New York City Mayor Zohran Mamdani's slate of progressives has swept establishment-backed Democrats in New York's closely watched congressional primaries, ousting two sitting congressmen in a show of force for the democratic socialist leader of the United States's largest city. On Tuesday, Adriano Espaillat, who leads the Congressional Hispanic Caucus and is in his fifth term, was defeated by Mamdani's most polarising pick, Darializa Avila Chevalier, a democratic socialist who once helped organise pro-Palestinian protests at Columbia University. And another Mamdani ally, democratic socialist state Assembly Member Claire Valdez, defeated Brooklyn Borough President Antonio Reynoso, the handpicked successor of retiring US Representative Nydia Velazquez. New York's primary will determine which challengers the party nominates to run in the midterm elections in November. That vote will, in turn, decide which party controls Congress, giving its lawmakers the power to aid or impede US President Donald Trump's legislative agenda for his final two years in office.

Samples of rare earth luminescent materials displayed at an exhibition on China's manufacturing achievements at the National Museum in Beijing in March | REUTERS China's tightening export controls on dual-use materials and strategically important rare earths are beginning to disrupt Japanese industry -- including the defense sector. Chinese customs data tell the sharpest part of the story. Exports of dysprosium oxide to Japan ceased after October 2025, and shipments of terbium oxide ended a month later. No shipments of either material have been recorded since. The halt matters because dysprosium and terbium -- both heavy rare earth elements -- are among the most critical inputs for high-performance permanent magnets used in advanced military systems, electric vehicle motors, aerospace applications and industrial robotics.

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.

AI boom sees investors shift from Japan's value to growth stocks Some see Japanese equities as an attractive way to diversify away from U.S. stocks while still benefiting from the global artificial intelligence rally. Japanese equities, long regarded by global investors as a value market, are beginning to attract growth funds, as artificial intelligence-linked firms power to the top of market-cap rankings, beating out the manufacturers and telecoms giants that dominated for decades. "We have been raising our exposure to Japan based on the growth prospects of Japanese companies" under a strategy of investing in innovative firms globally, said Kei Takizawa, senior investment strategist at AllianceBernstein Japan. The nation's firms are playing an increasingly critical role in building AI infrastructure, he added. Investors had historically classified Japan's equities as low-growth value stocks due to the country's sluggish economic growth and declining population.

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.

Causal discovery seeks to uncover the causal dependencies among variables. For this purpose, we propose an algorithm called Tensor-based Second-order Causal Discovery (TSCD). Its input is a tensor obtained from the covariance matrices of observational and interventional data. Assuming the causal dependencies follow a linear structural equation model on a directed acyclic graph (DAG), TSCD outputs the DAG and the functions on its edges, requiring only that the noise variables are uncorrelated. We also implement a version of the approach for nonlinear models. Our focus on second-order statistics (via the covariance matrices) is motivated by their statistical and computational efficiency relative to higher-order moments, their identifiability relative to first-order statistics, and that they work regardless of whether the variables are Gaussian. We show that TSCD has identifiable causal order and parameters from a number of interventions that is logarithmic in the number of variables. Experiments show that TSCD is robust to noise, competitive with existing methods, and scales to hundreds of variables.