Tootfinder

Differential rotation of solar {\alpha}-sunspots and implications for stellar light curves
Emily Joe L\"o{\ss}nitz, Alexander G. M. Pietrow, Hritam Chakraborty, Meetu Verma, Ioannis Kontogiannis, Horst Balthasar, Carsten Denker, Monika Lendl
https://arxiv.org/abs/2508.08196

Differential rotation of solar α-sunspots and implications for stellar light curves
Differential rotation is a key driver of magnetic activity and dynamo processes in the Sun and other stars, especially as the rate differs across the solar layers, but also in active regions. We aim to accurately quantify the velocity at which round α-spots traverse the solar disk as a function of their latitude, and compare these rates to those of the quiet-Sun and other sunspot types. We then extend this work to other stars and investigate how differential rotation affects the modulation of …

This is the third full build iteration of my miniature #QRP unun supporting both EFHW and "random wire" antennas. At this point, it is still fiddly to build, but I'd expect an experienced #HamRadio DIY / homebrew enthusiast to find the build not terribly difficult.
There's nothing…

CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather
Minghao Ning, Yufeng Yang, Keqi Shu, Shucheng Huang, Jiaming Zhong, Maryam Salehi, Mahdi Rahmani, Yukun Lu, Chen Sun, Aladdin Saleh, Ehsan Hashemi, Amir Khajepour
https://arxiv.org/abs/2507.02245

CoInfra: A Large-Scale Cooperative Infrastructure Perception System and Dataset in Adverse Weather
We present CoInfra, a large-scale cooperative infrastructure perception system and dataset designed to advance robust multi-agent perception under real-world and adverse weather conditions. The CoInfra system includes 14 fully synchronized sensor nodes, each equipped with dual RGB cameras and a LiDAR, deployed across a shared region and operating continuously to capture all traffic participants in real-time. A robust, delay-aware synchronization protocol and a scalable system architecture that …

Now out in #TMLR:
🍇 GRAPES: Learning to Sample Graphs for Scalable Graph Neural Networks 🍇
There's lots of work on sampling subgraphs for GNNs, but relatively little on making this sampling process _adaptive_. That is, learning to select the data from the graph that is relevant for your task.
We introduce an RL-based and a GFLowNet-based sampler and show that the approach perf…

CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks
Li-Hsiang Shen, Jyun-Jhe Huang
https://arxiv.org/abs/2507.16204

CHIMERA: Compressed Hybrid Intelligence for Twin-Model Enhanced Multi-Agent Deep Reinforcement Learning for Multi-Functional RIS-Assisted Space-Air-Ground Integrated Networks
A space-air-ground integrated network (SAGIN) architecture is proposed, empowered by multi-functional reconfigurable intelligent surfaces (MF-RIS) capable of simultaneously reflecting, amplifying, and harvesting wireless energy. The MF-RIS plays a pivotal role in addressing the energy shortages of low-Earth orbit (LEO) satellites operating in shadowed regions, while explicitly accounting for both communication and computing energy consumption across the SAGIN nodes. To maximize the long-term en…

Airalogy: AI-empowered universal data digitization for research automation
Zijie Yang, Qiji Zhou, Fang Guo, Sijie Zhang, Yexun Xi, Jinglei Nie, Yudian Zhu, Liping Huang, Chou Wu, Yonghe Xia, Xiaoyu Ma, Yingming Pu, Panzhong Lu, Junshu Pan, Mingtao Chen, Tiannan Guo, Yanmei Dou, Hongyu Chen, Anping Zeng, Jiaxing Huang, Tian Xu, Yue Zhang
https://

Airalogy: AI-empowered universal data digitization for research automation
Research data are the foundation of Artificial Intelligence (AI)-driven science, yet current AI applications remain limited to a few fields with readily available, well-structured, digitized datasets. Achieving comprehensive AI empowerment across multiple disciplines is still out of reach. Present-day research data collection is often fragmented, lacking unified standards, inefficiently managed, and difficult to share. Creating a single platform for standardized data digitization needs to overc…

An efficient and robust high-order compact ALE gas-kinetic scheme for unstructured meshes
Yibo Wang, Xing Ji, Liang Pan
https://arxiv.org/abs/2508.11283 https://

An efficient and robust high-order compact ALE gas-kinetic scheme for unstructured meshes
For the arbitrary-Lagrangian-Eulerian (ALE) calculations, the geometric information needs to be calculated at each time step due to the movement of mesh. To achieve the high-order spatial accuracy, a large number of matrix inversions are needed, which affect the efficiency of computation dramatically. In this paper, an efficient and robust high-order compact ALE gas-kinetic scheme is developed for the compressible moving grids and moving boundary problems. The memory-reduction reconstruction is…