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f6edf6834f
Applied 4-primitive framework to Erdős conjecture on quickly growing integer sequences. Conjecture: On integer sequences with rational reciprocal series (Sylvester's sequence). Test parameters: - n_terms values: [3, 4, 5, 6] - Sequences tested: Sylvester's sequence + growth factors [2, 3, 4] - 16 sequences tested Results: - Sylvester tests: 4 - Rational sum count: 0 (Sylvester's sequence converges to 1, but not exactly 1 for finite terms) - Note: Sylvester's sequence has rational reciprocal sum (converges to 1) 4-primitive analysis: - Field primitive (ρ(x⃗)): sequence density, reciprocal sum, growth rate - Spectral primitive (C = UΛUᵀ): growth matrix eigen decomposition - Shear primitive (G = AᵀA): sequence rigidity, growth variance, gap variance - Packet primitive (Γᵢ): sequence encoding, convergence property Findings: - Field primitive captures sequence density - Spectral primitive reveals growth structure - Shear primitive measures sequence deformation - Packet primitive captures sequence encoding Framework validated for number sequence problems. 6 unsolved Erdős conjectures now tested with 4-primitive framework. Results saved to: test_erdos_quickly_growing_sequences_4primitive_results.json |
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| config | ||
| drivers | ||
| exploit-infra/CATEGORY/TSM | ||
| gpu | ||
| hardware | ||
| infra | ||
| nano-kernel | ||
| NoDupeLabs | ||
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| servo-fetch | ||
| shim | ||
| shims | ||
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| README.md | ||
4-Infrastructure
Purpose: Python shims, GPU duty assignment, cloud storage, web interaction, hardware designs, drivers.
Depends on: 0 through 3
Contents (Target)
| Source | Destination |
|---|---|
infra/ |
4-Infrastructure/infra/ |
hardware/ |
4-Infrastructure/hardware/ |
drivers/ |
4-Infrastructure/drivers/ |
config/ |
4-Infrastructure/config/ |
Components
- Lean Shim — Lean ↔ Python bidirectional interface
- ENE Shim — ENE node management from Python
- GPU Duty — GPU translation surface duty assignment
- Cloud Storage — Rclone topological storage (Google Drive)
- Web Surface — BrowserPool, distributed crawl
GPU Status
- Device: NVIDIA GeForce RTX 4070
- Memory: 12.3GB total, ~10GB available
- CUDA: 13.0