Tootfinder

The variety and number of strategic aircraft shown to be attacked by Ukrainian drones in this video is... shocking.
Bombers, transports, "AWACs”.
Some of the drones are even shown *landing softly on top* of the bombers before, presumably denotating.
A really stunning operation on all sorts of levels but while this will certainly put a dent in some of Russia’s airborne capabilities, the fact the operation took more than a year to plan and execute shows that this isn't exactly an immediate game changer and is not proof of any new Ukrainian ability that would stop or reverse Russia's aggression.
#RussiaUkraineWar
https://kyivindependent.com/ukraines-sbu-releases-exclusive-footage-of-operation-spiderweb-targeting-russias-strategic-aircraft/

Sources: Byron Allen takes substantial steps toward selling all of his local TV stations, including those affiliated with ABC, CBS, Fox, and NBC in 24 markets (Matthew Keys/Multicast News)
https://multicastnews.com/p/exclusive-byron-allen-selling-off

Exclusive: Byron Allen is selling his local TV stations
Allen has received offers for some or all of his local TV station portfolio; a divestiture allows Allen Media Group to pay down existing debt, rather than refinance it.

This https://arxiv.org/abs/2505.19746 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_csCV_…

Improving Heart Rejection Detection in XPCI Images Using Synthetic Data Augmentation
Accurate identification of acute cellular rejection (ACR) in endomyocardial biopsies is essential for effective management of heart transplant patients. However, the rarity of high-grade rejection cases (3R) presents a significant challenge for training robust deep learning models. This work addresses the class imbalance problem by leveraging synthetic data generation using StyleGAN to augment the limited number of real 3R images. Prior to GAN training, histogram equalization was applied to sta…

This https://arxiv.org/abs/2504.08201 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

Neural Encoding and Decoding at Scale
Recent work has demonstrated that large-scale, multi-animal models are powerful tools for characterizing the relationship between neural activity and behavior. Current large-scale approaches, however, focus exclusively on either predicting neural activity from behavior (encoding) or predicting behavior from neural activity (decoding), limiting their ability to capture the bidirectional relationship between neural activity and behavior. To bridge this gap, we introduce a multimodal, multi-task m…

Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer
Wenhao Guo, Yuda Wang, Zeqiao Huang, Changjiang Zhang, Shumin ma
https://arxiv.org/abs/2505.05595

Trading Under Uncertainty: A Distribution-Based Strategy for Futures Markets Using FutureQuant Transformer
In the complex landscape of traditional futures trading, where vast data and variables like real-time Limit Order Books (LOB) complicate price predictions, we introduce the FutureQuant Transformer model, leveraging attention mechanisms to navigate these challenges. Unlike conventional models focused on point predictions, the FutureQuant model excels in forecasting the range and volatility of future prices, thus offering richer insights for trading strategies. Its ability to parse and learn from…