Tootfinder

Donald Trump has finally unveiled his long-awaited framework for healthcare affordability,
almost a year and a half after announcing during a pre-election presidential debate that he had the “concepts of a plan” for healthcare reform.
The short document, titled
"the Great Healthcare Plan",
provides four headline objectives,
but few specific details as to how they will be achieved.
The Trump administration says it intends to lower prescription pri…

Part of why #Trump has always been so hard to pin down politically is that he was always representing highly conflicting interests. Now, as that eats him alive, the GOP is fracturing in to two main groups: the Pinochet/Franco wing and the Hitler wing.
The Pinochet/Franco wing (let's call them PF) are lead by Vance. PF are also a coalition with some competing interests, but basically it's evangelical leaders, Opus Dei (fascist catholics), tech fascists (Yarvinites), pharma, and the other normal big republican donors. They support Israel, some because apartheid is extremely profitable and some because they support the genocide of Palestinian in order to bring the end of the world. They are split between extremely antisemitic evangelicals and Zionists, wanting similar things for completely different reasons. PF wants strong immigration enforcement because it lets them exploit immigrants, they don't want actual ethnic cleansing (just the constant threat). They want H1B visas because they want to a precarious tech work force. They want to end tariffs because they support free trade and don't actually care about things being made here.
The Hitler wing are lead by Nick Fuentes. I think they're a more unified group, but they're going to try to pull together a coalition that I don't think can really work. They're against Israel because they believe in some bat shit antisemitic conspiracy theory (which they are trying to inject along side legitimate criticism of Israel). They are focused on release of the #EpsteinFiles because they believe that it shows that Epstein worked for Mossad. They don't think that the ICE raids are going far enough, they oppose H1Bs because they are racists. They want a full ethnic cleansing of the US where everyone who isn't "white" is either enslaved for menial labor, deported, or dead. But they're also critical of big business (partially because of conspiracy theories but also) because they think their best option is to push for a white socialism (red/brown alliance).
Both of them want to sink Trump because they see him as standing in the way of their objectives. Both see #Epstein as an opportunity. Both of them have absolutely terrifying visions of authoritarian dictatorships, but they're different dictatorships.with opposing interests. Even within these there may be opportunities to fracture these more.
While these fractures decrease the likelihood of either group getting enough people together, their vision is more clear and thus more likely to succeed if they can make that happen. Now is absolutely *not* the time to just enjoy the collapse, we need to keep up or accelerate anti-fascist efforts to avoid repeating some of the mistakes of history.
Edit:
I should not that this isn't *totally* original analysis. I'll link a video later when I have time to find it.
Here it is:
#USPol

TIL about the Perverse Incentive (aka Cobra Effect), which describes the effect when "incentives are often designed to achieve short-term goals, but in the long run, they lead to bigger problems or undermine the original objectives":
https://en.wikipedia.org/wiki/Perverse_incentiv…

Perverse incentive - Wikipedia

Beyond Revenue and Welfare: Counterfactual Analysis of Spectrum Auctions with Application to Canada's 3800MHz Allocation
Sara Jalili Shani, Kris Joseph, Michael B. McNally, James R. Wright
https://arxiv.org/abs/2512.08106 https://arxiv.org/pdf/2512.08106 https://arxiv.org/html/2512.08106
arXiv:2512.08106v1 Announce Type: new
Abstract: Spectrum auctions are the primary mechanism through which governments allocate scarce radio frequencies, with outcomes that shape competition, coverage, and innovation in telecommunications markets. While traditional models of spectrum auctions often rely on strong equilibrium assumptions, we take a more parsimonious approach by modeling bidders as myopic and straightforward: in each round, firms simply demand the bundle that maximizes their utility given current prices. Despite its simplicity, this model proves effective in predicting the outcomes of Canada's 2023 auction of 3800 MHz spectrum licenses. Using detailed round-by-round bidding data, we estimate bidders' valuations through a linear programming framework and validate that our model reproduces key features of the observed allocation and price evolution. We then use these estimated valuations to simulate a counterfactual auction under an alternative mechanism that incentivizes deployment in rural and remote regions, aligning with one of the key objectives set out in the Canadian Telecommunications Act. The results show that the proposed mechanism substantially improves population coverage in underserved areas. These findings demonstrate that a behavioral model with minimal assumptions is sufficient to generate reliable counterfactual predictions, making it a practical tool for policymakers to evaluate how alternative auction designs may influence future outcomes. In particular, our study demonstrates a method for counterfactual mechanism design, providing a framework to evaluate how alternative auction rules could advance policy goals such as equitable deployment across Canada.
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You Only Train Once: Differentiable Subset Selection for Omics Data
Daphn\'e Chopard, Jorge da Silva Gon\c{c}alves, Irene Cannistraci, Thomas M. Sutter, Julia E. Vogt
https://arxiv.org/abs/2512.17678 https://arxiv.org/pdf/2512.17678 https://arxiv.org/html/2512.17678
arXiv:2512.17678v1 Announce Type: new
Abstract: Selecting compact and informative gene subsets from single-cell transcriptomic data is essential for biomarker discovery, improving interpretability, and cost-effective profiling. However, most existing feature selection approaches either operate as multi-stage pipelines or rely on post hoc feature attribution, making selection and prediction weakly coupled. In this work, we present YOTO (you only train once), an end-to-end framework that jointly identifies discrete gene subsets and performs prediction within a single differentiable architecture. In our model, the prediction task directly guides which genes are selected, while the learned subsets, in turn, shape the predictive representation. This closed feedback loop enables the model to iteratively refine both what it selects and how it predicts during training. Unlike existing approaches, YOTO enforces sparsity so that only the selected genes contribute to inference, eliminating the need to train additional downstream classifiers. Through a multi-task learning design, the model learns shared representations across related objectives, allowing partially labeled datasets to inform one another, and discovering gene subsets that generalize across tasks without additional training steps. We evaluate YOTO on two representative single-cell RNA-seq datasets, showing that it consistently outperforms state-of-the-art baselines. These results demonstrate that sparse, end-to-end, multi-task gene subset selection improves predictive performance and yields compact and meaningful gene subsets, advancing biomarker discovery and single-cell analysis.
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