Our study of 22 mitigation techniques and five baselines reveals up to 12.6% fairness variance across identical training runs with identical seeds.

Queen Camilla told her friend that Meghan Markle'brainwashed' Prince Harry, new book claims Ground stop issued for all three Washington DC-area airports after'strong chemical smell' detected Uncomfortable truth about what happened to Rob Reiner's forgotten daughter Tracy: As she breaks cover for first time since murders... new details of secret New Mexico life Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Ohio mom's agony as National Guard member, 28, named as one of six Americans killed in Iraq crash Dak Prescott's crippling secret fear: Quarterback'preparing for the worst' after fiancée split... as career-ending gossip now seems inevitable Mysterious'Trump' airships appearing in 100-year-old sketchbooks sparks'time traveler' theories Downfall of Trump VP hopeful exiled to construction job: Filthy messages, Oval Office humiliations and the Ice Maiden who'f***ing hates his guts' What convinced Timothy Busfield's wife Melissa Gilbert that he didn't grope children: 'She would dump his a**' Woke Seattle writer claims she's no longer devastated by husband's demand for open marriage after she had threesome with him and his girlfriend Inside the sex guide electrifying conservative women: Good Christian wives purring over'explicit illustrations' that teach them the ultimate taboos Trump hails dramatic bombing raid on'Iran's crown jewel'... but says one area deliberately SPARED: Live updates AMANDA PLATELL: Meghan had the world at her feet. Now I feel reality is finally dawning on her and Harry. Princess Anne's secret phone call to Andrew, how she reacted to his arrest... and surprising offer she made to him: Insiders tell RICHARD KAY her hidden role as the Epstein crisis engulfed the royals - and what she thinks of Kate'I was in love with him': Woman who had years-long romance with Timothee Chalamet says he blindsided her with Kylie Jenner relationship Pieces of raw meat suddenly began falling from the sky over rural Kentucky, baffling witnesses who watched the bizarre shower unfold beneath a clear blue sky. The strange incident occurred on farmland owned by Allen and Rebecca Crouch on March 3, 1876. Witnesses said chunks of meat continued to fall from the sky for several minutes, scattering across an area roughly 100 yards long and 50 yards wide.

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Flexport CEO Ryan Petersen says the conflict is stranding cargo and threatening inflation. After years of chaos in the global supply chain, Ryan Petersen, CEO of the logistics company Flexport, felt 2026 might offer some modicum of order. The pandemic was firmly in the rearview mirror. Red Sea shipping channels--which had been closed due to the Gaza crisis--were finally opening. The Supreme Court struck down many of Donald Trump's tariffs, and some Flexport customers were hoping for refunds.

'Comatose' Mojtaba Khamenei'is UNAWARE there is a war on and has no idea he is supreme leader', report says - despite regime issuing his'first statement' FBI storms home of Lebanese-born restaurant worker who drove truck filled with explosives into synagogue and opened fire after his'family were killed in airstrike' Trump slammed after lifting oil sanctions on Russia as gas prices skyrocket: 'It's a betrayal' Alexander brothers' alleged HIGH SCHOOL rape video: Classmates speak out on sickening footage... as creepy unseen photos are exposed Kylie Jenner's total humiliation in Hollywood: Derogatory rumor leaves her boyfriend's peers'laughing at her' behind her back Billy Joel's daughter Alexa Ray gives health update amid his battle with rare brain disorder Concerning whispers inside Trump World that Operation Epic Fury is suddenly at risk... and the critical question that will determine how this ends: MARK HALPERIN Meghan Markle masks up to cheer young patients at Los Angeles children's hospital as she agrees deal to sign her latest documentary Beauty queen slams Trump as she's FIRED by White House: 'I stood by you for 20 years... now, I don't even recognize you' Wall Street issues stark warning that Iran oil attacks could wreck Trump's key election promises Truth behind the massacre of 110 school girls in Iran: How shameful episode sparked a deluge of conspiracy theories and lies... as JAKE WALLIS SIMONS explores what really happened Long hair over 45 is ageing and try-hard. I've finally cut mine off. NFL fans left divided as team replace historic logo with'boring' new design as part of franchise rebrand I worked with Carolyn Bessette. This is the'messy' truth about what she was REALLY like in secret. After she met JFK Jr she tried to hide it... but we all knew the nighttime gossip Trump says US is'totally destroying' Iran as he issues chilling threat of more action coming TODAY The 7 types of'hyperarousal' - so, do you get cold sweats or tingling fingers?

