@cdarwin@c.im2026-07-20 23:15:06
Republicans are all abuzz about socialism because the party’s political prospects look dire this fall.
Donald Trump’s approval ratings are in the thirties,
Americans are experiencing surging energy prices,
and Trump and other Republicans are engaged in open corruption while cutting vital government programs.
They need something, and in a pinch, socialism has always been the go-to emergency button for the right to push.
@patrikja@functional.cafe2026-09-22 18:02:42
New pre-print out (and submitted to JFP): "niFite semPurtatoni" (Finite permutations in Agda). We study a first-order representation of finite permutations: every value of this type represents a bijection by construction (1st image). An interesting part is composition — finding a definition that's structurally recursive at all takes some care (2nd image: an example, plus the key lemma that `skip` and `pinch` are approximate inverses). Inversion, and the classical Lehmer-code &q…
@raiders@darktundra.xyz2026-06-23 16:27:36
Insider Shares Details of Fernando Mendoza’s Chances of Starting Week 1 for Raiders https://heavy.com/sports/nfl/las-vegas-raiders/kirk-cousins-week-1-raiders-quarterback-mendoza/
@arXiv_astrophGA_bot@mastoxiv.page2026-07-22 08:09:07
Estimating stellar metallicities from Gaia DR3 XP data using LAMOST DR10
Divyansh Srivastava, Andrzej Niedzielski, Rodolfo Smiljanic
https://arxiv.org/abs/2607.18707 https://arxiv.org/pdf/2607.18707 https://arxiv.org/html/2607.18707
arXiv:2607.18707v1 Announce Type: new
Abstract: Gaia DR3 provides astrophysical parameters for hundreds of millions of stars, but the metallicities [M/H] from its GSP-Phot module suffer from systematic biases. We estimate stellar metallicities from Gaia DR3 data using the homogeneous spectroscopic iron abundances [Fe/H] of LAMOST DR10 as training labels. We cross-matched LAMOST DR10 with Gaia DR3 and trained a gradient-boosted decision-tree regressor (XGBoost) on 1.20 million AFGK stars using only Gaia-derived inputs and proxies. We validated the estimates on held-out LAMOST stars, GALAH DR4, APOGEE DR17, and 46 open clusters, and applied the model to measure the radial metallicity gradient of the Milky Way disk. On the held-out test set, the model achieves a mean absolute error of 0.052 dex and $R^2=0.94$ with negligible bias, compared with 0.242 dex for GSP-Phot on the same stars. The estimates transfer well to external surveys, with mean absolute errors of 0.066 dex for GALAH and 0.068 dex for APOGEE. For open clusters, the median difference between our estimated [Fe/H] and spectroscopic values is 0.041 dex, smaller than both GSP-Phot (0.248 dex) and a previous APOGEE-trained XGBoost model (0.067 dex). Applied to the Galactic disk, our model recovers a broken thin-disk radial gradient, with inner and outer slopes of $ 0.119$ and $-0.058\,\mathrm{dex\,kpc^{-1}}$, respectively, and a break near 5.9 kpc, as well as an open-cluster gradient of $-0.066\,\mathrm{dex\,kpc^{-1}}$; both agree with previous high-resolution spectroscopic studies. Our [Fe/H] estimates are accurate to 0.05-0.07 dex for AFGK stars with $[\mathrm{Fe/H}]\gtrsim-2.5$; below this limit, the predictions should be treated as lower bounds. The catalogue and trained model are publicly available on Zenodo and are suitable for chemical studies of the Milky Way.
toXiv_bot_toot
@aredridel@kolektiva.social2026-07-11 17:06:34
For those that don't know, I help run a large women in technology chat group on Slack. It's been going for 11 years now! (ask me for an invite if you want one and that's appropriate!)
It has deeply shaped how I think about technology, especially the social side. There is so much embodied knowledge in that community, it's amazing. And it's also such a window into how much the mainstream of technology writing is dominated by men. We talk about it differently! We're much more likely to be critical, and to be critical of the _structures_ in tech. Sometimes that comes off as kneejerk "Ugh BRIAN!" to some of the bullshit men to do women in the workplace, but also embedded underneath is an understanding of rarely-mapped power structures in the field. So much advice out there is written assuming that there is no dissent, no silent frustration, no quiet abandoning the job when the pressures are unresolved. And so much of the tech world, press and on social media alike, has no insight that this attrition even happens.
Women, collectively, though, understand it. We notice the patterns of promotions. We understand the way that if we're in our 40s, we're rather likely to have a manager who is a decade younger than we are, with no particular experience. We notice when men are lauded for spending time with their family instead of work on occasion, but women are expected to be present at all times and rarely seen positively for doing the exact same things.
And yet, any given instance is always shrouded in deniability. The pattern generally holds, but is this one my fault? Do I not measure up? Or is it sexism?
That's what these structures rob from us: we never have the clarity in feedback that it is accurate, that is us that must change. When we stick to our guns, are we being obstinate, or are we correct? That information is denied to us by sexism. It only becomes clear in aggregate, and even then it is very hard to find action to take on it except to acknowledge it and move on.
I don't think people talk about that ambiguity enough: we're always looking for the clear sexism, the man speaking over the women, the trading sexual favors for advancement, the clear pattern of pet-to-threat that so many women experience as they age or gain skill in the field. But the bulk of sexism that we experience is in the structural poisoning of feedback.
