Hidden
Scanning Tunneling Microscopy (STM) — Hidden Tier
(3 scenes)Fully blind server-side evaluation — no data download.
What you get
No data downloadable. Algorithm runs server-side on hidden measurements.
How to use
Package algorithm as Docker container / Python script. Submit via link.
What to submit
Containerized algorithm accepting y + H, outputting x_hat + corrected spec.
Parameter Specifications
🔒
True spec hidden — blind evaluation, only ranges available.
| Parameter | Spec Range | Unit |
|---|---|---|
| tip_electronic_structure | -0.15 – 0.15 | - |
| piezo_creep | -0.7 – 2.3 | - |
| tunneling_barrier_height | 4.29 – 5.19 | eV |
| vibration_amplitude | -0.7 – 2.3 | pm |
Hidden Tier Leaderboard
| # | Method | Score | PSNR | SSIM | Consistency | Trust | Source |
|---|---|---|---|---|---|---|---|
| 1 | SPM-Former + gradient | 0.669 | 26.39 | 0.843 | 0.84 | ✓ Certified | Chen et al., NanoLett 2024 |
| 2 | E2E-BTR + gradient | 0.608 | 23.53 | 0.751 | 0.86 | ✓ Certified | Kossler et al., Sci. Rep. 2022 |
| 3 | DeepSPM + gradient | 0.586 | 22.53 | 0.712 | 0.88 | ✓ Certified | Alldritt et al., Commun. Phys. 2020 |
| 4 | ScoreSPM + gradient | 0.558 | 21.56 | 0.671 | 0.87 | ✓ Certified | Wei et al., 2025 |
| 5 | Reg-Deconv + gradient | 0.552 | 21.4 | 0.664 | 0.86 | ✓ Certified | Dongmo et al., 2000 |
| 6 | U-Net-SPM + gradient | 0.506 | 20.31 | 0.613 | 0.79 | ✓ Certified | SPM U-Net variant |
| 7 | MLE Reconstruction + gradient | 0.484 | 19.48 | 0.573 | 0.8 | ✓ Certified | Classical statistical method |
| 8 | BTR + gradient | 0.476 | 19.19 | 0.559 | 0.8 | ✓ Certified | Villarrubia, JRNIST 1997 |
| 9 | TV-Deconvolution + gradient | 0.443 | 17.89 | 0.495 | 0.83 | ✓ Certified | TV regularization for SPM |
| 10 | DiffusionSPM + gradient | 0.372 | 15.37 | 0.371 | 0.85 | ✓ Certified | Zhang et al., 2024 |
Dataset
Scenes: 3
Scoring Formula
0.4 × PSNR_norm + 0.4 × SSIM + 0.2 × (1 − ‖y − Ĥx̂‖/‖y‖)
PSNR: 40%
SSIM: 40%
Consistency: 20%