Tootfinder

Of interest to experimental petrologists, upcoming virtual workshop "to discuss and plan to address systematic bias in predictive models caused by the way trace element partitioning data is currently published." #geology #geochemistry ⚒️

Overview
This workshop overview page outlines a February–March 2026 virtual event for experimental petrologists, editors, and program managers to address systemic bias in trace element partitioning data by advocating for publication of raw analytical data to support FAIR principles, improve model accuracy, and establish community-driven data standards.

LoD-Structured 3D Gaussian Splatting for Streaming Video Reconstruction
Xinhui Liu, Can Wang, Lei Liu, Zhenghao Chen, Wei Jiang, Wei Wang, Dong Xu
https://arxiv.org/abs/2601.18475 https://arxiv.org/pdf/2601.18475 https://arxiv.org/html/2601.18475
arXiv:2601.18475v1 Announce Type: new
Abstract: Free-Viewpoint Video (FVV) reconstruction enables photorealistic and interactive 3D scene visualization; however, real-time streaming is often bottlenecked by sparse-view inputs, prohibitive training costs, and bandwidth constraints. While recent 3D Gaussian Splatting (3DGS) has advanced FVV due to its superior rendering speed, Streaming Free-Viewpoint Video (SFVV) introduces additional demands for rapid optimization, high-fidelity reconstruction under sparse constraints, and minimal storage footprints. To bridge this gap, we propose StreamLoD-GS, an LoD-based Gaussian Splatting framework designed specifically for SFVV. Our approach integrates three core innovations: 1) an Anchor- and Octree-based LoD-structured 3DGS with a hierarchical Gaussian dropout technique to ensure efficient and stable optimization while maintaining high-quality rendering; 2) a GMM-based motion partitioning mechanism that separates dynamic and static content, refining dynamic regions while preserving background stability; and 3) a quantized residual refinement framework that significantly reduces storage requirements without compromising visual fidelity. Extensive experiments demonstrate that StreamLoD-GS achieves competitive or state-of-the-art performance in terms of quality, efficiency, and storage.
toXiv_bot_toot

Crosslisted article(s) found for cond-mat.dis-nn. https://arxiv.org/list/cond-mat.dis-nn/new
[1/1]:
- Partitioning networks into clusters of synchronized nodes via the message-passing algorithm: an u...
Massimo Ostilli

Binary partitioning with k-d trees is silly amazing. And with OSM data in radians and pretending the planet is a perfect sphere, scikit-learn's BallTree works wonderfully for quickly finding things nearby some reference point.
Like how far away the nearest bicycle_parking is for every building in a city.
#IMadeAThing #python #osm #overpassql