Tootfinder

YES!
GDP growth is effectively dead.The motor is broken.
Now it's time to face reality and share the wealth that is so evidently present.
An economy for life!
#degrowth
Blow to Chancellor as UK economy shrank unexpectedly in May

Blow to Chancellor as UK economy shrank unexpectedly in May
The UK economy contracted unexpectedly in May, marking the second month in a row of falling output and heaping further pressure on Chancellor Rachel Reeves. The Office for National Statistics (ONS) said gross domestic product (GDP) contracted by 0.1% in May, following a 0.3% drop in April. Most economists had expected the economy to rebound […]

⭐ Researchers uncover surprising limit on human imagination
#brain

Researchers uncover surprising limit on human imagination
Human beings can juggle up to 10 balls at once. But how many can they move through the air with their imaginations?

'Unacceptable' — Ukraine denounces Israeli strike in Qatar: https://benborges.xyz/2025/09/10/unacceptable-ukraine-denounces-israeli-strike.html

Treasury posts unexpected surplus in June as tariff receipts surge (Jeff Cox/CNBC)
https://www.cnbc.com/2025/07/11/treasury-posts-unexpected-surplus-in-june-as-tariff-receipts-surge.html
http://www.memeorandum.com/250711/p92#a250711p92

Dynamic Scene 3D Reconstruction of an Uncooperative Resident Space Object
Bala Prenith Reddy Gopu, Timothy Jacob Huber, George M. Nehma, Patrick Quinn, Madhur Tiwari, Matt Ueckermann, David Hinckley, Christopher McKenna
https://arxiv.org/abs/2509.07932

Dynamic Scene 3D Reconstruction of an Uncooperative Resident Space Object
Characterization of uncooperative Resident Space Objects (RSO) play a crucial role in On-Orbit Servicing (OOS) and Active Debris Removal (ADR) missions to assess the geometry and motion properties. To address the challenges of reconstructing tumbling uncooperative targets, this study evaluates the performance of existing state-of-the-art 3D reconstruction algorithms for dynamic scenes, focusing on their ability to generate geometrically accurate models with high-fidelity. To support our evaluat…

Uncovering Scaling Laws for Large Language Models via Inverse Problems
Arun Verma, Zhaoxuan Wu, Zijian Zhou, Xiaoqiang Lin, Zhiliang Chen, Rachael Hwee Ling Sim, Rui Qiao, Jingtan Wang, Nhung Bui, Xinyuan Niu, Wenyang Hu, Gregory Kang Ruey Lau, Zi-Yu Khoo, Zitong Zhao, Xinyi Xu, Apivich Hemachandra, See-Kiong Ng, Bryan Kian Hsiang Low
https://

Uncovering Scaling Laws for Large Language Models via Inverse Problems
Large Language Models (LLMs) are large-scale pretrained models that have achieved remarkable success across diverse domains. These successes have been driven by unprecedented complexity and scale in both data and computations. However, due to the high costs of training such models, brute-force trial-and-error approaches to improve LLMs are not feasible. Inspired by the success of inverse problems in uncovering fundamental scientific laws, this position paper advocates that inverse problems can …

Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals
Cheng Chen, Haiyan Yin, Ivor Tsang
https://arxiv.org/abs/2509.08809

Evaluating LLMs Without Oracle Feedback: Agentic Annotation Evaluation Through Unsupervised Consistency Signals
Large Language Models (LLMs), when paired with prompt-based tasks, have significantly reduced data annotation costs and reliance on human annotators. However, evaluating the quality of their annotations remains challenging in dynamic, unsupervised environments where oracle feedback is scarce and conventional methods fail. To address this challenge, we propose a novel agentic annotation paradigm, where a student model collaborates with a noisy teacher (the LLM) to assess and refine annotation qu…

FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
Sara Behnamian, Rasoul Khaksarinezhad, Andreas Langer
https://arxiv.org/abs/2509.08670

FractalPINN-Flow: A Fractal-Inspired Network for Unsupervised Optical Flow Estimation with Total Variation Regularization
We present FractalPINN-Flow, an unsupervised deep learning framework for dense optical flow estimation that learns directly from consecutive grayscale frames without requiring ground truth. The architecture centers on the Fractal Deformation Network (FDN) - a recursive encoder-decoder inspired by fractal geometry and self-similarity. Unlike traditional CNNs with sequential downsampling, FDN uses repeated encoder-decoder nesting with skip connections to capture both fine-grained details and long…

'Unacceptable': MSNBC Analyst Michael Dowd Fired After Blaming Charlie Kirk for His Own Murder (Matthew Xiao/The Washington Free Beacon)
https://freebeacon.com/latest-news/unacceptable-msnbc-analyst-michael-dowd-fired-after-blaming-charlie-kirk-for-his-own-murder/
http://www.memeorandum.com/250911/p126#a250911p126

TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation
Mattia Litrico, Mario Valerio Giuffrida, Sebastiano Battiato, Devis Tuia
https://arxiv.org/abs/2508.06452 ht…

TRUST: Leveraging Text Robustness for Unsupervised Domain Adaptation
Recent unsupervised domain adaptation (UDA) methods have shown great success in addressing classical domain shifts (e.g., synthetic-to-real), but they still suffer under complex shifts (e.g. geographical shift), where both the background and object appearances differ significantly across domains. Prior works showed that the language modality can help in the adaptation process, exhibiting more robustness to such complex shifts. In this paper, we introduce TRUST, a novel UDA approach that exploit…