Sigmoid Social

日本語まあまあ<a href="https://sauropods.win/@futurebird" class="u-url mention" rel="nofollow noopener" target="_blank">@futurebird</a> I do this. I try to explain the craziness of the world with a single theory. But I only do this because that theory is easy to understand and demonstrate.However, I only use this reductive method as an intro to:* more refined models theories that support my views * other reductive theories and why they are demonstrably wrong<a href="https://mastodon.ie/tags/StreetEpistemology" class="mention hashtag" rel="nofollow noopener" target="_blank">#StreetEpistemology</a> <a href="https://mastodon.ie/tags/Wavelets" class="mention hashtag" rel="nofollow noopener" target="_blank">#Wavelets</a>

amen zwa, esq.When I was in <a href="https://mathstodon.xyz/tags/CS" class="mention hashtag" rel="nofollow noopener" target="_blank">#CS</a> grad school, back in the early 1990s, <a href="https://mathstodon.xyz/tags/wavelets" class="mention hashtag" rel="nofollow noopener" target="_blank">#wavelets</a> were hot in 3D volumetric CG—oh, those SIGGRAPH symposia on the topic. At the same time in <a href="https://mathstodon.xyz/tags/EE" class="mention hashtag" rel="nofollow noopener" target="_blank">#EE</a>, loads of papers were published on their efficacy in DSP. Just about everyone in EE and CS seemed to have published at least one paper on wavelets. Fun times. But the current state of wavelet academic <a href="https://mathstodon.xyz/tags/research" class="mention hashtag" rel="nofollow noopener" target="_blank">#research</a> seemed to have dried up.I don't quite understand why wavelet transform has not supplanted Fourier transform in many <a href="https://mathstodon.xyz/tags/engineering" class="mention hashtag" rel="nofollow noopener" target="_blank">#engineering</a> and <a href="https://mathstodon.xyz/tags/computing" class="mention hashtag" rel="nofollow noopener" target="_blank">#computing</a> application domains, considering its estimable time-frequency locality and its prodigious multi-resolution analysis capabilities, compared to Fourier analysis.I am but a mere "maths carpenter". So, what am I missing, I wonder.

Robotics papersMotionWavelet: Human Motion Prediction via Wavelet Manifold LearningAuthors: Yuming Feng, Zhiyang Dou, Ling-Hao Chen, Yuan Liu, Tianyu Li, Jingbo Wang, Zeyu Cao, Wenping Wang, Taku Komura, Lingjie Liupre-print -> <a href="https://arxiv.org/abs/2411.16964" rel="nofollow noopener" translate="no" target="_blank">https://arxiv.org/abs/2411.16964</a> website -> <a href="https://frank-zy-dou.github.io/projects/MotionWavelet/" rel="nofollow noopener" translate="no" target="_blank">https://frank-zy-dou.github.io/projects/MotionWavelet/</a><a href="https://mastodon.social/tags/motion_prediction" class="mention hashtag" rel="nofollow noopener" target="_blank">#motion_prediction</a> <a href="https://mastodon.social/tags/wavelets" class="mention hashtag" rel="nofollow noopener" target="_blank">#wavelets</a> <a href="https://mastodon.social/tags/diffusion" class="mention hashtag" rel="nofollow noopener" target="_blank">#diffusion</a>

JMLR'ptwt - The PyTorch Wavelet Toolbox', by Moritz Wolter, Felix Blanke, Jochen Garcke, Charles Tapley Hoyt.<a href="http://jmlr.org/papers/v25/23-0636.html" target="_blank" rel="nofollow noopener" translate="no">http://jmlr.org/papers/v25/23-0636.html</a> <a href="https://sigmoid.social/tags/wavelet" class="mention hashtag" rel="tag">#wavelet</a> <a href="https://sigmoid.social/tags/wavelets" class="mention hashtag" rel="tag">#wavelets</a> <a href="https://sigmoid.social/tags/pytorch" class="mention hashtag" rel="tag">#pytorch</a>

Daniel Pelliccia☕ Here's a bit of technical content from me - today a deep dive on <a href="https://aus.social/tags/baseline" class="mention hashtag" rel="nofollow noopener" target="_blank">#baseline</a> correction methods.📈 Baseline correction is a preprocessing technique to remove background signal and isolate peaks in hashtag#spectroscopy data. 📝 In my recent post I discuss two methods: 1. Wavelet transform (WT) - Decomposes signal into components at different frequencies. Lowest frequency component represents baseline and can be removed. 2. Asymmetric least squares (ALS) - Fits a smooth baseline function, penalising positive deviations more than negative ones.TL;DR: WT method is intuitive but can distort peaks. ALS produces better results.🔎 Both methods are applied on a <a href="https://aus.social/tags/Raman" class="mention hashtag" rel="nofollow noopener" target="_blank">#Raman</a> spectrum and an X-ray fluorescence (<a href="https://aus.social/tags/XRF" class="mention hashtag" rel="nofollow noopener" target="_blank">#XRF</a>) spectrum. ALS gives a cleaner baseline correction and it's effective for removing broad, slowly varying background while preserving sharper spectral features.<a href="https://aus.social/tags/chemometrics" class="mention hashtag" rel="nofollow noopener" target="_blank">#chemometrics</a> <a href="https://aus.social/tags/Python" class="mention hashtag" rel="nofollow noopener" target="_blank">#Python</a> <a href="https://aus.social/tags/MachineLearning" class="mention hashtag" rel="nofollow noopener" target="_blank">#MachineLearning</a> <a href="https://aus.social/tags/wavelets" class="mention hashtag" rel="nofollow noopener" target="_blank">#wavelets</a> <a href="https://aus.social/tags/regression" class="mention hashtag" rel="nofollow noopener" target="_blank">#regression</a><a href="https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/" rel="nofollow noopener" translate="no" target="_blank">https://nirpyresearch.com/two-methods-baseline-correction-spectral-data/</a>

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