 48, said in an interview.

On London's streets, facial recognition tests the balance between security and liberty Temporary street signs warn pedestrians of a Metropolitan Police live facial recognition operation in London on May 11. | REUTERS London - Tourists, shoppers and office workers on a busy London street on an ordinary weekday found themselves part of a digital identity check as live facial recognition cameras scanned faces against a police watchlist. The operation was an example of a technology the Metropolitan Police say is transforming policing, helping officers arrest around 2,500 wanted people since the start of 2024, including suspects accused of violent and sexual offences. Critics, however, say live facial recognition undermines the presumption of innocence underpinning British law by treating every passerby as a potential suspect. 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.

How Saudi Arabia's spending spree reached the end of the line Autocratic monarchs once left an echo of their glory in the ruins of the megaprojects they commanded at the peak of their unchallenged power. Those monumental physical traces are to be found in the fertile plains, mountainsides and deserts of the Middle East. But one of their most prominent modern counterparts may only have a digital footprint to leave behind for some of his most ambitious concepts. A decade ago, the Crown Prince of Saudi Arabia Mohammed bin Salman - or MBS as he is widely known - decreed a revisioning of his country that leapt from the realm of science fiction. It was called Vision 2030. Extraordinary monolithic structures were to help bring forth new technological marvels not just for the Kingdom but for the world.

More information Adding us as a Preferred Source in Google by using this link indicates that you would like to see more of our content in Google News results. Breakthroughs, discoveries, and DIY tips sent six days a week. A tiny, bright blue octopus is small enough to fit inside the palm of your hand, but good luck trying to meet one. According to marine biologists, you'll likely have to settle with admiring it from afar for now unless you have access to a deep sea submersible--and a ticket to the Galápagos Islands . While conducting a deep sea expedition aboard the research vessel E/V, biologists spotted the diminutive invertebrate as they piloted a remotely operated vehicle (ROV) along the ocean floor near Darwin Island.

Making calibrated online predictions is a central challenge in modern AI systems. Much of the existing literature focuses on fully adversarial environments where outcomes may be arbitrary, leading to conservative algorithms that can perform suboptimally in more benign settings, such as when outcomes are nearly stationary. This gap raises a natural question: can we design online prediction algorithms whose calibration error automatically adapts to the degree of non-stationarity in the environment, smoothly interpolating between i.i.d. and adversarial regimes? We answer this question in the affirmative and develop a suite of algorithms that achieve adaptive calibration guarantees under multiple calibration measures. Specifically, with $T$ being the number of rounds, $K$ being the unknown number of i.i.d. segments of the environment, and $C\in[0,T]$ being another unknown non-stationary measure defined as the minimal $\ell_1$ deviation of the mean outcomes, our algorithms attain $\widetilde{O}(\min\{\sqrt{T}+(TC)^{\frac{1}{3}}, \sqrt{KT}\})$ for $\ell_1$ calibration error and $\widetilde{O}(\min\{(1+C)^{\frac{1}{3}}, K\})$ for both $\ell_2$ and pseudo KL calibration error. These bounds match the optimal rates in the stationary case ($C=0$ and $K=1$) and recover known guarantees in the fully adversarial regime ($C, K=Ω(T)$). Our approach builds on and extends prior work [Hu et al., 2026, Luo et al., 2025], introducing an epoch-based scheduling together with a novel non-uniform partition of the prediction space that allocates finer resolution near the underlying ground truth.

