A Saudi employee of OpenAI, Abdulrahman Alfozan, faces up to five years in prison over allegations he planted a hidden recording device to capture sensitive company conversations. Here is what we know. Do any of these Israeli candidates support a Palestinian state? Trump plans for'massive' resource exploration in Greenland

Greenland's delegation gets hero's welcome after signing US-Denmark pact Greenland's delegation gets hero's welcome after signing US-Denmark pact Greenland's delegation returned home to a hero's welcome in Nuuk after signing a new pact with the US and Denmark that expands military access to the island, strengthening security and preserving its sovereignty. Share Greenland's delegation gets hero's welcome after signing US-Denmark pact on social media UN funding cuts hit refugees at Cox's Bazar settlement OpenAI CEO: Tech companies don't'have all the answers' on AI policy Sikorski: Russia doesn't have the forces to invade NATO Why have some nations developed while others struggle? Spain's Sanchez warns of far-right threat in UNGA speech Venezuela's Rodriguez pledges elections, path forward in UNGA speech

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President Donald Trump says a new Greenland agreement with Denmark gives the U.S. "permanent control" over the island's security and expands its Arctic military presence.

The European Commission's president recognized "new trade routes, new security risks, and new environmental concerns" for Greenland and vowed to "navigate these changes together."

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Unlike chameleons, these insects don't choose to change color. 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. A pure green sweat bee covers itself in pollen, while pollinating the flower of a squash plant in Canada. Breakthroughs, discoveries, and DIY tips sent six days a week. For humans, humidity often makes us cranky, sweaty, and downright uncomfortable .

Selection bias arises when the probability that an observation enters a dataset depends on variables related to the quantities of interest, leading to systematic distortions in estimation and uncertainty quantification. For example, in epidemiological or survey settings, individuals with certain outcomes may be more likely to be included, resulting in biased prevalence estimates with potentially substantial downstream impact. Classical corrections, such as inverse-probability weighting or explicit likelihood-based models of the selection process, rely on tractable likelihoods, which limits their applicability in complex stochastic models with latent dynamics or high-dimensional structure. Simulation-based inference enables Bayesian analysis without tractable likelihoods but typically assumes missingness at random and thus fails when selection depends on unobserved outcomes or covariates. Here, we develop a bias-aware simulation-based inference framework that explicitly incorporates selection into neural posterior estimation. By embedding the selection mechanism directly into the generative simulator, the approach enables amortized Bayesian inference without requiring tractable likelihoods. This recasting of selection bias as part of the simulation process allows us to both obtain debiased estimates and explicitly test for the presence of bias. The framework integrates diagnostics to detect discrepancies between simulated and observed data and to assess posterior calibration. The method recovers well-calibrated posterior distributions across three statistical applications with diverse selection mechanisms, including settings in which likelihood-based approaches yield biased estimates. These results recast the correction of selection bias as a simulation problem and establish simulation-based inference as a practical and testable strategy for parameter estimation under selection bias.

The poached ingredients worth $1.3 million were seized in a nationwide hunt. 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. Shark fins remain a prized delicacy despite conservation efforts and education. Breakthroughs, discoveries, and DIY tips sent six days a week. The United States Fish and Wildlife Service (FWS) recently exposed a major international smuggling operation orchestrated across at least three cities around the country.

We study high-dimensional mediation analysis in which exposures, mediators, and outcomes are all multivariate, and both exposures and mediators may be high-dimensional. We formalize this as a many (exposures)-to-many (mediators)-to-many (outcomes) (MMM) mediation analysis problem. Methodologically, MMM mediation analysis simultaneously performs variable selection for high-dimensional exposures and mediators, estimates the indirect effect matrix (i.e., the coefficient matrices linking exposure-to-mediator and mediator-to-outcome pathways), and enables prediction of multivariate outcomes. Theoretically, we show that the estimated indirect effect matrices are consistent and element-wise asymptotically normal, and we derive error bounds for the estimators. To evaluate the efficacy of the MMM mediation framework, we first investigate its finite-sample performance, including convergence properties, the behavior of the asymptotic approximations, and robustness to noise, via simulation studies. We then apply MMM mediation analysis to data from the Alzheimer's Disease Neuroimaging Initiative to study how cortical thickness of 202 brain regions may mediate the effects of 688 genome-wide significant single nucleotide polymorphisms (SNPs) (selected from approximately 1.5 million SNPs) on eleven cognitive-behavioral and diagnostic outcomes. The MMM mediation framework identifies biologically interpretable, many-to-many-to-many genetic-neural-cognitive pathways and improves downstream out-of-sample classification and prediction performance. Taken together, our results demonstrate the potential of MMM mediation analysis and highlight the value of statistical methodology for investigating complex, high-dimensional multi-layer pathways in science. The MMM package is available at https://github.com/THELabTop/MMM-Mediation.