CyberGolem

Didn't get an invitation to Project Glasswing?
Neither did TENSORPROS LLC, so DataOpsNick made something open source (Apache-2)

Chat-UI frontend for Alibaba's open-code-review Do you need a way to convert OpenRouter API spend into GLM 5.2 code...

Our latest Al Native Portfolio Demo Application, LOOPMAXXER.review, now includes a PDF preview and LLM writing style filtering by default in the "Preflight Check"!

Check out our demonstration of applied loop engineering and if you like what you see: contact CyberGolem LLC to rescue your "AI transformation" from less experienced operators.

Schedule a 15 minute call and ask about our "price-match guarantee"!

LOOPMAXXER.review Launch Video CyberGolem has been at the forefront of academic writing automation...

Imputing missing values in non-uniformly sampled time series often comes with a distinct trade-off. Standard methods like linear or spline interpolations typically introduce sharp derivative discontinuities, which can degrade velocity or acceleration calculations, while systematically damping cross-channel covariance.

To address these limitations, we have released Non-Uniform Fourier Infilling (NUFI), a new open-source spectral reconstruction framework.
Licensed under the MIT License, NUFI is designed to reconstruct continuous-time representations of multi-dimensional irregular time series while preserving underlying physical and statistical properties.

Here is a breakdown of how the framework operates:
🔹 Infinite Differentiability (C∞): By projecting irregular, incomplete signals onto a smooth trigonometric basis, the reconstructed signal's derivatives of all orders remain analytically continuous.
🔹 Covariance Preservation: Rather than treating channels independently, NUFI employs a pivoted LDL^T-based spectral covariance compensation scheme to maintain the original joint correlation structure of the observed variables.
🔹 Accelerated & Scalable Solvers: Supports both direct SVD-based solvers and an iterative, PyTorch-accelerated Conjugate Gradient (CG) solver O(Nlog⁡N) compatible with CUDA and Apple Silicon (MPS) for high-dimensional scaling.
🔹 Automated Hyperparameter Tuning: Optimizes frequency bins (M) and Tikhonov regularization (α) automatically using SVD-based Generalized Cross-Validation (GCV) to minimize predictive error without explicit test partitions.
🔹 Stochastic Multiple Imputation: Supports posterior process sampling to capture and represent reconstruction uncertainty, rather than relying solely on deterministic means.

We have also designed the library with an agent-native architecture, featuring zero-config entrypoints, append-only transaction logging, and Git/DVC-style lightweight snapshot versioning for data lineage tracking.

If you work with irregular physical signals, IoT sensor networks, or high-frequency financial time series, we hope this framework helps improve your data preparation pipelines.

Explore the codebase, read the technical whitepaper, or support the project with a star on GitHub:
👉 https://github.com/dataopsnick/nonuniform-fourier-infill

We welcome your feedback, issues, and contributions.


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