Tootfinder

"At its heart, Nemo is an in-memory data processing engine, written in Rust."
"Nemo is a university-based OSS project and still a prototype, so bear with us if you discover bugs."
Glad we've cleared that up.
https://lists…

Eurovision winner Nemo to perform first solo concert in Kyiv after Russian attacks: https://benborges.xyz/2025/10/10/eurovision-winner-nemo-to-perform.html

Losing Nemo: In the Red Sea, clownfish vanish as anemones bleach
https://news.mongabay.com/2025/10/losing-nemo-in-the-red-sea-clownfish-vanish-as-anemones-bleach/

NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
Xiaohan Bi, Binhang Qi, Hailong Sun, Xiang Gao, Yue Yu, Xiaojun Liang
https://arxiv.org/abs/2508.11348

NeMo: A Neuron-Level Modularizing-While-Training Approach for Decomposing DNN Models
With the growing incorporation of deep neural network (DNN) models into modern software systems, the prohibitive construction costs have become a significant challenge. Model reuse has been widely applied to reduce training costs, but indiscriminately reusing entire models may incur significant inference overhead. Consequently, DNN modularization has gained attention, enabling module reuse by decomposing DNN models. The emerging modularizing-while-training (MwT) paradigm, which incorporates mod…

Canary-1B-v2 & Parakeet-TDT-0.6B-v3: Efficient and High-Performance Models for Multilingual ASR and AST
Monica Sekoyan, Nithin Rao Koluguri, Nune Tadevosyan, Piotr Zelasko, Travis Bartley, Nick Karpov, Jagadeesh Balam, Boris Ginsburg
https://arxiv.org/abs/2509.14128

Canary-1B-v2 & Parakeet-TDT-0.6B-v3: Efficient and High-Performance Models for Multilingual ASR and AST
This report introduces Canary-1B-v2, a fast, robust multilingual model for Automatic Speech Recognition (ASR) and Speech-to-Text Translation (AST). Built with a FastConformer encoder and Transformer decoder, it supports 25 languages primarily European. The model was trained on 1.7M hours of total data samples, including Granary and NeMo ASR Set 3.0, with non-speech audio added to reduce hallucinations for ASR and AST. We describe its two-stage pre-training and fine-tuning process with dynamic d…

Am zweiten Tag des Urlaubs versuchen wir Nemo zu finden. Dazu geht es mit dem Tauchboot des Sinai Dive Clubs zur Insel Tiran im Roten Meer.
#Agypten #scubadiving #vacationtime…

MMORE: Massive Multimodal Open RAG & Extraction
Alexandre Sallinen, Stefan Krsteski, Paul Teiletche, Marc-Antoine Allard, Baptiste Lecoeur, Michael Zhang, Fabrice Nemo, David Kalajdzic, Matthias Meyer, Mary-Anne Hartley
https://arxiv.org/abs/2509.11937

MMORE: Massive Multimodal Open RAG & Extraction
We introduce MMORE, an open-source pipeline for Massive Multimodal Open RetrievalAugmented Generation and Extraction, designed to ingest, transform, and retrieve knowledge from heterogeneous document formats at scale. MMORE supports more than fifteen file types, including text, tables, images, emails, audio, and video, and processes them into a unified format to enable downstream applications for LLMs. The architecture offers modular, distributed processing, enabling scalable parallelization ac…

Ecosystem Recovery to Historical Targets Becomes Unattainable Under Modelled Fishing and Climate in the Barents Sea
Matthew Hatton, Jack H Laverick, Neil Banas, Michael Heath
https://arxiv.org/abs/2510.04806

Ecosystem Recovery to Historical Targets Becomes Unattainable Under Modelled Fishing and Climate in the Barents Sea
Climate change and fisheries jointly shape the resilience of the Barents Sea marine ecosystem, yet the recovery of key fish populations to climate and anthropogenic disturbances requires further investigation. This study examines how fishing pressure and climate change, driven by the NEMO-MEDUSA Earth system model, influence the recovery times of Demersal and Planktivorous fish in the Barents Sea. We used the StrathE2EPolar end-to-end ecosystem model to simulate transient dynamics under increas…