Tootfinder

Five crucial Earth systems near a tipping point: Report https://news.mongabay.com/short-article/2025/10/five-crucial-earth-systems-near-a-tipping-point-report/

Five crucial Earth systems near a tipping point: Report
Five of Earth’s vital systems are close to a point of irreversible change, warns a new report released by a global network of scientists ahead of the upcoming U.N. climate change conference in Brazil. The 2025 Global Tipping Points report updates a 2023 report to assess 25 Earth systems that human societies and economies depend […]

from my link log —
Pnut: a C to POSIX shell compiler.
https://pnut.sh/
saved 2025-09-09 https://dotat.at/:/CPX64.html

paint() your data when you print them to make them easier to grasp: #rstats #console

Invariant Generation for Floating-Point Programs via Constraint Solving
Xuran Cai, Liqian Chen, Hongfei Fu
https://arxiv.org/abs/2507.15017 https://…

Invariant Generation for Floating-Point Programs via Constraint Solving
In numeric-intensive computations, it is well known that the execution of floating-point programs is imprecise as floating point arithmetics (e.g., addition, subtraction, multiplication, division, etc.) incurs rounding errors. Albeit the rounding error is small for every single floating-point operation, the aggregation of such error in multiple operations may be dramatic and cause catastrophic program failures. Therefore, to ensure the correctness of floating-point programs, the effect of float…

Graph-based Point Cloud Surface Reconstruction using B-Splines
Stuti Pathak, Rhys G. Evans, Gunther Steenackers, Rudi Penne
https://arxiv.org/abs/2509.16050 https://

Graph-based Point Cloud Surface Reconstruction using B-Splines
Generating continuous surfaces from discrete point cloud data is a fundamental task in several 3D vision applications. Real-world point clouds are inherently noisy due to various technical and environmental factors. Existing data-driven surface reconstruction algorithms rely heavily on ground truth normals or compute approximate normals as an intermediate step. This dependency makes them extremely unreliable for noisy point cloud datasets, even if the availability of ground truth training data …

🐮 Bucolic battlefield: a California seashore says goodbye to dairy farms – and hopes to heal
https://www.theguardian.com/us-news/2025/sep/06/california-point-reyes-farmers-environment

Pond Slider turtles basking on the rocks in a pond. LA Arboretum, Arcadia, California, USA. December, 2024. #laarboretum #turtle #pondslider

Jets erase 20-point deficit in final minutes before falling to Buccaneers in latest gut-wrenching loss

https://www.cbssports.com/nfl/news/jets-er

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Peiming Li, Ziyi Wang, Yulin Yuan, Hong Liu, Xiangming Meng, Junsong Yuan, Mengyuan Liu
https://arxiv.org/abs/2508.14604

UST-SSM: Unified Spatio-Temporal State Space Models for Point Cloud Video Modeling
Point cloud videos capture dynamic 3D motion while reducing the effects of lighting and viewpoint variations, making them highly effective for recognizing subtle and continuous human actions. Although Selective State Space Models (SSMs) have shown good performance in sequence modeling with linear complexity, the spatio-temporal disorder of point cloud videos hinders their unidirectional modeling when directly unfolding the point cloud video into a 1D sequence through temporally sequential scann…

Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registration
Xingmei Wang, Xiaoyu Hu, Chengkai Huang, Ziyan Zeng, Guohao Nie, Quan Z. Sheng, Lina Yao
https://arxiv.org/abs/2509.15882

Self-Supervised Cross-Modal Learning for Image-to-Point Cloud Registration
Bridging 2D and 3D sensor modalities is critical for robust perception in autonomous systems. However, image-to-point cloud (I2P) registration remains challenging due to the semantic-geometric gap between texture-rich but depth-ambiguous images and sparse yet metrically precise point clouds, as well as the tendency of existing methods to converge to local optima. To overcome these limitations, we introduce CrossI2P, a self-supervised framework that unifies cross-modal learning and two-stage reg…