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@Mediagazer@mstdn.social
2026-08-13 11:25:45

Disney secures rights to podcast content of Gary Neville's soccer media group The Overlap in a multimillion-pound deal; shows will still broadcast on YouTube (Matt Hughes/The Guardian)
theguardian.com/football/2026/

@aral@mastodon.ar.al
2026-09-12 10:59:19

So there is a protest in Dublin today “at 1pm at the Garden of Remembrance, Dublin 1”¹
If you’re thinking of heading up on the Dart or the M50, however, think again.
Dart services are down and there are M50 closures.
Protect someone the way the Irish government protects a genocide-complicit, rapist, war-criminal paedophile.
¹

@NFL@darktundra.xyz
2026-10-06 11:11:38

Revisiting the Sauce Gardner trade: Jets sitting pretty as blockbuster blowing up in Colts face

cbssports.com/nfl/news/revisit

@michabbb@social.vivaldi.net
2026-09-11 07:38:00

📦 Ships 68 specialized agents, 291 skills and 94 commands covering planning, build repair, code review, security, architecture, docs, frontend, data and ML work
🧪 #TDD becomes a gated RED − GREEN − REFACTOR flow with evidence, and a fresh-context reviewer checks the changes instead of the same context grading its own code

@arXiv_statCO_bot@mastoxiv.page
2026-08-14 07:49:17

Hierarchical Bayesian Calibration with Bayesian Committee Machine
Sebastian Heinekamp, David M. Higdon, Andreas Adelmann
arxiv.org/abs/2608.12603 arxiv.org/pdf/2608.12603 arxiv.org/html/2608.12603
arXiv:2608.12603v1 Announce Type: new
Abstract: Calibrating computational models to experimental data is a core task in applied statistics, especially in scientific domains, where physical experiments are costly and simulations play a central role in design and inference. Motivated by uncertainty quantification challenges in particle accelerator experiments, we develop and evaluate a Hierarchical Bayesian Calibration framework. In contrast to standard Bayesian calibration, certain inputs - such as beam injection amplitude - must be estimated separately for each experiment. We adopt the Kennedy-O'Hagan formulation and extend it with a hierarchical prior structure to model the distribution of experiment-specific calibration parameters, thus borrowing strength and improving generalisation across repeated experiments. A key methodological challenge arises from the need to evaluate a large number of forward simulations, which renders conventional Markov chain Monte Carlo approaches computationally prohibitive. To address this, we leverage the Bayesian Committee Machine as a scalable modelling strategy for Gaussian Process emulators. The BCM provides a principled divide-and-conquer approach, enabling parallel inference and reducing computational cost without requiring problem-specific tuning of the emulator approximation. Posterior sampling is performed using the No-U-Turn Sampler, supported by automatic differentiation in Julia, which removes the need for analytic gradient derivation and facilitates flexible model specification. We assess the proposed framework using established benchmark problems and simulated data from the Argonne Wakefield Accelerator. The results demonstrate substantial computational savings and robust calibration performance, highlighting the applicability of the method to large-scale scientific modelling problems.
toXiv_bot_toot

@memeorandum@universeodon.com
2026-08-13 04:25:42

Inside feds' stealth mission to deport California killers, rapists and child molesters (Ben Chapman/New York Post)
nypost.com/2026/08/12/us-news/
memeorandum.com/260813/p2#a260

@markhburton@mstdn.social
2026-09-18 19:10:16

The right decision.
Polanski request to postpone Greens’ conference to focus on byelection refused by party | Green party | The Guardian
theguardian.com/politics/2026/

@raiders@darktundra.xyz
2026-07-26 20:01:20

Antonio Brown has a very different memory of his Raiders disaster raiderramble.com/2026/07/26/an