Seekers then come and try to find the hiders and, this being an online video game, shoot them. It's frantic, silly and fiendishly creative: finding a spot on one of the maps that you feel confident to paint yourself into - whether it's a laundry room or a farm outbuilding - is a challenging artistic and perceptual task, as well as a neat game mechanic. Meccha Chameleon perfectly encapsulates two popular and interconnected indie genres - prop games (hide and seek, but people disguise themselves as everyday objects) and the slightly pejoratively named "friendslop" (accessible, crudely designed multiplayer titles). So no wonder it has sold 7m units in less than a month.

Google's first new smart speaker in six years is here and once again leads its competitors--now with paywalled features. Sounds a little more human than competitors. Gemini is helpful and smart. Some assistant features are hidden behind paywalls. Works best if you buy or have bought several Google devices for your home.

Is the war entering a new phase? Ukrainian strikes on Russian-occupied Crimea have triggered power outages in its largest city, Sevastopol, according to statements from both sides, as Kyiv intensifies attacks on the peninsula Moscow annexed in 2014. Crimea has been forced to suspend fuel sales to the public as Ukraine's army targets Russian logistics to the region and has hit a series of oil refineries and depots across southern Russia that provide supplies. "The enemy is once again striking treacherously, attempting to deprive us of normal living conditions and sow panic," he posted. He said some areas of the city - where temperatures are approaching 30 degrees Celsius (86 degrees Fahrenheit) - would be without power until at least Wednesday evening.

The biodegradable material can help improve healing after surgery--or an avocado-related accident. Roughly 500,000 Americans suffer nerve injuries that require treatment each year, whether from an errant attempt to hack out an avocado pit or an unfortunate woodworking accident. Many will never get full feeling back in their fingers. But a startup has developed a thick and sticky liquid that could change that, and it's begun deploying it with surgeons in the US. French firm Tissium is working to replace and supplement medical stitches with a liquid that attaches to tissue when exposed to light.

British startup Wayve, in partnership with Uber, is racing to beat U.S. rival Waymo, owned by Google-parent Alphabet.

Whether you're dreaming of your very own Dyson Airwrap to style your hair, or you're wishing you had a Dyson vacuum of some kind to really get your carpet clean, the way Dyson uses air power in both its vacuum and beauty products impresses us again and again. But that power comes with a price, so these Prime Day sales are a prime time to shop for a Dyson gadget of your own. Whether you're looking for a stick vacuum like the Dyson Gen5Detect (on sale for $660) or a new robot vacuum like Dyson Spot+Scrub AI (on sale for $850) that can mop and vacuum your floors at the same, there's a Dyson on sale for you. Make sure to also check out our roundup of the Absolute Best Prime Day Deals and Best Prime Day Beauty Deals . This was Dyson's top of the line vacuum until this month, thanks to Dyson's newest cordless vacuums usurping its spot.

Automated diabetic retinopathy (DR) grading from colour fundus photographs can achieve strong predictive performance, but clinical interpretation requires more than an image-level label. It requires understanding how lesion evidence is distributed around retinal vessels and how this evidence relates to quantitative vascular biomarkers. We present a dual-edge spatial-Jacobian image graph for interpretable DR grading. Each fundus image is represented as a graph node with four aligned evidence streams: AutoMorph vessel information ($X_1$), DR-XAI-style lesion evidence maps ($X_2$), a 128-dimensional lesion-based contrastive image embedding ($X_3$), and AutoMorph morphometric biomarkers ($X_4$). The spatial edge branch ($X_{12}$) encodes vessel-lesion geometry, while the Jacobian branch ($X_{34}$) models embedding-biomarker sensitivity. Lightweight two-token attention fuses both edge families into a final image graph. On 2,910 matched non-augmented APTOS images, the full graph achieves 0.8076 accuracy, 0.8312 quadratic weighted kappa, 0.5915 macro-F1, and 0.9330 adjacent-grade accuracy; referable DR reaches 0.9055 accuracy and 0.9711 AUROC. The framework is positioned as an explainable representation-learning tool for lesion-biomarker hypothesis generation, rather than as a deployment-ready clinical classifier. The code is available at https://github.com/Inamullah-Colab/dual-edge-dr-graph-xai.

State-space models provide a powerful framework for describing the evolution of hidden states in dynamical systems [3], [4], [1]. Conventionally, state-space models assume Gaussian measurement and state noise, owing to their tractability and well-characterized statistical properties. However, many real-world phenomena are subject to perturbations that deviate from the conventional Gaussian noise assumption. In radio interferometry, for instance, observational data are frequently corrupted by non-Gaussian noise sources such as radio-frequency interference (RFI) [5], [2], which originates from man-made signals and introduces significant distortions into astronomical measurements [6], [30]. Such interference produces sporadic high-power spikes in the measured visibilities, leading to heavy-tailed statistics. Many radio-interferometric reconstruction methods assume Gaussian additive noise [7], [31], [33], [35], an approximation that can lead to inaccurate reconstructions when the heavy-tailed nature of real-world measurement noise is not properly accounted for. In the realm of state-space modeling, addressing non-Gaussian noise has led to the development of various methodological approaches, notably particle filtering and non-conventional Kalman filters. Particle filters [8], or Sequential Monte Carlo methods, are designed to handle non-linear and non-Gaussian state-space models by representing the posterior distribution with a set of weighted samples [9], [10], [32].

Survival analysis is central to clinical decision-making, yet reliable time-to-event models require large, diverse cohorts that are rarely available at a single institution, while privacy regulations restrict the centralization of patient data. Federated learning (FL) offers a privacy-preserving alternative by training shared models without exchanging raw data, but its effectiveness for survival modeling under realistic, heterogeneous conditions remains insufficiently understood. This paper presents a systematic, multi-model evaluation of federated survival analysis on a cross-institutional breast cancer cohort with naturally heterogeneous distributed clients. Three representative survival models, the Cox Proportional Hazards model, DeepSurv, and Random Survival Forest (RSF), are compared across centralized, local, and federated training, and three federated optimization strategies (FedAvg, FedProx, and FedAdam) are assessed for the gradient-based models. Results show that FL consistently outperforms local training and approaches, and occasionally exceeds, centralized performance, while RSF offers the best overall balance of discrimination, calibration, and robustness across heterogeneous clients. We further find that performance depends on the diversity of client distributions, and that FedAvg and FedProx are stronger and more stable than FedAdam. Based on these findings, we derive practical, decision-oriented guidelines mapping data, privacy, interpretability, and resource constraints to recommended model and training-paradigm choices for federated survival modeling in healthcare.

We study the problem of estimating the arrival times of vertices in a uniform random recursive tree from its unlabeled structure. We adopt a pointwise perspective and analyze the distribution of the relative estimation error, and derive tail bounds that are uniform in both the vertex and the tree size. For the ranking induced by Jordan centrality, the probability that the estimate exceeds the true arrival time by a factor $S$ decays on the order of $1/S$, while the probability of underestimating the arrival time by a factor $1/S$ decays exponentially in $S$. We introduce a refined centrality measure whose overestimation tail decays on the order of $(\log S)/S^{2}$, at the cost of a heavier lower tail of order $1/S^{2}$. These results reveal a tradeoff between upper- and lower-tail performance in arrival-time estimation that is invisible to the previously studied risk functional. Nevertheless, the refined centrality measure attains the optimal order of the risk for all its parameter values.