Tootfinder

I’m so proud of my little brother.
I just spoke to him in his dorm room at a top 30 college here in the US, he just arrived for freshman orientation.
He got in on a full ride - tuition, housing, food, books, travel, stipend for daily expenses. Won a competitive scholarship to do so.
More than that, he’s had a tougher road than most to get there:
- he had to suddenly move away from Manila, Philippines (where he grew up) in middle school because of COVID restrictions that didn’t let kids go outside (2020)
- then, just as he adjusted to school and a different language in our home country, Ukraine, Russia invaded (2022)
- he stayed in Greece for a month while I was calling our congressional representative here in NY and negotiating with the US embassy to get them a visa ASAP to enter the US and be with me and my husband. There were no paths for Ukrainian refugees yet, we just wanted them with us temporarily for a few months to figure out what options they even had next.
- he had to wait, not going to school, with no clue where they’d move next, until TPS became available to Ukrainians and they got to stay here in the US
- then he had to continue high school in yet another system, yet another country, amidst news of bombings and destruction back home
- my mother wasn’t allowed to work for months while their documents were pending, so we had to raise money with a public GoFundMe campaign and my husband and I maxed out our credit cards to help them get by
- they shared a one-room cottage for the first year, graciously hosted for free by an elderly local couple
- he saw a therapist who also graciously took him in for free while they didn’t have insurance
- he had to graduate high school amidst news of other immigrant students getting arrested, detained, and deported at their own graduations around the country
- he wasn’t sure if he would even make it to college as this administration publicly considered canceling TPS for Ukrainians and cutting off their pathway to maintaining legal status.
We don’t know what tomorrow holds. But he’s there. He’s on campus. He got to go to college.
I love him so much.

"Pediatric asthma ER visits dropped 40 percent after a coal processing plant near Pittsburgh closed down, researchers found."
Kids in Pennsylvania Are Breathing (Much) Easier After a Coal Plant Shuttered https://insideclimatenews.org/news/050

Kids in Pennsylvania Are Breathing (Much) Easier After a Coal Plant Shuttered - Inside Climate News
Pediatric asthma ER visits dropped 40 percent after a coal processing plant near Pittsburgh closed down, researchers found.

So, Altman’s superintelligence will allegedly do away with millions of jobs, but don’t worry kids, after graduating you “could be leaving on some kind of mission to explore the solar system on a spaceship.”
“Some kind of a mission,” huh? That’s a nice way of saying that he’ll shoot useless people into space.
This level of stupidity and arrogance is sickening.

Do the #BBC hand over website news stories to the work experience kids at the weekend, or something? This is embarrassingly bad.
https://www.bbc.co.uk/news/articles/cn7zngr6jn5o

Today I discovered a very nice "experience" park in the park in Amsterdam east. It's aimed at kids but it's so cool that I'm tempted to come back just with my significant other.

Meta stellt sich gegen die KI-Pläne der EU
Meta, der US-Konzern hinter Facebook und Instagram, lehnt den EU-Kodex zur KI-Regulierung ab. Das Unternehmen sieht Überregulierung und Innovationsbremsen.
https://www.

Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures
Sara M. Ichinaga, Steven L. Brunton, Aleksandr Y. Aravkin, J. Nathan Kutz
https://arxiv.org/abs/2507.19787

Sparse-mode Dynamic Mode Decomposition for Disambiguating Local and Global Structures
The dynamic mode decomposition (DMD) is a data-driven approach that extracts the dominant features from spatiotemporal data. In this work, we introduce sparse-mode DMD, a new variant of the optimized DMD framework that specifically leverages sparsity-promoting regularization in order to approximate DMD modes which have localized spatial structure. The algorithm maintains the noise-robust properties of optimized DMD while disambiguating between modes which are spatially local versus global in na…

Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
Harrison Katz, Liz Medina, Robert E. Weiss
https://arxiv.org/abs/2506.13973

Sensitivity Analysis of Priors in the Bayesian Dirichlet Auto-Regressive Moving Average Model
Prior choice can strongly influence Bayesian Dirichlet ARMA (B-DARMA) inference for compositional time-series. Using simulations with (i) correct lag order, (ii) overfitting, and (iii) underfitting, we assess five priors: weakly-informative, horseshoe, Laplace, mixture-of-normals, and hierarchical. With the true lag order, all priors achieve comparable RMSE, though horseshoe and hierarchical slightly reduce bias. Under overfitting, aggressive shrinkage-especially the horseshoe-suppresses noise …