Tootfinder

🔊 #NowPlaying on #BBCRadio3:
#ThroughTheNight
- American Landscapes
From Turin, the RAI National Symphony Orchestra and conductor David Greilsammer perform music by Grofé, Daugherty, Ives and Copland. Penny Gore presents.
Relisten now 👇
https://www.bbc.co.uk/programmes/m002cyms

'Active travel' study identifies pathways for designing walking- and cycling-friendly cities #city

'Active travel' study identifies pathways for designing walking- and cycling-friendly cities
A new study published in the Proceedings of the National Academy of Sciences reveals the transformative potential of walking- and cycling-friendly policies in cities around the world. Conducted by researchers from UCLA and Google, the study analyzed travel data from more than 11,500 cities across 121 countries, making it the most comprehensive global assessment to date of active transportation, or the potential for walking or biking.

Mind the Gap: Revealing Security Barriers through Situational Awareness of Small and Medium Business Key Decision-Makers
Yuanhaur Chang, Oren Heller, Yaniv Shlomo, Iddo Bar-Noy, Ella Bokobza, Michal Grinstein-Weiss, Ning Zhang
https://arxiv.org/abs/2506.10025

Mind the Gap: Revealing Security Barriers through Situational Awareness of Small and Medium Business Key Decision-Makers
Key decision-makers in small and medium businesses (SMBs) often lack the awareness and knowledge to implement cybersecurity measures effectively. To gain a deeper understanding of how SMB executives navigate cybersecurity decision-making, we deployed a mixed-method approach, conducting semi-structured interviews (n=21) and online surveys (n=322) with SMB key decision-makers. Using thematic analysis, we revealed SMB decision-makers' perceived risks in terms of the digital assets they valued, and…

Identifying Clean and Contaminated Atomic-Sized Gold Contacts under Ambient Conditions Using a Clustering Algorithm
Guillem Pellicer, Carlos Sabater
https://arxiv.org/abs/2506.09608

Identifying Clean and Contaminated Atomic-Sized Gold Contacts under Ambient Conditions Using a Clustering Algorithm
Molecular electronics studies have advanced from early, simple single-molecule experiments at cryogenic temperatures to complex and multifunctional molecules under ambient conditions. However, room-temperature environments increase the risk of contamination, making it essential to identify and quantify clean and contaminated rupture traces (i.e., conductance versus relative electrode displacement) within large datasets. Given the high throughput of measurements, manual analysis becomes unfeasib…

Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems
Bo Ren, Yu Shi, Jinyu Li
https://arxiv.org/abs/2506.06252 https://

Lightweight Prompt Biasing for Contextualized End-to-End ASR Systems
End-to-End Automatic Speech Recognition (ASR) has advanced significantly yet still struggles with rare and domain-specific entities. This paper introduces a simple yet efficient prompt-based biasing technique for contextualized ASR, enhancing recognition accuracy by leverage a unified multitask learning framework. The approach comprises two key components: a prompt biasing model which is trained to determine when to focus on entities in prompt, and a entity filtering mechanism which efficiently…

NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding
Vladimir Bataev, Andrei Andrusenko, Lilit Grigoryan, Aleksandr Laptev, Vitaly Lavrukhin, Boris Ginsburg
https://arxiv.org/abs/2505.22857

NGPU-LM: GPU-Accelerated N-Gram Language Model for Context-Biasing in Greedy ASR Decoding
Statistical n-gram language models are widely used for context-biasing tasks in Automatic Speech Recognition (ASR). However, existing implementations lack computational efficiency due to poor parallelization, making context-biasing less appealing for industrial use. This work rethinks data structures for statistical n-gram language models to enable fast and parallel operations for GPU-optimized inference. Our approach, named NGPU-LM, introduces customizable greedy decoding for all major ASR mod…