@tiotasram@kolektiva.social2026-07-12 16:28:07
Since it was relevant to a discussion I just had on here and is something most people probably haven't thought about much (unless you've taken one of a handful of philosophy classes), I thought I'd try to lay out a key piece of Descartes' Meditations (#philosophy
@markhburton@mstdn.social2026-07-09 08:43:36
I agree with Tim
https://timotheeparrique.com/a-response-to-the-world-inequality-lab-degrowth-for-global-justice/
"First, I want to challenge their definitions of sufficiency and degrowth, showing that their usage of the…
@toxi@mastodon.thi.ng2026-05-08 11:42:03
Schlangenhaus (16th century, Werdenberg, St. Gallen)
#FensterFreitag #History #Switzerland
@kexpmusicbot@mastodonapp.uk2026-07-05 09:09:33
@leftsidestory@mstdn.social2026-05-23 02:05:47
The Story of the Two Towers II 📖
双塔故事 II 📖
📷 Nikon F4
🎞️ 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
@arXiv_eessAS_bot@mastoxiv.page2026-05-13 07:40:50
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.
toXiv_bot_toot
@yaya@jorts.horse2026-05-28 20:13:17
done a bit of sprucing to the football nook, finally put my scarves up and put up the new shirts I got specifically for decoration (bc they don't fit me lol)
I need to acquire a ladder or a tall friend to fill in the space higher up #fedifc
@arXiv_quantph_bot@mastoxiv.page2026-06-11 08:30:44
An iterative Ising decoder for quantum error correction codes
Yuanqi Liu, Weilei Zeng, Peixiang Li, Yantong Liu, Guangyao Huang, Yingwen Liu, Dongyang Wang, Junjie Wu, Lingling Lao
https://arxiv.org/abs/2606.12301 https://arxiv.org/pdf/2606.12301 https://arxiv.org/html/2606.12301
arXiv:2606.12301v1 Announce Type: new
Abstract: The Ising framework maps the decoding problem in quantum error correction onto ground-state optimization of a classical Hamiltonian, in which $X$-$Z$ error correlations enter as cross terms. Under phenomenological depolarizing noise, the exact joint formulation contains up to 8-body interactions for the toric code and 10-body for the $6.6.6$ color code. These high-order terms degrade solver convergence, inflate runtime, and raise the auxiliary spin overhead when embedding into native 2-body Ising hardware. In this work, we propose the iterative low-order decoding (ILOD) algorithm, which alternates between $X$- and $Z$-type sub-Hamiltonians, approximating cross-type correlations through Bayesian priors that reweight each type's couplings using the other type's inferred error configuration. This halves the maximum body count of interaction terms in the Hamiltonian, accelerating the solver, restoring convergence at larger code distances, and reducing the total spin count for 2-body embedding by a factor of $2.5$. For the toric code, ILOD attains a threshold of $4.73%$ versus $4.83%$ for the joint formulation, with the empirical runtime ratio scaling as $(0.81)^d$. For the $6.6.6$ color code, their thresholds agree within statistical uncertainty for small code distances, and ILOD remains convergent for larger distances where the joint formulation fails to converge despite a larger annealing budget.
toXiv_bot_toot
@kexpmusicbot@mastodonapp.uk2026-07-05 01:21:29