@Techmeme@techhub.socialAnthropic commits to buy $30B in Azure capacity in a new deal with Microsoft and Nvidia, which commit to invest up to $5B and $10B, respectively, in Anthropic (Microsoft)
https://blogs.microsoft.com/blog/2025/11/18/microsoft-nvidia-…
@simon_brooke@mastodon.scot"These deals represent the corporate capture of the UK state including, our cloud capacity, National Health Service, and now our military establishment...
Starmer’s inability to speak the truth is not diplomacy. It’s evidence." @…
@andres4ny@social.ridetrans.itHm, $450 for a bike whose manufacturer is about to enter bankruptcy (in no small part due to recalls), with broken rear wheel & motor, and almost 7,000 miles on it? Good luck with that.
https://www.ebay.com/itm/267487858971
@hex@kolektiva.socialThe fracturing of the Dutch far-right, after Wilder's reminded everyone that bigots are bad at compromise, is definitely a relief. Dutch folks I've talked to definitely see D66 as progressive, <strike>so there's no question this is a hard turn to the left (even if it's not a total flip to the far-left)</strike> a lot of folks don't agree. I'm going to let the comments speak rather than editorialize myself..
While this is a useful example of how a democracy can be far more resilient to fascism than the US, that is, perhaps, not the most interesting thing about Dutch politics. The most interesting thing is something Dutch folks take for granted and never think of as such: there are two "governments."
The election was for the Tweede Kamer. This is a house of representatives. The Dutch use proportional representation, so people can (more or less) vote for the parties they actually want. Parties <strike>rarely</strike> never actually get a ruling majority, so they have to form coalition governments. This forces compromise, which is something Wilders was extremely bad at. He was actually responsible for collapsing the coalition his party put together, which triggered this election... and a massive loss of seats for his party.
Dutch folks do still vote strategically, since a larger party has an easier time building the governing coalition and the PM tends to come from the largest party. This will likely be D66, which is really good for the EU. D66 has a pretty radical plan to solve the housing crisis, and it will be really interesting to see if they can pull it off. But that's not the government I want to talk about right now.
In the Netherlands, failure to control water can destroy entire towns. A good chunk of the country is below sea level. Both floods and land reclamation have been critical parts of Dutch history. So in the 1200's or so, the Dutch realized that some things are too important to mix with normal politics.
You see, if there's an incompetent government that isn't able to actually *do* anything (see Dick Schoof and the PVV/VVD/NSC/BBB coalition) you don't want your dikes to collapse and poulders to flood. So the Dutch created a parallel "government" that exists only to manage water: waterschap or heemraadschap (roughly "Water Board" in English). These are regional bureaucracies that exist only to manage water. They exist completely outside the thing we usually talk about as a "government" but they have some of the same properties as a government. They can, for example, levy taxes. The central government contributes funds to them, but lacks authority over them. Water boards are democratically elected and can operate more-or-less independent of the central government.
Controlling water is a common problem, so water boards were created to fulfill the role of commons management. Meanwhile, so many other things in politics run into the very same "Tragedy of the Commons" problems. The right wing solution to commons management is to let corporations ruin everything. The left-state solution is to move everything into the government so it can be undermined and destroyed by the right. The Dutch solution to this specific problem has been to move commons management out of the domain of the central government into something else.
And when I say "government" here, I'm speaking more to the liberal definition of the term than to an anarchist definition. A democratically controlled authority that facilitates resource management lacks the capacity for coercive violence that anarchists define as "government." (Though I assume they might leverage police or something if folks refuse to pay their taxes, but I can't imagine anyone choosing not to.)
As the US federal government destroys the social fabric of the US, as Trump guts programs critical to people's survival, it might be worth thinking about this model. These authorities weren't created by any central authority, they evolved from the people. Nothing stops Americans from building similar institutions that are both democratic and outside of the authority of a government that could choose to defund and abolish them... nothing but the realization that yes, you actually can.
#USPol #NLPol
@arXiv_csLG_bot@mastoxiv.pageSpatially-informed transformers: Injecting geostatistical covariance biases into self-attention for spatio-temporal forecasting
Yuri Calleo
https://arxiv.org/abs/2512.17696 https://arxiv.org/pdf/2512.17696 https://arxiv.org/html/2512.17696
arXiv:2512.17696v1 Announce Type: new
Abstract: The modeling of high-dimensional spatio-temporal processes presents a fundamental dichotomy between the probabilistic rigor of classical geostatistics and the flexible, high-capacity representations of deep learning. While Gaussian processes offer theoretical consistency and exact uncertainty quantification, their prohibitive computational scaling renders them impractical for massive sensor networks. Conversely, modern transformer architectures excel at sequence modeling but inherently lack a geometric inductive bias, treating spatial sensors as permutation-invariant tokens without a native understanding of distance. In this work, we propose a spatially-informed transformer, a hybrid architecture that injects a geostatistical inductive bias directly into the self-attention mechanism via a learnable covariance kernel. By formally decomposing the attention structure into a stationary physical prior and a non-stationary data-driven residual, we impose a soft topological constraint that favors spatially proximal interactions while retaining the capacity to model complex dynamics. We demonstrate the phenomenon of ``Deep Variography'', where the network successfully recovers the true spatial decay parameters of the underlying process end-to-end via backpropagation. Extensive experiments on synthetic Gaussian random fields and real-world traffic benchmarks confirm that our method outperforms state-of-the-art graph neural networks. Furthermore, rigorous statistical validation confirms that the proposed method delivers not only superior predictive accuracy but also well-calibrated probabilistic forecasts, effectively bridging the gap between physics-aware modeling and data-driven learning.
toXiv_bot_toot
