Tootfinder

Konstantin Ryabitsev, an admin of kernel.org infrastructure, reported on increasing difficulties they are having with SMTP deliveries.
"major hosts are increasingly not interested in getting mail from anyone who isn't also a major mail service provider"
"With lore and public-inbox, we *are* in the process of moving away from
relying on the increasingly unreliable SMTP layer"

🔍 Full transparency in every layer: Verifiable SBOMs, SLSA Build Level 3 provenance, public CVE data - no hidden or downgraded vulnerabilities
🏗️ Built on trusted foundations: #Alpine and #Debian as proven open-source bases - minimal migration effort, maximum compatibility with existing wo…

Deconstructing Attention: Investigating Design Principles for Effective Language Modeling
Huiyin Xue, Nafise Sadat Moosavi, Nikolaos Aletras
https://arxiv.org/abs/2510.11602 htt…

Deconstructing Attention: Investigating Design Principles for Effective Language Modeling
The success of Transformer language models is widely credited to their dot-product attention mechanism, which interweaves a set of key design principles: mixing information across positions (enabling multi-token interactions), sequence-dependent activations (where attention weights adapt to each input), a specific mathematical form (dot-product similarities plus softmax weighting), and coupling of queries and keys to evolving hidden states (grounding attention in the current layer). However, th…

Based on Deep Neural Networks: A Machine Learning-Assisted Channel Estimation Method for MIMO Systems
Haoran He
https://arxiv.org/abs/2510.11891 https://ar…

Based on Deep Neural Networks: A Machine Learning-Assisted Channel Estimation Method for MIMO Systems
This paper proposes a machine learning-assisted channel estimation approach for massive MIMO systems, leveraging DNNs to outperform traditional LS and MMSE methods. In 5G and beyond, accurate channel estimation mitigates pilot contamination and high mobility issues that harm system reliability. The proposed DNN architecture includes multi-layer perceptrons with ReLU activation, 3 hidden layers (256, 128, 64 neurons respectively), uses Adam optimizer (learning rate 1e-4) and MSE loss function. I…