Tootfinder

This https://arxiv.org/abs/2408.11884 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_qbi…

ST-USleepNet: A Spatial-Temporal Coupling Prominence Network for Multi-Channel Sleep Staging
Sleep staging is critical to assess sleep quality and diagnose disorders. Despite advancements in artificial intelligence enabling automated sleep staging, significant challenges remain: (1) Simultaneously extracting prominent temporal and spatial sleep features from multi-channel raw signals, including characteristic sleep waveforms and salient spatial brain networks. (2) Capturing the spatial-temporal coupling patterns essential for accurate sleep staging. To address these challenges, we prop…

Jag upplever mer och mer att usla meta-analyser används för att få saker att framstå som etablerad vetenskap när stödet bakom påståendet egentligen är uselt. Under pandemin var det flera sidor som påstods sig ha i princip realtidsuppdaterade meta-analyser som var av den här typen och sen har det fortsatt bli vanligare, tycker jag.
The Research on Fluoride and IQ

#Bitterböse Moritz von Uslar aber gut!
https://meldungenausdemwald.de/ 20250629

Region Västernorrland tycks enligt DN ha haft 60 chefer utan underställda. Nu ska de göra någonting åt det och beräknar en besparing på 3 mnkr! Vet inte vad som är värst, bristen på koll ELLER de extremt usla chefslönerna (dela 3mnkr på 60) 😂

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
Yigit Berkay Uslu, Navid NaderiAlizadeh, Mark Eisen, Alejandro Ribeiro
https://arxiv.org/abs/2506.18748

Fast State-Augmented Learning for Wireless Resource Allocation with Dual Variable Regression
We consider resource allocation problems in multi-user wireless networks, where the goal is to optimize a network-wide utility function subject to constraints on the ergodic average performance of users. We demonstrate how a state-augmented graph neural network (GNN) parametrization for the resource allocation policy circumvents the drawbacks of the ubiquitous dual subgradient methods by representing the network configurations (or states) as graphs and viewing dual variables as dynamic inputs t…