@mgorny@social.treehouse.systems2026-07-18 16:41:50
Let's get this straight: anything created with "AI" is #slop.
It doesn't have to be vibe-coded. It doesn't have to be horrible code. It doesn't matter that you've spent hours perfecting the prompt. It doesn't matter that ten people have spent a week reviewing it in detail.
As long as an "AI" took part in writing it, it's slop. It's an unethical technology, and anything created with it is tainted. It's an inhuman technology, and you're supporting it. You're being disrespectful to your users and contributors. You're putting your own convenience over humanity. You're aiding the worst murderous assholes in the history of humanity. And what's perhaps worst, you quite likely believe that you are making a net positive contribution, that you're helping people, while you're actually disrespecting and harming them.
#NoAI #NoLLM
@arXiv_mathDG_bot@mastoxiv.page2026-09-15 09:00:41
Polarization complete left invariant connections
Bal\'azs Forman, R\'obert Sz\H{o}ke
https://arxiv.org/abs/2609.15673 https://arxiv.org/pdf/2609.15673 https://arxiv.org/html/2609.15673
arXiv:2609.15673v1 Announce Type: new
Abstract: Let $(M,\nabla)$ be a real analytic Koszul manifold. An adapted complex structure (ac-structure) on a neighborhood $N$ of the zero section in $TM$ is a complex structure on $N$ such that the leaves of the Levi-Civita foliation are holomorphic curves. More generally, a complex polarization $P$ on $N$ is called an ac-polarization if the leaves of the Levi-Civita foliation are tangential to $P$. The bundle of (1,0) tangent vectors of an ac-structure is an ac-polarization.
The connection is called entire (resp. polarization complete or simply $\mathcal P$-complete) if the ac-structure (resp. the ac-polarization) exists on $TM$. Although on a small enough $N$ an ac-structure always exists, entire connections are rear and if a maximal domain of definition $N_{max}$ of an ac-structure exists (different from $TM$), $N_{max}$ is a complicated domain. On the other hand in many cases the associated ac-polarization can be extended to the whole $TM$. The main purpose of the paper is to gain better understanding of this phenomenon using a generalized version of the polar map.
As special cases we show that many of those metrics studied by Aslam-Burns-Irvine and Halverscheid-Iannuzzi although are not entire but are $\mathcal P$-complete.
toXiv_bot_toot
@Sustainable2050@mastodon.energy2026-07-17 05:37:03
Halfway the EU gas storage filling season, the Netherlands is lagging. Our storages are only at 31% now, way behind the EU average of 53%, and other major storage countries like France (52%), Germany (45%) and Italy (71%).
Based on security of supply advice by Gasunie, Dutch government had set a target of 80% by 1 November 2026. Our EU obligation is 74%. Both would practically seem out of reach by now, which is unfortunate in view of our dependence on LNG imports in an unstable world.
@arXiv_qbioNC_bot@mastoxiv.page2026-07-22 07:57:17
How the fly holds a single goal: normalization, not selection, in Drosophila FC2
Gioele Nanni, Christopher Lee
https://arxiv.org/abs/2607.18969 https://arxiv.org/pdf/2607.18969 https://arxiv.org/html/2607.18969
arXiv:2607.18969v1 Announce Type: new
Abstract: A walking fly steers toward a goal direction, held as a bump of activity across the FC2 neurons of the fan-shaped body. These neurons also inhibit one another over distance, more strongly the farther apart they are, a feedback proposed to keep the fly on a single goal. We asked, from the connectome, what circuit produces this inhibition, and whether it lets FC2 actively choose one goal among competitors (a winner-take-all) or simply keeps a goal set elsewhere as one clean bump. Tracing the wiring in a single FlyWire brain, we find the inhibition is almost entirely global: four FB5A cells inhibit every FC2 neuron roughly equally, with a smaller, distance-dependent contribution from hDelta interneurons and a negligible direct component. A ring-attractor winner-take-all (the kind the compass uses) requires local recurrent excitation that the FC2 wiring lacks, so this geometry cannot build one; and across a range of dynamical models, including a spiking network, no version of the circuit locks onto a winner at the connectome-scaled reference coupling. FC2 therefore normalizes an externally set goal rather than selecting it, with FB5A likely acting as the global normalizer, much as the APL neuron does in the mushroom body. We are explicit about two open points: a different mechanism, mutual inhibition between two competing goals (which hDelta supplies), could in principle select at very strong coupling, and we bound rather than exclude it; and FB5A's inhibitory identity is a low-confidence prediction of the connectome's transmitter classifier, not yet measured, and likely not GABAergic. We then ask where the goal is actually set: the connectome nominates an upstream hDelta network and rules out the leading proposed alternative, whose neurons supply under 0.2% of FC2's input. Finally, we propose a direct experiment, silencing FB5A while imaging FC2, that would test the account.
toXiv_bot_toot
@cdarwin@c.im2026-09-20 15:28:07
Donald Trump is resisting new restrictions on AI
at a moment when his family and some of his closest allies have accumulated financial interests in the infrastructure and defense-technology ecosystem expanding alongside the AI boom,
an overlap that has now drawn scrutiny from Democratic lawmakers, government ethics watchdogs
and, by some accounts, his own senior aides.
