Tootfinder

Light Streaks — generative photography
A small selection of long-exposures of randomly generated IFS (aka Iterated Function Systems), a family of very oldskool primitive/trivial fractal functions, but which can produce a fairly wide variety of outcomes. Each image is the result of billions of iterations of a single particle being iteratively transformed (meaning the particle's current position is used as the input for computing its next position etc.) For each iteration & posit…

Week 16 NFL odds, lines, best bets, predictions: Computer model backing Ravens and Chiefs

https://www.cbssports.com/nfl/news/week-16-nfl-odds-lines-best-bets-predictions/

Estimating Spatially Resolved Radiation Fields Using Neural Networks
Felix Lehner, Pasquale Lombardo, Susana Castillo, Oliver Hupe, Marcus Magnor
https://arxiv.org/abs/2512.17654 https://arxiv.org/pdf/2512.17654 https://arxiv.org/html/2512.17654
arXiv:2512.17654v1 Announce Type: new
Abstract: We present an in-depth analysis on how to build and train neural networks to estimate the spatial distribution of scattered radiation fields for radiation protection dosimetry in medical radiation fields, such as those found in Interventional Radiology and Cardiology. Therefore, we present three different synthetically generated datasets with increasing complexity for training, using a Monte-Carlo Simulation application based on Geant4. On those datasets, we evaluate convolutional and fully connected architectures of neural networks to demonstrate which design decisions work well for reconstructing the fluence and spectra distributions over the spatial domain of such radiation fields. All used datasets as well as our training pipeline are published as open source in separate repositories.
toXiv_bot_toot

Replaced article(s) found for cs.GR. https://arxiv.org/list/cs.GR/new
[1/1]:
- Controllable Video Generation: A Survey
Yue Ma, et al.
https://arxiv.org/abs/2507.16869 https://mastoxiv.page/@arXiv_csGR_bot/114907178598354130
- Lightning Fast Caching-based Parallel Denoising Prediction for Accelerating Talking Head Generation
Jianzhi Long, Wenhao Sun, Rongcheng Tu, Dacheng Tao
https://arxiv.org/abs/2509.00052 https://mastoxiv.page/@arXiv_csGR_bot/115139250819269869
- MimicKit: A Reinforcement Learning Framework for Motion Imitation and Control
Xue Bin Peng
https://arxiv.org/abs/2510.13794 https://mastoxiv.page/@arXiv_csGR_bot/115382726856686148
- TIDI-GS: Floater Suppression in 3D Gaussian Splatting for Enhanced Indoor Scene Fidelity
Sooyeun Yang, Cheyul Im, Jee Won Lee, Jongseong Brad Choi
https://arxiv.org/abs/2601.09291 https://mastoxiv.page/@arXiv_csGR_bot/115898204587831863
- Eye-tracked Virtual Reality: A Comprehensive Survey on Methods and Privacy Challenges
Bozkir, S\Ozdel, Wang, David-John, Gao, Butler, Jain, Kasneci
https://arxiv.org/abs/2305.14080
- Hi5: Synthetic Data for Inclusive, Robust, Hand Pose Estimation
Hasan, Ozel, Long, Martin, Potter, Adnan, Lee, Hoque
https://arxiv.org/abs/2406.03599 https://mastoxiv.page/@arXiv_csCV_bot/112573997027314918
- A Text-to-3D Framework for Joint Generation of CG-Ready Humans and Compatible Garments
Zhiyao Sun, Yu-Hui Wen, Ho-Jui Fang, Sheng Ye, Matthieu Lin, Tian Lv, Yong-Jin Liu
https://arxiv.org/abs/2503.12052 https://mastoxiv.page/@arXiv_csCV_bot/114182219370820263
- A Unified Architecture for N-Dimensional Visualization and Simulation: 4D Implementation and Eval...
Hirohito Arai
https://arxiv.org/abs/2512.01501 https://mastoxiv.page/@arXiv_csCG_bot/115648840470000746
toXiv_bot_toot

Just kinda thinking out loud here, don't have a solution or architecture in mind: I really want a better solution for high speed FPGA / ASIC protocol design and debug.
In particular:
1) Using scopehal protocol captures as stimuli for a virtual DUT (i.e. decoded 8b10b of PCIe or something into RTL simulator, pretending to be a SERDES)

😅 Chasing a winning streak: A new way to trigger responses in the body by simulating psychological pressure
https://medicalxpress.com/news/2025-11-streak-trigger-responses-body-simulating.html

Chasing a winning streak: A new way to trigger responses in the body by simulating psychological pressure
Researchers at the University of Tokyo have developed an experimental method to induce a strong physiological response linked to psychological pressure by making participants aim for a streak of success in a task.

Runway launches GWM-1, its first world model, which uses frame-by-frame prediction to simulate physics, and updates Gen 4.5 to add native audio and more (TechCrunch)
https://techcrunch.com/2025/12/11/runway-releases-its-first-w…

Runway releases its first world model, adds native audio to latest video model | TechCrunch
Runway debuts a physics-aware world model that simulates reality to train agents and power video, robotics and avatar applications.

Calibratable Disambiguation Loss for Multi-Instance Partial-Label Learning
Wei Tang, Yin-Fang Yang, Weijia Zhang, Min-Ling Zhang
https://arxiv.org/abs/2512.17788 https://arxiv.org/pdf/2512.17788 https://arxiv.org/html/2512.17788
arXiv:2512.17788v1 Announce Type: new
Abstract: Multi-instance partial-label learning (MIPL) is a weakly supervised framework that extends the principles of multi-instance learning (MIL) and partial-label learning (PLL) to address the challenges of inexact supervision in both instance and label spaces. However, existing MIPL approaches often suffer from poor calibration, undermining classifier reliability. In this work, we propose a plug-and-play calibratable disambiguation loss (CDL) that simultaneously improves classification accuracy and calibration performance. The loss has two instantiations: the first one calibrates predictions based on probabilities from the candidate label set, while the second one integrates probabilities from both candidate and non-candidate label sets. The proposed CDL can be seamlessly incorporated into existing MIPL and PLL frameworks. We provide a theoretical analysis that establishes the lower bound and regularization properties of CDL, demonstrating its superiority over conventional disambiguation losses. Experimental results on benchmark and real-world datasets confirm that our CDL significantly enhances both classification and calibration performance.
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Bills vs. Broncos prediction, odds, time: NFL divisional round Saturday picks from 10,000 simulations

https://www.cbssports.com/nfl/news/bills-broncos-predicti…

Eagles vs. 49ers prediction, odds, start time: NFL Wild Card Sunday picks from 10,000 simulations

https://www.cbssports.com/nfl/news/eagles-49ers-prediction-odds-time-nf…