Tootfinder

Microsoft says clients of its Azure cloud platform may experience increased latency after multiple international cables in the Red Sea were cut (Susanne Barton/Bloomberg)
https://www.bloomberg.com/news/articles/2025-09-06/mic…

Efficient Scaling for LLM-based ASR
Bingshen Mu, Yiwen Shao, Kun Wei, Dong Yu, Lei Xie
https://arxiv.org/abs/2508.04096 https://arxiv.org/pdf/2508.04096

Efficient Scaling for LLM-based ASR
Large language model (LLM)-based automatic speech recognition (ASR) achieves strong performance but often incurs high computational costs. This work investigates how to obtain the best LLM-ASR performance efficiently. Through comprehensive and controlled experiments, we find that pretraining the speech encoder before integrating it with the LLM leads to significantly better scaling efficiency than the standard practice of joint post-training of LLM-ASR. Based on this insight, we propose a new m…

User-centered design is what made the iPhone take over the mobile world. But that was then. Now? Now I agree with what @… said.
Byzantine navigation. Confusing state. Gratuitous graphical flair. Designed for marketing, not for user experience.
Full circle. The iOS 26 is the new J2ME.
/end
https://pdx.social/@louie/114793614305262321

What Is Love? Scientists Have Answers—But They Don’t All Agree https://greatergood.berkeley.edu/article/item/what_is_love_scientists_have_answer…

What Is Love? Scientists Have Answers—But They Don’t All Agree
Researchers across disciplines have attempted to define, categorize, and measure the elusive experience of love.

O palhacito do Marques Mendes, a única coisa que tem a dizer sobre a cena do elevador da Glória parece ser que "o Estado falhou".
Parece que lhe passa ao lado (ou por cima, provavelmente) que a manutenção daquilo é agora privada.
O Estado falhou? Talvez, sim, ao passar a manutenção daquilo para mãos privadas, deitando fora trabalhadores com décadas de experiência para poupar uns cobres.

REF-VC: Robust, Expressive and Fast Zero-Shot Voice Conversion with Diffusion Transformers
Yuepeng Jiang, Ziqian Ning, Shuai Wang, Chengjia Wang, Mengxiao Bi, Pengcheng Zhu, Lei Xie, Zhonghua Fu
https://arxiv.org/abs/2508.04996

REF-VC: Robust, Expressive and Fast Zero-Shot Voice Conversion with Diffusion Transformers
In real-world voice conversion applications, environmental noise in source speech and user demands for expressive output pose critical challenges. Traditional ASR-based methods ensure noise robustness but suppress prosody, while SSL-based models improve expressiveness but suffer from timbre leakage and noise sensitivity. This paper proposes REF-VC, a noise-robust expressive voice conversion system. Key innovations include: (1) A random erasing strategy to mitigate the information redundancy inh…

Augmented Question-guided Retrieval (AQgR) of Indian Case Law with LLM, RAG, and Structured Summaries
Vishnuprabha V, Daleesha M Viswanathan, Rajesh R, Aneesh V Pillai
https://arxiv.org/abs/2508.04710 …

Augmented Question-guided Retrieval (AQgR) of Indian Case Law with LLM, RAG, and Structured Summaries
Identifying relevant legal precedents remains challenging, as most retrieval methods emphasize factual similarity over legal issues, and current systems often lack explanations clarifying case relevance. This paper proposes the use of Large Language Models (LLMs) to address this gap by facilitating the retrieval of relevant cases, generating explanations to elucidate relevance, and identifying core legal issues all autonomously, without requiring legal expertise. Our approach combines Retrieval…

Urban Solitude III 🈳
城市孤独 III 🈳
📷 Pentax MX
🎞️Fujifilm Neopan SS, expired 1995
buy me ☕️ ?/请我喝杯☕️？
#filmphotography

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Leveraging Transfer Learning and Mobile-enabled Convolutional Neural Networks for Improved Arabic Handwritten Character Recognition
Mohsine El Khayati, Ayyad Maafiri, Yassine Himeur, Hamzah Ali Alkhazaleh, Shadi Atalla, Wathiq Mansoor
https://arxiv.org/abs/2509.05019

Leveraging Transfer Learning and Mobile-enabled Convolutional Neural Networks for Improved Arabic Handwritten Character Recognition
The study explores the integration of transfer learning (TL) with mobile-enabled convolutional neural networks (MbNets) to enhance Arabic Handwritten Character Recognition (AHCR). Addressing challenges like extensive computational requirements and dataset scarcity, this research evaluates three TL strategies--full fine-tuning, partial fine-tuning, and training from scratch--using four lightweight MbNets: MobileNet, SqueezeNet, MnasNet, and ShuffleNet. Experiments were conducted on three benchma…

🧑🏻‍💻 .NET 10 Preview Now Available on #Azure App Service! :azure: https://azure.github.io/AppService/2025/08/26/dotnet-10-preview-on-App-Service.html…