Match figure styling and labels to the submitted manuscript

Enlarge the in-canvas fonts and line weights of the result figures so
that they stay legible at the printed column width, split the Fig. 5
convergence curve into a pre-meta adaptation entry and the proposed
MAML entry, and rename the autoencoder legend to match the table row.
Add the analytic complexity replot behind Fig. 4, which was missing
from the repository, and correct the table numbering in the README.

README.md

@@ -19,8 +19,9 @@ fixed random seeds.
 | `c18_mnist_doppler.py` | Doppler sweep behind Fig. 7, with task-conditional decoder-side adaptation at every operating point. |
 | `c19_mnist_epoch.py` | Convergence study behind Fig. 5, recording the task-adapted SER at every training checkpoint. |
 | `c22_maml_epoch.py` | Budget-matched meta-training trajectory that forms the right-hand segment of Fig. 5. |
-| `c21_mnist_flat.py` | Flat Rayleigh study behind Table III, including the decoder-side first-order MAML variant. |
+| `c21_mnist_flat.py` | Flat Rayleigh study behind Table II, including the decoder-side first-order MAML variant. |
 | `c20_bert.py` | Concluding BERT/AG News text study behind Fig. 8, from feature caching to training and evaluation. |
+| `fig_replot_complexity.py` | Analytic complexity comparison behind Fig. 4, for the softmax and the signed realizations. |
 
 ## Reproducing the figures
 
@@ -28,14 +29,21 @@ Each figure regenerates from the stored CSV results without
 retraining.
 
 ```bash
+python3 fig_replot_complexity.py           # Fig. 4
 python3 c19_mnist_epoch.py --mode fig      # Fig. 5
 python3 c13_mnist.py --mode fig            # Fig. 6
 python3 c18_mnist_doppler.py --mode fig    # Fig. 7
 python3 c20_bert.py --mode fig             # Fig. 8
 ```
 
-Table III values are stored in `results_mnist/mnist_flat.csv`, and
-Table IV values come from `results_mnist/mnist_results.csv` and
+Fig. 4 is analytic and needs no stored results. In Fig. 5 the green
+curve is split into two legend entries: left of the dotted line the
+five-step adaptation starts from the jointly trained model and no
+meta-training has taken place, and right of it the receiver
+meta-trains within the same total step budget.
+
+Table II values are stored in `results_mnist/mnist_flat.csv`, and
+Table III values come from `results_mnist/mnist_results.csv` and
 `results_mnist/mnist_doppler.csv`.
 
 ## Rerunning the experiments