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Paper A v3.17: correct Experimental Setup hardware description

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User flagged that the Experimental Setup claim "All experiments were
conducted on a workstation equipped with an Apple Silicon processor
with Metal Performance Shaders (MPS) GPU acceleration" was factually
inaccurate: YOLOv11 training/inference and ResNet-50 feature
extraction were actually performed on an Nvidia RTX 4090 (CUDA), and
only the downstream statistical analyses ran on Apple Silicon/MPS.

Rewrote Section IV-A (Experimental Setup) to describe the mixed
hardware honestly:

- Nvidia RTX 4090 (CUDA): YOLOv11n signature detection (training +
  inference on 90,282 PDFs yielding 182,328 signatures); ResNet-50
  forward inference for feature extraction on all 182,328 signatures
- Apple Silicon workstation with MPS: downstream statistical analyses
  (KDE antimode, Hartigan dip test, Beta-mixture EM with logit-
  Gaussian robustness check, 2D GMM, BD/McCrary diagnostic, pairwise
  cosine/dHash computations)

Added a closing sentence clarifying platform-independence: because
all steps rely on deterministic forward inference over fixed pre-
trained weights (no fine-tuning) plus fixed-seed numerical
procedures, reported results are platform-independent to within
floating-point precision. This pre-empts any reader concern about
the mixed-platform execution affecting reproducibility.

This correction is consistent with the v3.16 integrity standard
(all descriptions must back-trace to reality): where v3.16 fixed
the fabricated "human-rater sanity sample" and "visual inspection"
claims, v3.17 fixes the similarly inaccurate hardware description.

No substantive results change.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
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gbanyan
2026-04-25 01:27:07 +08:00
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## A. Experimental Setup
All experiments were conducted on a workstation equipped with an Apple Silicon processor with Metal Performance Shaders (MPS) GPU acceleration.
Experiments used mixed hardware: YOLOv11n training and inference for signature detection, and ResNet-50 forward inference for feature extraction over all 182,328 detected signatures, were performed on an Nvidia RTX 4090 (CUDA); the downstream statistical analyses (KDE antimode, Hartigan dip test, Beta-mixture EM with logit-Gaussian robustness check, 2D Gaussian mixture, Burgstahler-Dichev/McCrary density-smoothness diagnostic, and pairwise cosine/dHash computations) were performed on an Apple Silicon workstation with Metal Performance Shaders (MPS) acceleration.
Feature extraction used PyTorch 2.9 with torchvision model implementations.
The complete pipeline---from raw PDF processing through final classification---was implemented in Python.
Because all steps rely on deterministic forward inference over fixed pre-trained weights (no fine-tuning) plus fixed-seed numerical procedures, reported results are platform-independent to within floating-point precision.
## B. Signature Detection Performance