Tootfinder

Artificial intelligence for simplified patient-centered dosimetry in radiopharmaceutical therapies
Alejandro Lopez-Montes, Fereshteh Yousefirizi, Yizhou Chen, Yazdan Salimi, Robert Seifert, Ali Afshar-Oromieh, Carlos Uribe, Axel Rominger, Habib Zaidi, Arman Rahmim, Kuangyu Shi
https://arxiv.org/abs/2510.12714…

Artificial intelligence for simplified patient-centered dosimetry in radiopharmaceutical therapies
KEY WORDS: Artificial Intelligence (AI), Theranostics, Dosimetry, Radiopharmaceutical Therapy (RPT), Patient-friendly dosimetry KEY POINTS - The rapid evolution of radiopharmaceutical therapy (RPT) highlights the growing need for personalized and patient-centered dosimetry. - Artificial Intelligence (AI) offers solutions to the key limitations in current dosimetry calculations. - The main advances on AI for simplified dosimetry toward patient-friendly RPT are reviewed. - Future directions on th…

An interview with Evan Prodromou, "inventor of the Fediverse", on ActivityPub's origins, Mastodon's privacy focus, expanding beyond microblogging, and more (Matthias Pfefferle/Open Channels FM)
https://openchannels.fm/how-decentrali

How Decentralized Social Platforms Grew from Identica to Modern-Day Mastodon
Join Matthias Pfefferle and Evan Prodromou as they explore the evolution of decentralized social networks and the future of the Fediverse.

'Dissent becomes evil': Eric Trump’s claim father is guided by god slammed by experts - Alternet.org
https://www.alternet.org/news-politics/eric-trump-god/

Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
Yuhao Pan, Xiucheng Wang, Nan Cheng, Wenchao Xu
https://arxiv.org/abs/2510.09405

Cross-Receiver Generalization for RF Fingerprint Identification via Feature Disentanglement and Adversarial Training
Radio frequency fingerprint identification (RFFI) is a critical technique for wireless network security, leveraging intrinsic hardware-level imperfections introduced during device manufacturing to enable precise transmitter identification. While deep neural networks have shown remarkable capability in extracting discriminative features, their real-world deployment is hindered by receiver-induced variability. In practice, RF fingerprint signals comprise transmitter-specific features as well as c…

Mastodon, the decentralized, open social network with over 8 million accounts and nearly 700,000 monthly active users,
is planning to introduce a new feature, called “Packs,”
which will make it easier for newcomers to discover curated collections of users to follow.
The idea is similar to a feature Bluesky, another open social platform, introduced last year.
Starter packs are designed to address the “cold start” problem of joining new social networks.
When you …

Mastodon is taking cues from Bluesky with plans for its own starter 'Packs' | TechCrunch
Mastodon is planning to make it easier for newcomers to discover curated collections of users to follow by launching a new starter packs feature.

A Morphology-Adaptive Random Feature Method for Inverse Source Problem of the Helmholtz Equation
Xinwei Hu, Jingrun Chen, Haijun Yu
https://arxiv.org/abs/2510.09213 https://

A Morphology-Adaptive Random Feature Method for Inverse Source Problem of the Helmholtz Equation
The inverse source problem for the Helmholtz equation poses significant challenges, particularly when sources exhibit complex or discontinuous geometries. Traditional numerical methods suffer from prohibitive computational costs, while machine learning-based approaches such as Physics-Informed Neural Networks (PINNs) and the Random Feature Method (RFM) -- though computationally efficient for inverse problems -- lack the intrinsic machinery to handle the sharp morphological features in such sing…

Sharing to save it for future reference. #SFF #Magic
https://reactormag.com/magic-doesnt-have-to…

Magic Doesn't Have to Make Sense - Reactor
In praise of fantasy that embraces rebellious, lawless, and delightfully un-rulebound magic.

Soft Disentanglement in Frequency Bands for Neural Audio Codecs
Benoit Ginies, Xiaoyu Bie, Olivier Fercoq, Ga\"el Richard
https://arxiv.org/abs/2510.03735 https://

Soft Disentanglement in Frequency Bands for Neural Audio Codecs
In neural-based audio feature extraction, ensuring that representations capture disentangled information is crucial for model interpretability. However, existing disentanglement methods often rely on assumptions that are highly dependent on data characteristics or specific tasks. In this work, we introduce a generalizable approach for learning disentangled features within a neural architecture. Our method applies spectral decomposition to time-domain signals, followed by a multi-branch audio co…

TracE2E: Easily Deployable Middleware for Decentralized Data Traceability
Daniel Pressens\'e, Elisavet Kozyri
https://arxiv.org/abs/2510.08225 https://…

TracE2E: Easily Deployable Middleware for Decentralized Data Traceability
This paper presents TracE2E, a middleware written in Rust, that can provide both data explainability and compliance across multiple nodes. By mediating inputs and outputs of processes, TracE2E records provenance information and enforces data-protection policies (e.g., confidentiality, integrity) that depend on the recorded provenance. Unlike existing approaches that necessitate substantial application modifications, TracE2E is designed for easy integration into existing and future applications …

SpotDiff: Spotting and Disentangling Interference in Feature Space for Subject-Preserving Image Generation
Yongzhi Li, Saining Zhang, Yibing Chen, Boying Li, Yanxin Zhang, Xiaoyu Du
https://arxiv.org/abs/2510.07340

SpotDiff: Spotting and Disentangling Interference in Feature Space for Subject-Preserving Image Generation
Personalized image generation aims to faithfully preserve a reference subject's identity while adapting to diverse text prompts. Existing optimization-based methods ensure high fidelity but are computationally expensive, while learning-based approaches offer efficiency at the cost of entangled representations influenced by nuisance factors. We introduce SpotDiff, a novel learning-based method that extracts subject-specific features by spotting and disentangling interference. Leveraging a pre-tr…