Tootfinder

We in Game Workers Unite Ireland stand in solidarity with Animation Workers of Ireland, and the international trade union coalition happening to organise against the threat of AI in the animation industry, and we support their call to acton.
Join them Thursday 12 June at 14:00 at the Pâquier in Annecy!!
#Animation

@merlelaperle.bsky.social@bsky.brid.gy hallo Merle, ich habe dir jetzt seit einigen Tagen hier im fediverse zugehört. War skeptisch ob deinem engagierten Fürsprechen für Threema - und kann dir trotzdem inhaltlich oft folgen, aber … die Empfehlung jemand möge an einem Galgen hängen, finde ich, für die Wahl seines persönlichen Messengrs zu überspitzt. Mögen seine oder deine Argumente stimmen, wird damit egal. Schade.

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
Hao Wu, Haijun Wang, Shangwang Li, Yin Wu, Ming Fan, Yitao Zhao, Ting Liu
https://arxiv.org/abs/2506.08561

Detecting State Manipulation Vulnerabilities in Smart Contracts Using LLM and Static Analysis
An increasing number of DeFi protocols are gaining popularity, facilitating transactions among multiple anonymous users. State Manipulation is one of the notorious attacks in DeFi smart contracts, with price variable being the most commonly exploited state variable-attackers manipulate token prices to gain illicit profits. In this paper, we propose PriceSleuth, a novel method that leverages the Large Language Model (LLM) and static analysis to detect Price Manipulation (PM) attacks proactively.…

Factorized RVQ-GAN For Disentangled Speech Tokenization
Sameer Khurana, Dominik Klement, Antoine Laurent, Dominik Bobos, Juraj Novosad, Peter Gazdik, Ellen Zhang, Zili Huang, Amir Hussein, Ricard Marxer, Yoshiki Masuyama, Ryo Aihara, Chiori Hori, Francois G. Germain, Gordon Wichern, Jonathan Le Roux
https://arxiv.org/abs/2506.15…

Factorized RVQ-GAN For Disentangled Speech Tokenization
We propose Hierarchical Audio Codec (HAC), a unified neural speech codec that factorizes its bottleneck into three linguistic levels-acoustic, phonetic, and lexical-within a single model. HAC leverages two knowledge distillation objectives: one from a pre-trained speech encoder (HuBERT) for phoneme-level structure, and another from a text-based encoder (LaBSE) for lexical cues. Experiments on English and multilingual data show that HAC's factorized bottleneck yields disentangled token sets: one…