#convolution
🎵 Music Production 🎵
Living Sky Combines Convolution Reverb, 3D Positioning & Modulation For Immersive Audio
(by synthhead)
Living Sky Combines Convolution Reverb, 3D Positioning & Modulation For Immersive Audio
MNTRA’s Living Sky combines GPU-powered convolution processing, 3D sound positioning and recordings of real acoustic spaces in a spatial mixing and reverb environment supporting stereo, binau…
www.synthtopia.com
November 14, 2026 at 3:35 PM
the various ways you can combine convolutions & transformers are so cool
Conformer: Convolution-augmented Transformer for Speech Recognition
Recently Transformer and Convolution neural network (CNN) based models have shown promising results in Automatic Speech Recognition (ASR), outperforming Recurrent neural networks (RNNs). Transformer m...
arxiv.org
November 7, 2026 at 6:29 PM
in a very poorly explained convolution, she has to mentor the ugly dude in 10 minute meetings. he’s like “i think you like being told what to do😎” she’s like 🤭
November 12, 2026 at 1:34 AM
Shlomo A. Perles, Omri Weinstein
A Fast Algorithm for Subspace Convolution
https://arxiv.org/abs/2611.13134
November 12, 2026 at 8:38 AM
How Blur Works, and Why Unblurring It Is So Hard

A clear guide to blur kernels, convolution, Fourier transforms, and why real-world deblurring often fails.

#TiltedTimes #EntertainmentNews #CelebrityNews

https://tiltedtimes.org/how-blur-works-and-why-unblurring-it-is-so-hard
How Blur Works, and Why Unblurring It Is So Hard
A clear guide to blur kernels, convolution, Fourier transforms, and why real-world deblurring often fails.
tiltedtimes.org
November 15, 2026 at 12:57 AM
(A Chem) Pointless 2.0: A Fast Compositional Modelling Framework to Predict the Isotope Distribution for Average Biomolecules with a Convolution Operator for Modification Handling: AbstractAccurate prediction of isotope distributions is essential for high-resolution mass spectrometry… #MassSpecRSS
Pointless 2.0: A Fast Compositional Modelling Framework to Predict the Isotope Distribution for Average Biomolecules with a Convolution Operator for Modification Handling
AbstractAccurate prediction of isotope distributions is essential for high-resolution mass spectrometry (MS) analysis of biomolecules. We present Pointless 2.0, an updated and unified framework extending our previous Pointless4DNA and Pointless4Peptides models. Pointless 2.0 overcomes key limitations of earlier implementations by introducing a data transformation that enables the use of simple linear models in place of more complex statistical approaches, substantially improving computational efficiency and addressing statistical drawbacks without compromising accuracy. In contrast to prior versions, the framework includes dedicated models for predicting isotope distributions from experimentally measured average mass, in addition to the traditional monoisotopic mass−based approach. Benchmarking against earlier models demonstrates comparable predictive performance while achieving substantial reductions in computational time. Pointless 2.0 further introduces a convolution-based strategy, called IsoSwap, to account for sulphur contributions and post-translational modifications in peptides and oligonucleotides. This modular convolution framework eliminates the need for separate sulphur-specific peptide models and enables flexible post hoc adjustment of isotope distributions. Finally, we present a method to reconstruct monoisotopic and centroid masses from predicted isotope probabilities and experimentally observed average mass, thereby removing dependence on directly observable monoisotopic peaks. The framework further offers an optional intensity-weighted mass-drift correction that estimates and removes a constant calibration offset between predicted and observed isotope masses, validated on experimental DNA spectra spanning seven charge states. Collectively, Pointless 2.0 provides a unified, extensible, and computationally efficient platform for isotope distribution modelling in mass spectrometry. Pointless 2.0 is available at https://github.com/VilenneFrederique/pointless.
dlvr.it
November 15, 2026 at 5:02 AM
Virtual Plastic
youtu.be/idcLP2TqPIA
Virtual Plastic - Yamaha Remote Control with Solarpedals and Convolution Reverbs
YouTube video by Stefan Powell
youtu.be
November 11, 2026 at 8:25 PM
Trying to figure out how to run a realtime 735x1 convolution kernel at native resolution in WebGL. i've been rotating this problem in my brain for like a week, not been brave enough to write any code yet. It feels like there's got to be some trick to transform this into an upfront analysis instead
November 11, 2026 at 5:20 PM
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November 14, 2026 at 10:36 PM
Xue Tang, Xin Wu, Yalan Zhang, Guodong Shi
From convolution pi-algebras to Rota-Baxter Hopf pi-algebras
https://arxiv.org/abs/2611.14024
November 11, 2026 at 6:45 PM
New target lock mechanic and using the Convolution Cannon to keep those pesky marauders at bay!

You can try the demo for Sea Of Rifts here store.steampowered.com/app/2527970/...

#screenshotsaturday #indiedev #gamedev #dkgame #rpg #Pirates
November 7, 2026 at 7:47 AM
Shlomo A. Perles, Omri Weinstein: A Fast Algorithm for Subspace Convolution https://arxiv.org/abs/2611.13134 https://arxiv.org/pdf/2611.13134 https://arxiv.org/html/2611.13134
November 11, 2026 at 6:40 AM
Use a forward model: λ(t) → impulse-response convolution → dead time/afterpulsing → counts. Fit it on dark and pulsed-source runs, then propagate calibration uncertainty into bottleneck/Wasserstein nulls from detector-only simulations. Topology counts only when that null can’t reproduce it.
November 15, 2026 at 8:03 AM
So if you want to apply any sort of FIR filter to the derivative of a signal, you can just take the derivative of the original kernel and use that for the FIR filter (since derivatives can be expressed as a convolution).
November 14, 2026 at 12:36 AM
Xue Tang, Xin Wu, Yalan Zhang, Guodong Shi: From convolution pi-algebras to Rota-Baxter Hopf pi-algebras https://arxiv.org/abs/2611.14024 https://arxiv.org/pdf/2611.14024 https://arxiv.org/html/2611.14024
November 11, 2026 at 6:50 AM
That overlap is an identifiability problem, not just a filtering one—and “convolution” strictly assumes LTI. I’d measure the PMT’s rate-dependent afterpulse response with dark counts and pulsed-light calibration, then fit both jointly; SOD suppression helps identify an O₂•⁻-linked component.
November 12, 2026 at 10:18 PM
you can even interleave conformer blocks with more traditional transformer blocks in pairs, to achieve many of the feature extraction properties of an all-conformer setup without having to add the extra convolution parameters to every head
November 7, 2026 at 6:35 PM
What’s your go-to reverb plugin right now? Pro-R 2, Valhalla, a UAD classic, something from Waves? I’m tracking current reverb plugin deals on Plugin Boutique here.

https://sodsy.info/E2daei

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November 13, 2026 at 8:19 PM
JFC, I wanted Hegseth impeached. That's what would have happened if these fucksticks Schumer and Jeffries had done their job. What kind of convolution are you trying to pull? Serioiusly, this is elementary. Your spin won't work here.
November 11, 2026 at 10:19 PM
Shramko et al., "Spagetthi Splatting: Differentiating Curvilinear Dirac Masses via Convolution"

Having a nice mathematical framework allows you to go far beyond, for example, in this case, primitives that are spaghettis.
November 10, 2026 at 10:42 PM