Tootfinder

Die spinnen, die TechBros.
"OpenAI executives have discussed filing an antitrust complaint with US regulators against Microsoft, the company's largest investor, The Wall Street Journal reported Monday, marking a dramatic escalation in tensions between the two long-term AI partners."

Senators Assail Antidoping Regulator for 'Stonewalling and Intimidation' (Michael S. Schmidt/New York Times)
https://www.nytimes.com/2025/06/17/us/politics/senate-swimmers-antidoping-regulator.html
http://www.memeorandum.com/250617/p176#a250617p176

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Amirreza Fateh, Yasin Rezvani, Sara Moayedi, Sadjad Rezvani, Fatemeh Fateh, Mansoor Fateh
https://arxiv.org/abs/2506.14318

BRISC: Annotated Dataset for Brain Tumor Segmentation and Classification with Swin-HAFNet
Accurate segmentation and classification of brain tumors from Magnetic Resonance Imaging (MRI) remain key challenges in medical image analysis, largely due to the lack of high-quality, balanced, and diverse datasets. In this work, we present a new curated MRI dataset designed specifically for brain tumor segmentation and classification tasks. The dataset comprises 6,000 contrast-enhanced T1-weighted MRI scans annotated by certified radiologists and physicians, spanning three major tumor types-g…

Antidirected paths in oriented graphs
Andrzej Grzesik, Marek Skrzypczyk
https://arxiv.org/abs/2506.11866 https://arxiv.org/pdf/2506.1…

Antidirected paths in oriented graphs
We show that for any integer $k \ge 4$, every oriented graph with minimum semidegree bigger than $\frac{1}{2}(k-1+\sqrt{k-3})$ contains an antidirected path of length $k$. Consequently, every oriented graph on $n$ vertices with more than $(k-1+\sqrt{k-3})n$ edges contains an antidirected path of length $k$. This asymptotically proves the antidirected path version of a conjecture of Stein and of a conjecture of Addario-Berry, Havet, Linhares Sales, Reed and Thomassé, respectively.

The arts and education — when they are at their best, anyway — have in common this deep root: they ask us to see and to think and to feel, to see with fresh eyes, to be aware of our own seeing, to be •active• in our seeing.
In that way, both the arts and education are antidotes to the AI hype cycle, and similar endeavors of capitalism run amok.
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Here I find myself meditatively annotating data structures in a binary.
Clever stuff in there.

Mr. Abrego Garcia, speaking through an interpreter, has entered "not guilty" pleas to the two charges
@tennesseelookout.com
https://bsky.app/profile/anitawadhwani.bsky.social/post/3lripyl3g5s2q

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
Fuaing Niu, Zini Chen, Zhiyu Xie, Genan Dai, Bowen Zhang
https://arxiv.org/abs/2506.13343

TwiUSD: A Benchmark Dataset and Structure-Aware LLM Framework for User Stance Detection
User-level stance detection (UserSD) remains challenging due to the lack of high-quality benchmarks that jointly capture linguistic and social structure. In this paper, we introduce TwiUSD, the first large-scale, manually annotated UserSD benchmark with explicit followee relationships, containing 16,211 users and 47,757 tweets. TwiUSD enables rigorous evaluation of stance models by integrating tweet content and social links, with superior scale and annotation quality. Building on this resource,…

But here’s the thing: anyone could make music •before• gen AI. Some more skilled or more artistically successful than others, sure! But that’s not the point. •Doing it• is the point. •Living it• is the point.
A product that promises to generate it for you so that you neither do it nor live it is antithetical to the point, is hostile to the idea of art itself.
(Note: that’s exactly what the artists in the OP are •not• doing! They are all grabbing the AI and actively •doing• and •living• while poking at the curious new object.)
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orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Niran Nataraj, Maina Sogabe, Kenji Kawashima
https://arxiv.org/abs/2506.14303

orGAN: A Synthetic Data Augmentation Pipeline for Simultaneous Generation of Surgical Images and Ground Truth Labels
Deep learning in medical imaging faces obstacles: limited data diversity, ethical issues, high acquisition costs, and the need for precise annotations. Bleeding detection and localization during surgery is especially challenging due to the scarcity of high-quality datasets that reflect real surgical scenarios. We propose orGAN, a GAN-based system for generating high-fidelity, annotated surgical images of bleeding. By leveraging small "mimicking organ" datasets, synthetic models that replicate t…