Tootfinder

Experts: Stuxnet-linked Fast16 malware, designed to subvert nuclear weapons testing simulations, was likely part of a campaign to slow Iran's nuclear ambitions (Kim Zetter/ZERO DAY)
https://www.zetter-zeroday.com/experts-con

Experts Confirm the Fast16 Malware Was Sabotaging Nuclear Weapons Tests, Likely in Iran
Fast16 didn't predate Stuxnet but was contemporaneous with it. It also wasn't aimed at altering nuclear weapons but was simply feeding false data to engineers about the nuclear detonation tests they were conducting, in order to trick them into believing the tests were failing.

Got an Amber Alert today, and my weather app, @…, did a better job than the state of Tennessee. In fact, TN failed, as they do at most things lately.
The alert from TN had a Bitly link (already looks suspicious) that goes to a Facebook page that requires you to login. :picard:

I've been enjoying practicing for field day using my fork of Morse Walker, and also using cwsim for a more realistic simulation of a busy contest CW experience.
It made me wonder whether anyone had made a cwsim fork to support field day.
When I did a Google search, it sent me to Gemini, which helpfully …

Government #censorship is having a direct impact on federally funded research, restricting the topics addressed and the experiences & perspectives recognized. Join Jeremy Berg, Former #NIH institute director and Editor-in-Chief, Science magazine, Michael D. Green, Ph.D., Early Career Researcher…

Integrative neurocybernetic modeling in the era of large-scale neuroscience
Il Memming Park, Ayesha Vermani, Gonzalo G. de Polavieja, Juan \'Alvaro Gallego, Kathleen Esfahany, Shreya Saxena, Michael Orger, Auke Ijspeert, Matthew Dowling, Daniel McNamee, Srinivas C. Turaga, Zachary Mainen, Joseph J. Paton, Alfonso Renart
https://arxiv.org/abs/2604.23903 https://arxiv.org/pdf/2604.23903 https://arxiv.org/html/2604.23903
arXiv:2604.23903v1 Announce Type: new
Abstract: Large-scale neuroscience is generating rich datasets across animals, brain areas and behavioral contexts, yet our modeling efforts remains fragmented across isolated experiments. We argue that understanding behavior requires integrative neurocybernetic models: understandable dynamical models that capture the closed-loop coupling of brain, body and environment, treat the brain as a controller pursuing latent objectives, represent structured variation across scales, and scale to heterogeneous datasets. Such models shift the goal from predicting neural recordings in isolation to inferring the organizing principles that govern neural and behavioral dynamics. We outline a practical route toward this goal by combining nonlinear state-space models and meta-dynamical extensions with scalable inference, knowledge distillation, mixed open- and closed-loop training, and connectomics-informed architectures. By pooling complementary constraints from recordings, behavior, perturbations and anatomy, integrative neurocybernetic models can provide statistical amplification, few-shot generalization, and mechanistic insight into shared dynamical structure, individual variation, and the control objectives that govern behavior. This agenda offers a model-centric path from fragmented data to a mechanistic science of how brains produce behavior.
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