Tootfinder

Das Ding mit den Chlorhühnern ist doch sicher eine Kampagne der Vegi-Produkt-Firmen, um ihren Umsatz zu steigern. Ist es doch, oder?
https://mstdn.social/@watson_news/115200995896580127

Multi Anatomy X-Ray Foundation Model
Nishank Singla, Krisztian Koos, Farzin Haddadpour, Amin Honarmandi Shandiz, Lovish Chum, Xiaojian Xu, Qing Jin, Erhan Bas
https://arxiv.org/abs/2509.12146

Multi Anatomy X-Ray Foundation Model
X-ray imaging is a ubiquitous in radiology, yet most existing AI foundation models are limited to chest anatomy and fail to generalize across broader clinical tasks. In this work, we introduce XR-0, the multi-anatomy X-ray foundation model using self-supervised learning on a large, private dataset of 1.15 million images spanning diverse anatomical regions and evaluated across 12 datasets and 20 downstream tasks, including classification, retrieval, segmentation, localization, visual grounding, …

Honoring the Memory of Charlie Kirk (President Donald J. Trump/The White House)
https://www.whitehouse.gov/presidential-actions/2025/09/honoring-the-memory-of-charlie-kirk/
http://www.memeorandum.com/250911/p140#a250911p140

UALM: Unified Audio Language Model for Understanding, Generation and Reasoning
Jinchuan Tian, Sang-gil Lee, Zhifeng Kong, Sreyan Ghosh, Arushi Goel, Chao-Han Huck Yang, Wenliang Dai, Zihan Liu, Hanrong Ye, Shinji Watanabe, Mohammad Shoeybi, Bryan Catanzaro, Rafael Valle, Wei Ping
https://arxiv.org/abs/2510.12000

UALM: Unified Audio Language Model for Understanding, Generation and Reasoning
Recent advances in the audio language modeling (ALM) domain tackle audio understanding and text-to-audio generation as separate tasks. Very few studies attempt to unify these tasks -- an essential step toward advanced multimodal reasoning. This paper introduces U}nified Audio Language Model (UALM), which aims to unify audio understanding, text-to-audio generation, and multimodal reasoning in a single model. To achieve this goal, we first present UALM-Gen, a text-to-audio language model that dir…

QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
Wei Huang, Yi Ge, Shuai Yang, Yicheng Xiao, Huizi Mao, Yujun Lin, Hanrong Ye, Sifei Liu, Ka Chun Cheung, Hongxu Yin, Yao Lu, Xiaojuan Qi, Song Han, Yukang Chen
https://arxiv.org/abs/2510.11696

QeRL: Beyond Efficiency -- Quantization-enhanced Reinforcement Learning for LLMs
We propose QeRL, a Quantization-enhanced Reinforcement Learning framework for large language models (LLMs). While RL is essential for LLMs' reasoning capabilities, it is resource-intensive, requiring substantial GPU memory and long rollout durations. QeRL addresses these issues by combining NVFP4 quantization with Low-Rank Adaptation (LoRA), accelerating rollout phase of RL while reducing memory overhead. Beyond efficiency, our findings show that quantization noise increases policy entropy, enh…

AI Darwin Awards
Honouring Those Who Asked "Can We?" Without Ever Asking "Should We?"
Named after Charles Darwin's theory of natural selection, the original Darwin Awards celebrated those who "improved the gene pool by removing themselves from it" through spectacularly stupid acts. Well, guess what? Humans have evolved! We're now so advanced that we've outsourced our poor decision-making to machines. […]
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AI Darwin Awards 2025 - Celebrating Spectacularly Bad AI Decisions
Honouring those who use artificial intelligence in spectacularly reckless ways. Submit nominations for the most catastrophically ill-advised AI deployments of 2025.

Ethical Medical Image Synthesis
Weina Jin, Ashish Sinha, Kumar Abhishek, Ghassan Hamarneh
https://arxiv.org/abs/2508.09293 https://arxiv.org/pdf/2508.09293…

Ethical Medical Image Synthesis
The task of ethical Medical Image Synthesis (MISyn) is to ensure that the MISyn techniques are researched and developed ethically throughout their entire lifecycle, which is essential to prevent the negative impacts of MISyn. To address the ever-increasing needs and requirements for ethical practice of MISyn research and development, we first conduct a theoretical analysis that identifies the key properties of ethical MISyn and intrinsic limits of MISyn. We identify that synthetic images lack i…

Hammering Higher Order Set Theory
Chad E. Brown, Cezary Kaliszyk, Martin Suda, Josef Urban
https://arxiv.org/abs/2509.08264 https://arxiv.org/pdf/2509.0826…

Hammering Higher Order Set Theory
We use automated theorem provers to significantly shorten a formal development in higher order set theory. The development includes many standard theorems such as the fundamental theorem of arithmetic and irrationality of square root of two. Higher order automated theorem provers are particularly useful here, since the underlying framework of higher order set theory coincides with the classical extensional higher order logic of (most) higher order automated theorem provers, so no significant tr…

🇺🇦 #NowPlaying on BBCRadio3's #ThroughTheNight
Currende, Claire Lefilliatre, Han Warmelinck, Francesco Mancini, Marnix De Cat & Erik Van Nevel:
🎵 Missa Septimus
#Currende #ClaireLefilliatre #HanWarmelinck
https://open.spotify.com/track/7ulaaRQ8nUfKaxKGP5G9cQ

Making Power Explicable in AI: Analyzing, Understanding, and Redirecting Power to Operationalize Ethics in AI Technical Practice
Weina Jin, Elise Li Zheng, Ghassan Hamarneh
https://arxiv.org/abs/2510.10588

Making Power Explicable in AI: Analyzing, Understanding, and Redirecting Power to Operationalize Ethics in AI Technical Practice
The operationalization of ethics in the technical practices of artificial intelligence (AI) is facing significant challenges. To address the problem of ineffective implementation of AI ethics, we present our diagnosis, analysis, and interventional recommendations from a unique perspective of the real-world implementation of AI ethics through explainable AI (XAI) techniques. We first describe the phenomenon (i.e., the "symptoms") of ineffective implementation of AI ethics in explainable AI using…