Tootfinder

Do you have a long running calculation freezing up your shiny app? {callr} or {crew} might help: https://discindo.org/post/asynchronous-execution-in-shiny/

Also wrote a blog post with some of the art/doodles/bad photoshops I made for over/under, and a bit about long term asynchronous roleplaying in that game near the end
https://seedlinggames.com/blogging/overunder/art.html

Replaced article(s) found for cs.LG. https://arxiv.org/list/cs.LG/new
[2/8]:
- Aggregation on Learnable Manifolds for Asynchronous Federated Optimization
Archie Licudi, Anshul Thakur, Soheila Molaei, Danielle Belgrave, David Clifton

Replaced article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- SwitchFS: Asynchronous Metadata Updates for Distributed Filesystems with In-Network Coordination
Jingwei Xu, Mingkai Dong, Qiulin Tian, Ziyi Tian, Tong Xin, Haibo Chen

Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Wanli Ni, Hui Tian, Shuai Wang, Chengyang Li, Lei Sun, Zhaohui Yang
https://arxiv.org/abs/2510.05713

Federated Split Learning for Resource-Constrained Robots in Industrial IoT: Framework Comparison, Optimization Strategies, and Future Directions
Federated split learning (FedSL) has emerged as a promising paradigm for enabling collaborative intelligence in industrial Internet of Things (IoT) systems, particularly in smart factories where data privacy, communication efficiency, and device heterogeneity are critical concerns. In this article, we present a comprehensive study of FedSL frameworks tailored for resource-constrained robots in industrial scenarios. We compare synchronous, asynchronous, hierarchical, and heterogeneous FedSL fram…

Replaced article(s) found for cs.OS. https://arxiv.org/list/cs.OS/new
[1/1]:
- SwitchFS: Asynchronous Metadata Updates for Distributed Filesystems with In-Network Coordination
Jingwei Xu, Mingkai Dong, Qiulin Tian, Ziyi Tian, Tong Xin, Haibo Chen

Learning Neuron Dynamics within Deep Spiking Neural Networks
Eric Jahns, Davi Moreno, Michel A. Kinsy
https://arxiv.org/abs/2510.07341 https://arxiv.org/pd…

Learning Neuron Dynamics within Deep Spiking Neural Networks
Spiking Neural Networks (SNNs) offer a promising energy-efficient alternative to Artificial Neural Networks (ANNs) by utilizing sparse and asynchronous processing through discrete spike-based computation. However, the performance of deep SNNs remains limited by their reliance on simple neuron models, such as the Leaky Integrate-and-Fire (LIF) model, which cannot capture rich temporal dynamics. While more expressive neuron models exist, they require careful manual tuning of hyperparameters and a…

Replaced article(s) found for cs.PF. https://arxiv.org/list/cs.PF/new
[1/1]:
- SwitchFS: Asynchronous Metadata Updates for Distributed Filesystems with In-Network Coordination
Jingwei Xu, Mingkai Dong, Qiulin Tian, Ziyi Tian, Tong Xin, Haibo Chen

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- FedQS: Optimizing Gradient and Model Aggregation for Semi-Asynchronous Federated Learning
Yunbo Li, Jiaping Gui, Zhihang Deng, Fanchao Meng, Yue Wu

Crosslisted article(s) found for cs.DC. https://arxiv.org/list/cs.DC/new
[1/1]:
- Ringleader ASGD: The First Asynchronous SGD with Optimal Time Complexity under Data Heterogeneity
Artavazd Maranjyan, Peter Richt\'arik