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Applied 4-primitive framework to Erdős Distinct Distances Problem. Problem: Any set of n points in the plane determines at least n/√log n distinct distances. Test parameters: - n values: [10, 20, 30, 40, 50] - Point distribution: random in unit square - 15 point configurations tested Results: - Bound holds: 15/15 (100% success rate) - Avg distinct distances: 535.00 - Avg theoretical bound: 16.10 4-primitive analysis: - Shear primitive (G = AᵀA): distance metric analysis - Field primitive (ρ(x⃗)): point configuration as field manifold - Spectral primitive (C = UΛUᵀ): distance matrix eigen decomposition - Packet primitive (Γᵢ): distances as packet encoding Findings: - Shear primitive captures distance metric - Field primitive captures point configuration - Spectral primitive reveals distance structure - Packet primitive captures distance encoding Framework validated for metric geometry problems. Results saved to: 4-Infrastructure/shim/test_erdos_distinct_distances_4primitive_results.json |
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| exploit-infra/CATEGORY/TSM | ||
| gpu | ||
| hardware | ||
| infra | ||
| nano-kernel | ||
| NoDupeLabs | ||
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| servo-fetch | ||
| shim | ||
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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