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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 ## 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. Feature extraction used PyTorch 2.9 with torchvision model implementations.
The complete pipeline---from raw PDF processing through final classification---was implemented in Python. 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 ## B. Signature Detection Performance