Tootfinder

Sources: ByteDance bought more Nvidia chips than any other Chinese company in 2025, but Chinese regulators now block it from using the chips in new data centers (Qianer Liu/The Information)
https://www.theinformation.com/articles/china-slowly-surely-breaking-…

China Is Slowly but Surely Breaking Free From Nvidia
ByteDance bought more Nvidia chips than any other Chinese company in 2025, according to three people with direct knowledge of its purchases. Racing to secure computing power for its billion-plus users and fearing Washington might suddenly cut off supply, the TikTok parent hoarded its stock of ...

Differentially Private Wasserstein Barycenters
Anming Gu, Sasidhar Kunapuli, Mark Bun, Edward Chien, Kristjan Greenewald
https://arxiv.org/abs/2510.03021 https://

Differentially Private Wasserstein Barycenters
The Wasserstein barycenter is defined as the mean of a set of probability measures under the optimal transport metric, and has numerous applications spanning machine learning, statistics, and computer graphics. In practice these input measures are empirical distributions built from sensitive datasets, motivating a differentially private (DP) treatment. We present, to our knowledge, the first algorithms for computing Wasserstein barycenters under differential privacy. Empirically, on synthetic d…

🕯️Simple casting and heat treatment yields alloy with both strength and ductility
https://techxplore.com/news/2025-09-simple-treatment-yields-alloy-strength.html

Simple casting and heat treatment yields alloy with both strength and ductility
In the world of metals, it has long been considered common knowledge that achieving both strength and ductility (the ability to stretch without breaking) at the same time is nearly impossible. When a metal is made stronger, it typically becomes brittle, and when it is made more ductile, it usually loses strength—like a seesaw, where one property increases as the other decreases.

Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Luca Bergamin, Giovanna Maria Dimitri, Fabio Aiolli
https://arxiv.org/abs/2509.22399

Integrating Background Knowledge in Medical Semantic Segmentation with Logic Tensor Networks
Semantic segmentation is a fundamental task in medical image analysis, aiding medical decision-making by helping radiologists distinguish objects in an image. Research in this field has been driven by deep learning applications, which have the potential to scale these systems even in the presence of noise and artifacts. However, these systems are not yet perfected. We argue that performance can be improved by incorporating common medical knowledge into the segmentation model's loss function. To…

#AI is not your friend…
#NYC
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