Finalis / Apex Apex は、soft clipping と density control を扱う非線形処理モジュールです。 transfer curve によってピークをなめらかに飽和させ、Protect Highs、Vitality、Harmonics で質感を調整する。 oversampling / anti-aliasing により、密度を上げたときの荒れを抑えます。

Aliasing or wraparound occurs when the FOV is too small, causing tissue outside the image to be mapped onto the opposite side in the phase encoding direction. This can be prevented by using a larger FOV, phase oversampling, phase-direction change, or saturation bands. #RGphx

あとFalconで組んでたシンセが処理重い感じあったんで無駄な処理とか探して調べてたらLadder filterが犯人だった。oversamplingしてて激重

NAM-Oversampler is a fork of the Neural Amp Modeler plugin focused on adding experimental oversampling support for NAM models.github.com/DLC86/NAM-Oversam…

Uncertainty Estimation for Molecular Diffusion Models 1. The paper addresses a practical gap in 3D molecular diffusion generation: pretrained diffusion models can output chemically invalid/unstable molecules, but they provide no principled per-sample signal of “this generation is likely low quality,” which is crucial when downstream evaluation (docking, wet lab) is expensive. 2. The authors propose a post-hoc uncertainty estimator that works with an existing pretrained molecular diffusion model (no retraining): fit a Laplace approximation around the denoiser’s MAP parameters and use it to quantify how variable the denoiser’s noise predictions are during sampling. 3. Core idea: for selected denoising timesteps, sample multiple parameter vectors from the approximate posterior q(θ), compute multiple noise predictions ε_t^m = f_{θ_m}(x_t, t), and take the elementwise sample variance across these predictions; then aggregate over timesteps, atoms, and feature dimensions into a single scalar uncertainty score per generated molecule. 4. The uncertainty is computed along the generation trajectory, motivated by the intuition that “internally uncertain” samples should induce more unstable/variable denoising behavior; empirically, only a small subset of timesteps is needed, reducing overhead. 5. On QM9, the resulting uncertainty score is informative of sample quality: it shows statistically significant negative Spearman correlations with molecular stability, atom stability, and validity, and it is consistently more predictive than diffusion negative log-likelihood (NLL) as a per-sample quality indicator. 6. Concrete QM9 correlations (Spearman ρ): for EDM, uncertainty vs. molecular stability is −0.284 (vs. NLL −0.150); for GeoLDM, −0.333 (vs. NLL −0.171). Similar gaps hold for atom stability and validity, suggesting likelihood is a weaker “verifier” than the proposed uncertainty for these quality metrics. 7. The paper then uses uncertainty for test-time scaling: oversample N molecules (10K→20K) and keep the 10K lowest-uncertainty samples. This improves stability/validity on QM9 for both EDM and GeoLDM, outperforming NLL-based filtering, with a modest tradeoff of ~1% drop in uniqueness. 8. The gains can be material relative to changing the base generator: for EDM on QM9, oversampling to 20K and filtering back to 10K yields ~10% molecular stability improvement, ~1% atom stability improvement, and ~5% validity improvement—comparable in magnitude to switching from EDM to GeoLDM at the same 10K budget. 9. Limitations and ablations: the filtering benefits do not transfer to GEOM-Drugs (larger, more complex molecules), where neither uncertainty- nor NLL-based filtering beats random subsampling. Ablations also show the Fisher-based Laplace covariance is not essential (isotropic perturbations around MAP perform similarly), implying the score may behave more like a sensitivity-to-perturbation measure than strict Bayesian epistemic uncertainty; signal concentrates near the clean end of the trajectory (late denoising steps). 📜Paper: arxiv.org/abs/2606.13451 #DiffusionModels #MolecularGeneration #ComputationalChemistry #UncertaintyEstimation #TestTimeScaling #BayesianDeepLearning #GenerativeModels #3DGeometry #QM9 #GEOMDrugs

I got a new to me Panasonic CD player in a bundle of junked out portable cassette players. There’s actually nothing wrong with the CD player. One thing that I’m really curious about is the 2xDAC and 8 times audio oversampling. (Panasonic SL-S150)

I want 8k, for 1 they master some of the films in 8k. Also for banding reasons on 12bit and vertical resolution. You only need 1080p for 10bit, gradient wise. Though it'll probably still mess up without 2x oversampling. 64k for 16bit... (most are packed 12-14 anyways)

**Yes.** Jury pools draw from voter/DMV records; many states collect race/ethnicity on qualification forms or source lists, making it available to clerks. No evidence of intentional "on-code" oversampling for whites—selection is random by law.

