Tootfinder

Micah Parsons takes a subtle shot at Jerry Jones and Dallas Cowboys after Packers' win against Commanders https://bolavip.com/en/nfl/nfl-news-micah-parsons-takes-a-subtle-shot-at-jerry-jones-and-cowboys-after…

Forscher warnen vor visuellen Angriffen in Augmented Reality
Virtuelle Schilder, falsche Wege: Eine AR-Demo zeigt, wie gefährlich subtile Manipulation in Augmented Reality werden kann.
https://www.

Exclusive: Justice Department Erases Domestic Terror Unit (SpyTalk)
https://www.spytalk.co/p/exclusive-justice-department-erases
http://www.memeorandum.com/250814/p142#a250814p142

SpaceVista: All-Scale Visual Spatial Reasoning from mm to km
Peiwen Sun, Shiqiang Lang, Dongming Wu, Yi Ding, Kaituo Feng, Huadai Liu, Zhen Ye, Rui Liu, Yun-Hui Liu, Jianan Wang, Xiangyu Yue
https://arxiv.org/abs/2510.09606

SpaceVista: All-Scale Visual Spatial Reasoning from mm to km
With the current surge in spatial reasoning explorations, researchers have made significant progress in understanding indoor scenes, but still struggle with diverse applications such as robotics and autonomous driving. This paper aims to advance all-scale spatial reasoning across diverse scenarios by tackling two key challenges: 1) the heavy reliance on indoor 3D scans and labor-intensive manual annotations for dataset curation; 2) the absence of effective all-scale scene modeling, which often …

MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations
Wenxiang Guo, Changhao Pan, Zhiyuan Zhu, Xintong Hu, Yu Zhang, Li Tang, Rui Yang, Han Wang, Zongbao Zhang, Yuhan Wang, Yixuan Chen, Hankun Xu, Ke Xu, Pengfei Fan, Zhetao Chen, Yanhao Yu, Qiange Huang, Fei Wu, Zhou Zhao
https://arxiv.org/abs/2510.1039…

MRSAudio: A Large-Scale Multimodal Recorded Spatial Audio Dataset with Refined Annotations
Humans rely on multisensory integration to perceive spatial environments, where auditory cues enable sound source localization in three-dimensional space. Despite the critical role of spatial audio in immersive technologies such as VR/AR, most existing multimodal datasets provide only monaural audio, which limits the development of spatial audio generation and understanding. To address these challenges, we introduce MRSAudio, a large-scale multimodal spatial audio dataset designed to advance re…

Incoherent Imaging with Spatially Structured Quantum Probes
Anthony J. Brady, Zihao Gong, Alexey V. Gorshkov, Saikat Guha
https://arxiv.org/abs/2510.09521 https://

Incoherent Imaging with Spatially Structured Quantum Probes
Incoherent imaging, including fluorescence and absorption microscopy, is often limited by weak signals and resolution constraints -- notoriously, Rayleigh's curse. We investigate how spatially structured quantum probes, combined with quantum detection strategies like spatial mode demultiplexing and photon counting, overcome these limitations. We propose a novel imaging protocol based on twin-beam echoes that maps the generalized incoherent-imaging model -- comprising both absorption and fluores…

Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
Yuko Kakikawa, Yoshiyuki Ninomiya
https://arxiv.org/abs/2510.11172 https:/…

Developing an information criterion for spatial data analysis through Bayesian generalized fused lasso
In the field of spatial data analysis, spatially varying coefficients (SVC) models, which allow regression coefficients to vary by region and flexibly capture spatial heterogeneity, have continued to be developed in various directions. Moreover, the Bayesian generalized fused lasso is often used as a method that efficiently provides estimation under the natural assumption that regression coefficients of adjacent regions tend to take the same value. In most Bayesian methods, the selection of pri…

Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
Shiyuan Luo, Runlong Yu, Shengyu Chen, Yingda Fan, Yiqun Xie, Yanhua Li, Xiaowei Jia
https://arxiv.org/abs/2510.09500

Geo-Aware Models for Stream Temperature Prediction across Different Spatial Regions and Scales
Understanding environmental ecosystems is vital for the sustainable management of our planet. However,existing physics-based and data-driven models often fail to generalize to varying spatial regions and scales due to the inherent data heterogeneity presented in real environmental ecosystems. This generalization issue is further exacerbated by the limited observation samples available for model training. To address these issues, we propose Geo-STARS, a geo-aware spatio-temporal modeling framewo…

LSZone: A Lightweight Spatial Information Modeling Architecture for Real-time In-car Multi-zone Speech Separation
Jun Chen, Shichao Hu, Jiuxin Lin, Wenjie Li, Zihan Zhang, Xingchen Li, JinJiang Liu, Longshuai Xiao, Chao Weng, Lei Xie, Zhiyong Wu
https://arxiv.org/abs/2510.10687

LSZone: A Lightweight Spatial Information Modeling Architecture for Real-time In-car Multi-zone Speech Separation
In-car multi-zone speech separation, which captures voices from different speech zones, plays a crucial role in human-vehicle interaction. Although previous SpatialNet has achieved notable results, its high computational cost still hinders real-time applications in vehicles. To this end, this paper proposes LSZone, a lightweight spatial information modeling architecture for real-time in-car multi-zone speech separation. We design a spatial information extraction-compression (SpaIEC) module that…

SpatialVID: A Large-Scale Video Dataset with Spatial Annotations
Jiahao Wang, Yufeng Yuan, Rujie Zheng, Youtian Lin, Jian Gao, Lin-Zhuo Chen, Yajie Bao, Yi Zhang, Chang Zeng, Yanxi Zhou, Xiaoxiao Long, Hao Zhu, Zhaoxiang Zhang, Xun Cao, Yao Yao
https://arxiv.org/abs/2509.09676

SpatialVID: A Large-Scale Video Dataset with Spatial Annotations
Significant progress has been made in spatial intelligence, spanning both spatial reconstruction and world exploration. However, the scalability and real-world fidelity of current models remain severely constrained by the scarcity of large-scale, high-quality training data. While several datasets provide camera pose information, they are typically limited in scale, diversity, and annotation richness, particularly for real-world dynamic scenes with ground-truth camera motion. To this end, we col…