The malleable mind: context accumulation drives LLM's belief drift After being trained on a dataset of 80,000 words of conservative political philosophy, Grok-4 changed the stance of its outputs on political questions more than a quarter of the time. This was without any adversarial prompts - the change in training data was enough. As memory mechanisms and research agents [1, 2] enable LLMs to accumulate context across long horizons, earlier prompts increasingly shape later responses. In human decision-making, such repeated exposure influences beliefs without deliberate persuasion [3]. When an LLM operates over accumulated context, does this past exposure cause the stance of the LLM's responses to drift over time?

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In this work, we analyze alternative effective sample size (ESS) metrics for importance sampling algorithms, and discuss a possible extended range of applications. We show the relationship between the ESS expressions used in the literature and two entropy families, the Rényi and Tsallis entropy. The Rényi entropy is connected to the Huggins-Roy's ESS family introduced in \cite{Huggins15}. We prove that that all the ESS functions included in the Huggins-Roy's family fulfill all the desirable theoretical conditions. We analyzed and remark the connections with several other fields, such as the Hill numbers introduced in ecology, the Gini inequality coefficient employed in economics, and the Gini impurity index used mainly in machine learning, to name a few. Finally, by numerical simulations, we study the performance of different ESS expressions contained in the previous ESS families in terms of approximation of the theoretical ESS definition, and show the application of ESS formulas in a variable selection problem.

Mobile data technologies use ``actigraphs'' to furnish information on health variables as a function of a subject's movement. The advent of wearable devices and related technologies has propelled the creation of health databases consisting of human movement data to conduct research on mobility patterns and health outcomes. Statistical methods for analyzing high-resolution actigraph data depend on the specific inferential context, but the advent of Artificial Intelligence (AI) frameworks require that the methods be congruent to transfer learning and amortization. This article devises amortized Bayesian inference for actigraph time sheets. We pursue a Bayesian approach to ensure full propagation of uncertainty and its quantification using a hierarchical dynamic linear model. We build our analysis around actigraph data from the Physical Activity through Sustainable Transport Approaches in Los Angeles (PASTA-LA) study conducted by the Fielding School of Public Health in the University of California, Los Angeles. Apart from achieving probabilistic imputation of actigraph time sheets, we are also able to statistically learn about the time-varying impact of explanatory variables on the magnitude of acceleration (MAG) for a cohort of subjects.

Neural networks are the cornerstone of modern machine learning, yet can be difficult to interpret, give overconfident predictions and are vulnerable to adversarial attacks. Bayesian neural networks (BNNs) provide some alleviation of these limitations, but have problems of their own. The key step of specifying prior distributions in BNNs is no trivial task, yet is often skipped out of convenience. In this work, we propose a new class of prior distributions for BNNs, the Dirichlet scale mixture (DSM) prior, that addresses current limitations in Bayesian neural networks through structured, sparsity-inducing shrinkage. Theoretically, we derive general dependence structures and shrinkage results for DSM priors and show how they manifest under the geometry induced by neural networks. In experiments on simulated and real world data we find that the DSM priors encourages sparse networks through implicit feature selection, show robustness under adversarial attacks and deliver competitive predictive performance with substantially fewer effective parameters. In particular, their advantages appear most pronounced in correlated, moderately small data regimes, and are more amenable to weight pruning. Moreover, by adopting heavy-tailed shrinkage mechanisms, our approach aligns with recent findings that such priors can mitigate the cold posterior effect, offering a principled alternative to the commonly used Gaussian priors.