Tootfinder

🇺🇦 #NowPlaying on KEXP's #SonicReducer
Lambrini Girls:
🎵 Big Dick Energy
#LambriniGirls
https://lambrinigirlsband.bandcamp.com/track/big-dick-energy
https://open.spotify.com/track/77oKfNLyXNAWYPTmpCCFNT

Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
Wei Cui, Haoyu Wang, Wenkang Qin, Yijie Guo, Gang Han, Wen Zhao, Jiahang Cao, Zhang Zhang, Jiaru Zhong, Jingkai Sun, Pihai Sun, Shuai Shi, Botuo Jiang, Jiahao Ma, Jiaxu Wang, Hao Cheng, Zhichao Liu, Yang Wang, Zheng Zhu, Guan Huang, Jian Tang, Qiang Zhang
https:…

Humanoid Occupancy: Enabling A Generalized Multimodal Occupancy Perception System on Humanoid Robots
Humanoid robot technology is advancing rapidly, with manufacturers introducing diverse heterogeneous visual perception modules tailored to specific scenarios. Among various perception paradigms, occupancy-based representation has become widely recognized as particularly suitable for humanoid robots, as it provides both rich semantic and 3D geometric information essential for comprehensive environmental understanding. In this work, we present Humanoid Occupancy, a generalized multimodal occupanc…

GS-Occ3D: Scaling Vision-only Occupancy Reconstruction for Autonomous Driving with Gaussian Splatting
Baijun Ye, Minghui Qin, Saining Zhang, Moonjun Gong, Shaoting Zhu, Zebang Shen, Luan Zhang, Lu Zhang, Hao Zhao, Hang Zhao
https://arxiv.org/abs/2507.19451

GS-Occ3D: Scaling Vision-only Occupancy Reconstruction for Autonomous Driving with Gaussian Splatting
Occupancy is crucial for autonomous driving, providing essential geometric priors for perception and planning. However, existing methods predominantly rely on LiDAR-based occupancy annotations, which limits scalability and prevents leveraging vast amounts of potential crowdsourced data for auto-labeling. To address this, we propose GS-Occ3D, a scalable vision-only framework that directly reconstructs occupancy. Vision-only occupancy reconstruction poses significant challenges due to sparse view…

VRISE: A Virtual Reality Platfrom for Immersive and Interactive Surveying Education
Daniel Udekwe (Vivian), Dimitrios Bolkas (Vivian), Eren Erman Ozguven (Vivian), Ren Moses (Vivian), Qianwen (Vivian), Guo
https://arxiv.org/abs/2507.22810

VRISE: A Virtual Reality Platfrom for Immersive and Interactive Surveying Education
Surveying is a core component of civil engineering education, requiring students to engage in hands-on spatial measurement, instrumentation handling, and field-based decision-making. However, traditional instruction often poses logistical and cognitive challenges that can hinder accessibility and student engagement. While virtual laboratories have gained traction in engineering education, few are purposefully designed to support flexible, adaptive learning in surveying. To address this gap, we …

Complexity hierarchies in Euclidean stars
L. Herrera, A. Di Prisco, J. Ospino
https://arxiv.org/abs/2509.26384 https://arxiv.org/pdf/2509.26384

Complexity hierarchies in Euclidean stars
We establish a hierarchy of Euclidean stars according to their degree of complexity, as measured by the complexity factor and the complexity of the pattern of evolution. We consider both, nondissipative and dissipative systems. Solutions are ranged from the simplest one, in order of increasing complexity. Some specific models are found and analyzed in detail.

Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts
Fabian Raisch, Max Langtry, Felix Koch, Ruchi Choudhary, Christoph Goebel, Benjamin Tischler
https://arxiv.org/abs/2508.21615

Adapting to Change: A Comparison of Continual and Transfer Learning for Modeling Building Thermal Dynamics under Concept Drifts
Transfer Learning (TL) is currently the most effective approach for modeling building thermal dynamics when only limited data are available. TL uses a pretrained model that is fine-tuned to a specific target building. However, it remains unclear how to proceed after initial fine-tuning, as more operational measurement data are collected over time. This challenge becomes even more complex when the dynamics of the building change, for example, after a retrofit or a change in occupancy. In Machine…

Network-Optimised Spiking Neural Network for Event-Driven Networking
Muhammad Bilal
https://arxiv.org/abs/2509.23516 https://arxiv.org/pdf/2509.23516

Network-Optimised Spiking Neural Network for Event-Driven Networking
Spiking neural networks offer event-driven computation suited to time-critical networking tasks such as anomaly detection, local routing control, and congestion management at the edge. Classical units, including Hodgkin-Huxley, Izhikevich, and the Random Neural Network, map poorly to these needs. We introduce Network-Optimised Spiking (NOS), a compact two-variable unit whose state encodes normalised queue occupancy and a recovery resource. The model uses a saturating nonlinearity to enforce fin…

🇺🇦 #NowPlaying on KEXP's #Early
NoSo:
🎵 Nara
#NoSo
https://noso.bandcamp.com/track/nara
https://open.spotify.com/track/7o24443ZBPnM3GD8jfbI1O

Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC
Martino Errico (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Davide Fiacco (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Stefano Giagu (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Giuliano Gustavino (Sapienza Universit\`a di Roma, INFN Sezione di Roma), Valerio Ippolito (INFN Sezione di Roma), Graziella Russo (Sapienza Universit\`a di Roma, INFN Sezione di Roma)

Graph Neural Network Acceleration on FPGAs for Fast Inference in Future Muon Triggers at HL-LHC
The High-Luminosity LHC (HL-LHC) will reach luminosities up to 7 times higher than the previous run, yielding denser events and larger occupancies. Next generation trigger algorithms must retain reliable selection within a strict latency budget. This work explores machine-learning approaches for future muon triggers, using the ATLAS Muon Spectrometer as a benchmark. A Convolutional Neural Network (CNN) is used as a reference, while a Graph Neural Network (GNN) is introduced as a natural model f…

Public defender’s office seeks removal of Trump’s top federal prosecutor in L.A.
https://www.latimes.com/california/story/2025-08-29/public-defender-moves-to-disqualify-bill-essayli

Public defender's office seeks to remove L.A.'s top federal prosecutor
In a filing Friday, the federal public defender's office in Los Angeles argued acting U.S. Atty. Bill Essayli should be removed because he is unlawfully occupying his post.