The Substance VI 🧪
某种物质 VI 🧪
📷 Nikon F4E
🎞️ Harman Switch Azure (FF)
If you like my work, Support by buying me a coffee or a roll of film from PayPal https://www.paypal.com/paypalme/ydcdingsite
“The founder of OpenClaw, vibe coder Peter Steinberger, was also hired by a Big Tech firm.”
If there’s one thing that gives me comfort in this world, it’s knowing I will never be called “vibe coder”.
https://arstechnica.com/ai/2026/03/meta-acquire…
🧵 3/3
Full credits:
Project funded by Bartlett School of Architecture, UCL
Co-authors: Nina Vollenbroker, Jos Boys, Mine Sak-Acur
Insights generated by our co-creators Coco Briden, Christopher Laing, David Johnson, Jessica Thom, Jessica Ryan-Ndegwa, Jordan Whitewood-Neal, Mandy Redvers Rowe and Natasha Trotman
This has been a fantastic project and a joy to work on, there'll be more to come in the future
Jenny O’Connell-Nowain was ready to go to jail.
She had been prepared to spend six months in the custody of the Shasta county sheriff’s office.
One of the top prosecutors in this part of far northern California had presented the evidence against her in a weeklong trial,
and a jury had delivered a guilty verdict.
A judge offered probation, but O’Connell-Nowain did not agree to the terms.
Her crime?
Sitting on the floor in front of the dais of the board o…
The Substance 🧪
某种物质 🧪
📷 Nikon F4E
🎞️ Harman Switch Azure (FF)
If you like my work, Support by buying me a coffee or a roll of film from PayPal https://www.paypal.com/paypalme/ydcdingsite
Cowboys Could Circle Back on Trade for $41 Million LB During 2026 NFL Draft https://heavy.com/sports/nfl/dallas-cowboys/patrick-queen-nfl-draft-trade-rumors/
Chunkwise Aligners for Streaming Speech Recognition
Wen Shen Teo, Takafumi Moriya, Masato Mimura
https://arxiv.org/abs/2605.11422 https://arxiv.org/pdf/2605.11422 https://arxiv.org/html/2605.11422
arXiv:2605.11422v1 Announce Type: new
Abstract: We propose the Chunkwise Aligner, a novel architecture for streaming automatic speech recognition (ASR). While the Transducer is the standard model for streaming ASR, its training is costly due to the need to compute all possible audio-label alignments. The recently introduced Aligner reduces this cost by discarding explicit alignments, but this modification makes it unsuitable for streaming. Our approach overcomes this limitation by dividing the audio into chunks and aligning each label to the leftmost frames of its chunk, whereas transitions between chunks are managed by a learned end-of-chunk probability. Experiments show that the Chunkwise Aligner not only matches the Transducer's accuracy in both offline and streaming scenarios, but also offers superior training and decoding efficiencies.
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