We study feature learning in two-layer neural networks within the linear-width regime, where the number of hidden neurons, sample size, and input dimension scale proportionally. While recent work has analyzed feature learning via a single step of gradient descent on the first layer weights in this regime, such one-step update schemes are fundamentally limited: the update to the weights is approximately rank-one, captures only a single direction, and requires the target function to have an information exponent of one. In this paper, we go beyond one-step updates to provide a full characterization of the features learned during the \textit{second step} of gradient descent with step-sizes $η_1\asymp N^{α_1}$ and $η_2 \asymp N^{α_2}$ for $α_1, α_2 \in [0,0.5)$, where $N$ is the number of hidden neurons. We derive a spectral characterization of the updated weights, demonstrating they behave as a spiked random matrix with multiple outliers, each corresponding to a learned direction. We show that the number of the outliers is determined by the parameters $α_1, α_2$ through $\lfloor \frac{α_2}{1/2 - α_1} \rfloor$. Furthermore, by analyzing the alignment between the learned directions and the target function, we identify a gap between training with independent versus reused batches. While independent batches restrict learning to directions with an information exponent of one, batch reuse enables the second update to capture directions even when the information exponent exceeds one, provided that $α_1, α_2$ are chosen properly. This shows that the benefits of batch reuse, previously observed in narrow-width regimes, persist in the linear-width limit as well. By characterizing these early-phase evolutions, our work proposes a tractable framework for studying optimization and feature learning phenomenology in modern overparameterized networks.

We study sample quantiles of distributions indexed by estimated parameters, with a on Value-at-Risk related to linear projections of financial returns that whose underlying probability law is heavy-tailed. In this setting, the projection direction and the empirical quantile threshold are estimated from the data, so the standard Bahadur representation under a fixed distribution does not separate the distinct sources of instability. A canonical starting point is Bahadur's representation, which expresses the sample quantile through the empirical distribution function plus a remainder term \cite{bahadur1966}. Empirical-process theory provides a usable scaffolding through the mechanics of half-spaces, symmetric differences, and Glivenko--Cantelli uniform convergence. They yield stability bounds, but absorb changes in projection direction and changes in quantile threshold into a single symmetric-difference measure. Interestingly, a global uniform-convergence requirement is imposed on what is intrinsically a local quantile-stability problem. This paper introduces a Q-Q orthogonality formulation for separating projection-direction and quantile-threshold effects. The object of interest is the difference between the empirical quantile computed using the estimated projection direction and the population quantile computed at the reference projection direction. We decompose this difference into three terms, $\hat q_α(\hat w)-q_α(w_0)=D_1+D_2+D_3$. Here, $D_1$ measures the population quantile movement induced by perturbing the projection direction, $D_2$ measures the empirical quantile fluctuation with the projection direction held fixed, and $D_3$ is the Bahadur-type remainder.

In the era of precision medicine, genome-wide epigenetic modifications offer rich data that could inform risk prediction. However, these data are high-dimensional and exhibit complex dependence structures, which makes it difficult to jointly model them with low-dimensional covariates when the goal is to obtain interpretable effect estimates for covariate adjustment. Standard Bayesian additive regression trees (BART) provide strong predictive performance but treat all predictors uniformly within the tree ensemble, obscuring the contributions of significant covariates and complicating variable selection in high-dimensional settings. We propose a semi-parametric BART model (spBART) that addresses this limitation by modeling low-dimensional covariates through a parametric component with interpretable coefficients, while capturing complex nonlinear associations among high-dimensional predictors through the tree ensemble. To perform stable variable selection, we develop a cross-validation-based procedure that aggregates posterior inclusion probabilities across folds and applies Bayesian false discovery rate control. We apply the proposed method to a pooled case--control analysis of high-dimensional genome-wide 5-hydroxymethylcytosine profiles derived from circulating cell-free DNA in two multiple myeloma studies ($N = 869$). The approach identifies a parsimonious set of candidate loci and achieves strong out-of-sample discrimination (AUC $= 0.96$) in a held-out validation set. Overall, spBART provides a unified framework for combining interpretable covariate inference with flexible modeling and variable selection in high-dimensional biomedical studies.

Discrete probability laws underpin statistical modeling, yet the catalog of interpretable distributions has expanded only gradually through centuries of case-by-case mathematical derivations. We introduce symbolic density estimation (SDE), an unsupervised framework that automatically recovers closed-form probability mass functions by composing elementary analytic operations within a structured search space. Our method integrates domain-specific structural priors with evolutionary search and a validity-aware inference stage, and it extends to richer distribution families such as zero inflation and finite mixtures. To support systematic evaluation and future research, we contribute a benchmark dataset spanning a broad collection of commonly used discrete distributions. The proposed algorithm recovers all benchmark families with accurate parameter estimates. A real data application shows that it identifies concise and interpretable mixture models that improve goodness-of-fit over standard models.