Tootfinder

«sometimes the powerful commit or condone or bankroll acts of unspeakable evil, and any institution that prioritizes cashing the checks over calling out the evil is no longer an arts organization. It's a reputation-laundering firm with a well-read board.»
This is the same lesson so many academic institutions refuse to learn (of course they do, the income for their overpaid "leadership" depends on it).
https://bookwyrm.social/user/gedankenstuecke/quotation/7969803#anchor-7969803

Yesterday Trump told us that his plan was to remove 21m people from the United States,
or 6% of our current population and at least 5% of our total current workforce.
“All of them have to go home.”
A few points about this insane post:
Best estimates are that the total undocumented population in the US is between 12m and 15m.
The figure that Trump and his regime keeps using
- 21m came in under Biden
- is made up and false.
Here’s the latest Pe…

Trump's Escalating Migrant Terror Campaign Is Unpopular, Out Of Control, Inhumane And Doing Enormous Harm To The Country
Leon Krauze, long time Los Angeles Univision anchor, joins us Live at 12:15pm ET today

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
Xiaotian Zhang, Yuan Wang, Zhaopeng Feng, Ruizhe Chen, Zhijie Zhou, Yan Zhang, Hongxia Xu, Jian Wu, Zuozhu Liu
https://arxiv.org/abs/2506.12307

Med-U1: Incentivizing Unified Medical Reasoning in LLMs via Large-scale Reinforcement Learning
Medical Question-Answering (QA) encompasses a broad spectrum of tasks, including multiple choice questions (MCQ), open-ended text generation, and complex computational reasoning. Despite this variety, a unified framework for delivering high-quality medical QA has yet to emerge. Although recent progress in reasoning-augmented large language models (LLMs) has shown promise, their ability to achieve comprehensive medical understanding is still largely unexplored. In this paper, we present Med-U1, …

The $p$-adic valuation of local resolvents, generalized Gauss sums and anticyclotomic Hecke $L$-values of imaginary quadratic fields at inert primes
Ashay Burungale, Shinichi Kobayashi, Kazuto Ota
https://arxiv.org/abs/2507.08221

The $p$-adic valuation of local resolvents, generalized Gauss sums and anticyclotomic Hecke $L$-values of imaginary quadratic fields at inert primes
We prove an asymptotic formula for the $p$-adic valuation of Hecke $L$-values of an imaginary quadratic field at an inert prime $p$ along the anticyclotomic $\mathbb{Z}_p$-tower. The key is determination of the $p$-adic valuation of generalized Gauss sums defined using Coates-Wiles homomorphism, and of local resolvents in $\mathbb{Z}_p$-extensions. This answers a question of Rubin.

Hertz and other car rental agencies are increasingly relying on scanners that use high-res imaging and AI to flag even tiny blemishes, angering some customers (Gabe Castro-Root/New York Times)
https://www.nytimes.com/2025/07/09/travel/rental-car-ai-scann…

Hertz and Other Rental Car Agencies Turn to AI for Damage Detection
Hertz and other agencies are increasingly relying on scanners that use high-res imaging and A.I. to flag even tiny blemishes, and customers aren’t happy.

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
Tyler J. Richards, Adam E. Flanders, Errol Colak, Luciano M. Prevedello, Robyn L. Ball, Felipe Kitamura, John Mongan, Maryam Vazirabad, Hui-Ming Lin, Anne Kendell, Thanat Kanthawang, Salita Angkurawaranon, Emre Altinmakas, Hakan Dogan, Paulo Eduardo de Aguiar Kuriki, Arjuna Somasundaram, Christopher Ruston, Deniz Bulja, Naida Spahovic, Jennifer Sommer, Sirui Jiang, Eduardo Moreno Judice de Mattos Farina, Edu…

The RSNA Lumbar Degenerative Imaging Spine Classification (LumbarDISC) Dataset
The Radiological Society of North America (RSNA) Lumbar Degenerative Imaging Spine Classification (LumbarDISC) dataset is the largest publicly available dataset of adult MRI lumbar spine examinations annotated for degenerative changes. The dataset includes 2,697 patients with a total of 8,593 image series from 8 institutions across 6 countries and 5 continents. The dataset is available for free for non-commercial use via Kaggle and RSNA Medical Imaging Resource of AI (MIRA). The dataset was cre…

When I say that the protesters were angry but not violent, I guess I should also say this:
In the sick imagination of white supremacy, for a Black or brown person to be angry •is• for them to be violent, by definition.
So when you hear people saying “they were violent,” those people are flat wrong — but they may also be describing something that is in fact completely real and completely consistent in their mind.

I have a nice Anker power bank with a built-in USB-C cable strong enough to hold it by.
But recently they recalled it and offered to send a replacement. I just unboxed the replacement… and it’s a different model. One which is physically larger and has no built-in cable. Same capacity, higher wattage, but not what I want.
In fact the original model is no longer sold (at that capacity). I didn’t realize they’d recalled all of them…
Oh well. I imagine they’ll bring them b…

CUE-RAG: Towards Accurate and Cost-Efficient Graph-Based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval
Yaodong Su, Yixiang Fang, Yingli Zhou, Quanqing Xu, Chuanhui Yang
https://arxiv.org/abs/2507.08445

CUE-RAG: Towards Accurate and Cost-Efficient Graph-Based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval
Despite the remarkable progress of Large Language Models (LLMs), their performance in question answering (QA) remains limited by the lack of domain-specific and up-to-date knowledge. Retrieval-Augmented Generation (RAG) addresses this limitation by incorporating external information, often from graph-structured data. However, existing graph-based RAG methods suffer from poor graph quality due to incomplete extraction and insufficient utilization of query information during retrieval. To overcom…

I find myself thinking about a long-lost friend a lot lately. Sometime in the late 90s, we both accepted that our friendship had run its course. I had come to embrace fairly low-key socialism and he was increasingly angry about the media's "liberal bias." The common ground we had once shared was gone. I imagine him cheering now, even as I recall his father, a deeply conservative Marine colonel, remarking that only authoritarian regimes used the military against their own citize…