Tootfinder

Point Prompting: Counterfactual Tracking with Video Diffusion Models
Ayush Shrivastava, Sanyam Mehta, Daniel Geng, Andrew Owens
https://arxiv.org/abs/2510.11715 https://

Point Prompting: Counterfactual Tracking with Video Diffusion Models
Trackers and video generators solve closely related problems: the former analyze motion, while the latter synthesize it. We show that this connection enables pretrained video diffusion models to perform zero-shot point tracking by simply prompting them to visually mark points as they move over time. We place a distinctively colored marker at the query point, then regenerate the rest of the video from an intermediate noise level. This propagates the marker across frames, tracing the point's traj…

2026 NFL mock draft: Jets, Dolphins make first-round quarterback changes as Saints punt on the position

https://www.cbssports.com/nfl/draft/news/2…

A Mathematics-Guided Approach to Floating-Point Error Detection
Youshuai Tan, Zhanwei Zhang, Zishuo Ding, Lianyu Zheng, Jinfu Chen, Weiyi Shang
https://arxiv.org/abs/2510.10081 …

A Mathematics-Guided Approach to Floating-Point Error Detection
Floating-point program errors can lead to severe consequences, particularly in critical domains such as military applications. Only a small subset of inputs may induce substantial floating-point errors, prompting researchers to develop methods for identifying these error-inducing inputs. Although existing approaches have achieved some success, they still suffer from two major limitations: (1) High computational cost: The evaluation of error magnitude for candidate inputs relies on high-precisio…

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Morris Trestman, Stefan Gugler, Felix A. Faber, O. A. von Lilienfeld
https://arxiv.org/abs/2510.08906 h…

Gradient-Guided Furthest Point Sampling for Robust Training Set Selection
Smart training set selections procedures enable the reduction of data needs and improves predictive robustness in machine learning problems relevant to chemistry. We introduce Gradient Guided Furthest Point Sampling (GGFPS), a simple extension of Furthest Point Sampling (FPS) that leverages molecular force norms to guide efficient sampling of configurational spaces of molecules. Numerical evidence is presented for a toy-system (Styblinski-Tang function) as well as for molecular dynamics traject…

Semi-parametric Markov models for multi-type point patterns
Ib Thorsgaard Jensen, Jean-Fran\c{c}ois Coeurjolly, Rasmus Waagepetersen
https://arxiv.org/abs/2510.11226 https://

Semi-parametric Markov models for multi-type point patterns
Multi-type Markov point processes offer a flexible framework for modelling complex multi-type point patterns where it is pertinent to capture both interactions between points as well as large scale trends depending on observed covariates. However, estimation of interaction and covariate effects may be seriously biased in the presence of unobserved spatial confounders. In this paper we introduce a new class of semi-parametric Markov point processes that adjusts for spatial confounding through a …

NFL Week 2 projected scores: Model predicts point spreads and totals for every game https://www.nytimes.com/athletic/6620906/2025/09/11/nfl-week-2-projected-scores-model-point-spreads-totals/

SNAP: Towards Segmenting Anything in Any Point Cloud
Aniket Gupta, Hanhui Wang, Charles Saunders, Aruni RoyChowdhury, Hanumant Singh, Huaizu Jiang
https://arxiv.org/abs/2510.11565

SNAP: Towards Segmenting Anything in Any Point Cloud
Interactive 3D point cloud segmentation enables efficient annotation of complex 3D scenes through user-guided prompts. However, current approaches are typically restricted in scope to a single domain (indoor or outdoor), and to a single form of user interaction (either spatial clicks or textual prompts). Moreover, training on multiple datasets often leads to negative transfer, resulting in domain-specific tools that lack generalizability. To address these limitations, we present \textbf{SNAP} (…

Minkowski-MambaNet: A Point Cloud Framework with Selective State Space Models for Forest Biomass Quantification
Jinxiang Tu, Dayong Ren, Fei Shi, Zhenhong Jia, Yahong Ren, Jiwei Qin, Fang He
https://arxiv.org/abs/2510.09367

Minkowski-MambaNet: A Point Cloud Framework with Selective State Space Models for Forest Biomass Quantification
Accurate forest biomass quantification is vital for carbon cycle monitoring. While airborne LiDAR excels at capturing 3D forest structure, directly estimating woody volume and Aboveground Biomass (AGB) from point clouds is challenging due to difficulties in modeling long-range dependencies needed to distinguish trees.We propose Minkowski-MambaNet, a novel deep learning framework that directly estimates volume and AGB from raw LiDAR. Its key innovation is integrating the Mamba model's Selective …