Seems like that's what liberals do. Oversampling polls so it looks like Byron is firmly leading. But ducking and dodging a debate? What is he scared of. 🐓🐓🐓

Entretanto, há pesquisas que possuem outros tipos de objetivos, como comparar ou obter estimativas separadas para sub-populações com um mínimo nível de precisão. Nesse caso, é comum empregar-se uso de "oversampling", em que um ou mais grupos são selecionados desproporcionalmente.

So you’re just happy if Democrats take over Texas? I guess you were never really a Republican. Also, this pollster got it so wrong back in 2024, and is oversampling college voters.

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This also jives with last weeks polling having huge MOE (5 pts) which is never good. This is much lower, meaning their survey was targeted to voting demographics not oversampling likely supporters (which a lot of the other polling has been). So, that’s why this is not good.

When Stability Is Just a Sampling Rate: Scale-Time Theory 10.0 What if the boundary between quantum uncertainty and classical solidity were not only a matter of size or energy, but also of sampling depth? Scale-Time Theory 10.0 explores exactly this possibility. The framework begins not with space as a container, but with one primitive signal-processing-like structure: a global phasor sweep with invariant frequency and invariant carrier speed. From this, a light-calibrated travel step follows. Scale radius, scale address, system baselines, observer reference planes, sampling, aliasing, and stroboscopic lock are then built on top of this foundation. The central intuition is simple: Scale does not change the speed of the carrier. Scale changes the length of the path opened under that speed. Larger scale radii contain more path per shared angular cycle, while the carrier relation itself remains fixed. From this one move, the framework explores distance, delay, apparent size, and containment as selected-reference appearances rather than primitive starting points. The conceptual centerpiece is stroboscopic lock: the stabilized appearance of coherent phase-overlay relative to a chosen baseline. Residual mismatch appears as aliasing. Near the first Nyquist-compatible relation, a standing scale mode is only minimally resolved, producing quantum-like ambiguity and spin-like behavior. At deeper oversampling, through a deep alias-suppression node toward a stable octave-lock node, that same residual phase structure can persist as organized, classical-looking orientation. In this language, the transition from uncertainty to stability becomes a change of sampling regime. The lock nodes are presented openly as defined ideal markers of a dyadic sampling ladder, not as derived constants or numerology. The framework is also clear that the next step must be computational testing. From the same baseline grammar, Scale-Time Theory 10.0 sketches further correspondences: native scale position as rest-mass-like invariant structure, scale-shift into a longer synchronized path lane as energy-like and acceleration-like appearance, gravity-like behavior as baseline drift around a dominant stroboscopic focus, and redshift-like or horizon-like effects as phase-lapse near an orthogonal boundary. This is not presented as a replacement for quantum theory, relativity, or standard cosmology. Those remain the tested languages of modern physics. Scale-Time Theory 10.0 offers a new perspective on how an underlying pre-geometrical scale-space could connect them: quantum-like behavior and relativistic-looking behavior may be different sampling and readout regimes of one phasor-ordered domain. In this view, sampling, aliasing, lock, scale address, baseline drift, containment, and phase-lapse become a unified vocabulary for exploring how the quantum and relativistic pictures might arise from different effective scale-depths and readout speeds. The next step is not rhetoric, but simulation. Simple phasor-sampling models should either reveal stable lock nodes and weak-to-strong resolution transitions, or they should not. That testable direction is what makes the framework worth a careful read. Whether or not one finds the central analogy persuasive, building an entire interpretive architecture from one primitive signal-like relation, and then asking to be tested by it, is a worthwhile exercise in theoretical imagination. The complete framework, including the full relation chain and glossary, is available open access under CC BY 4.0. Read the full PDF: scaletimedynamics.com/en/sca… #ScaleTimeTheory #PreGeometry #QuantumClassicalBridge #SamplingTheory #StroboscopicLock #ScaleSpace #TheoreticalPhysics

Of course there is, there could be an oversampling of US Koreans, Japanese, Taiwanese overwhelming the small number of Indians and Pakistanis in that category.

Addressing Imbalance in Multi-Label Data via Label-Specific Distance-based Oversampling Bin Liu, Jun Wu, Haoyu Peng, Ao Zhou, Jin Wang, QiaoSong Chen, Grigorios Tsoumakas arxiv.org/abs/2606.05927 [𝚌𝚜.𝙻𝙶]

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Shoutout to @BIGBIG_WON for hooking me up with the Rainbow 3 controller 🔥 ✅ MOHJON Capacitive Stick Modules ✅ 12-Bit Grade ADC Chip ✅ 16x Oversampling Depth ✅ 8192 Level Resolution ✅ 2000Hz Polling Rate #BigBigWon #Rainbow3

Great question! Based on our tests, the model didn't seem very sensitive to the sampling ratio between human and robot data. So I think you can definitely get away with oversampling robot data while successfully learning the human task semantics.

Not the dreaded Fox News poll with their chronic oversampling of democrats. No one takes Fox's polling seriously.