Interference arises when the treatment assigned to one individual affects the outcomes of other individuals. Commonly, individuals are naturally grouped into clusters, and interference occurs only among individuals within the same cluster, a setting referred to as partial interference. We study network causal effects on outcome quantiles in the presence of partial interference. We develop a general nonparametric efficiency theory for estimating these network quantile causal effects, which leads to a nonparametrically efficient estimator. The proposed estimator is consistent and asymptotically normal with parametric convergence rates, while allowing for flexible, data-adaptive estimation of complex nuisance functions. We leverage a three-way cross-fitting procedure that avoids direct estimation of the conditional outcome distribution. Simulations demonstrate adequate finite-sample performance of the proposed estimators, and we apply the methods to a clustered observational study.

Predicting counterfactual outcomes in longitudinal data, where sequential treatment decisions heavily depend on evolving patient states, is critical yet notoriously challenging due to complex time-dependent confounding and inadequate uncertainty quantification in existing methods. We introduce the Causal Diffusion Model (CDM), the first denoising diffusion probabilistic approach explicitly designed to generate full probabilistic distributions of counterfactual outcomes under sequential interventions. CDM employs a novel residual denoising architecture with relational self-attention, capturing intricate temporal dependencies and multimodal outcome trajectories without requiring explicit adjustments (e.g., inverse-probability weighting or adversarial balancing) for confounding. In rigorous evaluation on a pharmacokinetic-pharmacodynamic tumor-growth simulator widely adopted in prior work, CDM consistently outperforms state-of-the-art longitudinal causal inference methods, achieving a 15-30% relative improvement in distributional accuracy (1-Wasserstein distance) while maintaining competitive or superior point-estimate accuracy (RMSE) under high-confounding regimes. By unifying uncertainty quantification and robust counterfactual prediction in complex, sequentially confounded settings, without tailored deconfounding, CDM offers a flexible, high-impact tool for decision support in medicine, policy evaluation, and other longitudinal domains.

This paper studies Graphical SLOPE for precision matrix estimation, with emphasis on its ability to recover both sparsity and clusters of edges with equal or similar strength. In a fixed-dimensional regime, we establish that the root-$n$ scaled estimation error converges to the unique minimizer of a strictly convex optimization problem defined through the directional derivative of the SLOPE penalty. We also establish convergence of the induced SLOPE pattern, thereby obtaining an asymptotic characterization of the clustering structure selected by the estimator. A comparison with GLASSO shows that the grouping property of SLOPE can substantially improve estimation accuracy when the precision matrix exhibits structured edge patterns. To assess the effect of departures from Gaussianity, we then analyze Gaussian-loss precision matrix estimation under elliptical distributions. In this setting, we derive the limiting distribution and quantify the inflation in variability induced by heavy tails relative to the Gaussian benchmark. We also study TSLOPE, based on the multivariate $t$-loss, and derive its limiting distribution. The results show that TSLOPE offers clear advantages over GSLOPE under heavy-tailed data-generating mechanisms. Simulation evidence suggests that these qualitative conclusions persist in high-dimensional settings, and an empirical application shows that SLOPE-based estimators, especially TSLOPE, can uncover economically meaningful clustered dependence structures.

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. The estimated commercial cost is also comparable to existing industry rinses. Breakthroughs, discoveries, and DIY tips sent six days a week. While rinsing really does help clean fruits and vegetables of harmful pesticides and bacteria, washing produce with water alone doesn't ensure a longer shelf life or guard against decay. With millions of pounds of fresh food wasted annually in the United States alone, agricultural researchers at the University of British Columbia (UBC) in Canada are investigating new ways to extend freshness and rid produce of unwanted pesticides.

Maybe the tiny hunter should've been named after Metallica? 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. We can call this newly discovered spider another brick--or web--in the wall. Scientists in Colombia named the new species in honor of English rock band Pink Floyd and the arachnid's preferred habitat--walls.

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. Hurricane Edouard as seen from the International Space Station in 2014. Breakthroughs, discoveries, and DIY tips sent six days a week. More signs indicate the 2026 Atlantic hurricane season could ultimately be a welcome reprieve from more recent devastating storms . As La Niña transitions into a stronger El Niño climate pattern later this summer, the United States may experience a below-average number of hurricanes.

It involves 4chan, of all places. In July 2020, 4chan's video-game discussion board looked much like the rest of the notorious online forum. There were elaborate, libidinal fantasies involving "whores" and "dragon cum," and comments on how long a gamer had to wait "before my dick can get up for another beating," as one put it. And yet, as the gamers discussed such things, they were also making a discovery of significance to the AI industry. Some of them were playing, a new text-based role-playing game that was essentially an AI version of .

'Workslop' is an unintended consequence of the AI boom. 'Workslop' is an unintended consequence of the AI boom. Bosses say AI boosts productivity - workers say they're drowning in'workslop' Ken, a copywriter for a large, Miami-based cybersecurity firm, used to enjoy his job. But then the "workslop" started piling up. Workslop is an unintended consequence of the AI boom.

Quantum computers could cause a global security crisis that makes the once-feared millennium bug, or Y2K, look quaint. This infamous computer risk was averted through the persistent behind-the-scenes work of engineers across the world, but whether the new threat will be tackled similarly is an urgent yet unresolved question. Most digital communications and transactions are protected by cryptography based on mathematical problems that are unsolvable by conventional computers but are solvable by a sufficiently capable quantum computer. Researchers have understood this since the late 1990s, but the day when this capable-enough quantum computer comes online - or Q-Day - was thought to be very far in the future. Working quantum computers are now a reality, and recent leaps in how to use them are bringing Q-Day ever closer.

From recipe changes to aging taste buds, here's why those peanut butter cups don't hit like they used to. 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. There's a reason you might remember Hershey's chocolate differently. Breakthroughs, discoveries, and DIY tips sent six days a week. Brad Reese, grandson of Reese's Peanut Butter Cups inventor H.B. Reese, caused a stir this year with his claims that The Hershey Company had changed his grandfather's recipes beyond recognition .