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from my link log —
yabai: A tiling window manager for macOS based on binary space partitioning.
https://github.com/asmvik/yabai
saved 2026-02-25 https://

GitHub - asmvik/yabai: A tiling window manager for macOS based on binary space partitioning
A tiling window manager for macOS based on binary space partitioning - asmvik/yabai

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.

@… it should be fine, after installation, however you'll need to customise during installation because FreeBSD Installer defaults to using an entire device (with a ZFS pool for the OS).
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BuffCut: Prioritized Buffered Streaming Graph Partitioning
Linus Baumg\"artner, Adil Chhabra, Marcelo Fonseca Faraj, Christian Schulz
https://arxiv.org/abs/2602.21248 https…

Fast Sparse Matrix Permutation for Mesh-Based Direct Solvers
Behrooz Zarebavami, Ahmed H. Mahmoud, Ana Dodik, Changcheng Yuan, Serban D. Porumbescu, John D. Owens, Maryam Mehri Dehnavi, Justin Solomon
https://arxiv.org/abs/2602.00898 https://arxiv.org/pdf/2602.00898 https://arxiv.org/html/2602.00898
arXiv:2602.00898v1 Announce Type: new
Abstract: We present a fast sparse matrix permutation algorithm tailored to linear systems arising from triangle meshes. Our approach produces nested-dissection-style permutations while significantly reducing permutation runtime overhead. Rather than enforcing strict balance and separator optimality, the algorithm deliberately relaxes these design decisions to favor fast partitioning and efficient elimination-tree construction. Our method decomposes permutation into patch-level local orderings and a compact quotient-graph ordering of separators, preserving the essential structure required by sparse Cholesky factorization while avoiding its most expensive components. We integrate our algorithm into vendor-maintained sparse Cholesky solvers on both CPUs and GPUs. Across a range of graphics applications, including single factorizations, repeated factorizations, our method reduces permutation time and improves the sparse Cholesky solve performance by up to 6.27x.
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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

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.
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