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Code and stored results for the IEEE Communications Letters submission

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Semantic Multiplexing Gain in Wireless Systems via Expanded Embeddings:
A BERT Case Study. Includes the shared library, all experiment scripts
(training with and without SNR-aware MAML, the token-domain comparison,
the K sweep, and DistilBERT), the replot script that regenerates every
figure from the stored results, the supplementary probe-versus-cosine
analysis, and the raw results behind every figure in the letter.
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Ki-Ho Lee
2026-08-26 22:02:59 +09:00
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README.md
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# Semantic Multiplexing Gain in Wireless Systems via Expanded Embeddings: A BERT Case Study
Code, stored results, and supplementary material for the IEEE
Communications Letters submission by Ki-Ho Lee, Hyun-Ho Choi, and
Jung-Ryun Lee.
Multiple users share one expanded embedding block of dimension
`d_s = K * d_b`: each user's frozen BERT sentence embedding is projected
into the shared space, superimposed through learnable masks, and
demultiplexed by user-wise attention. All reported transceivers are
trained with SNR-aware MAML; the training without MAML of the earlier
JSAC paper is included as a prior-art reference.
## Files
| File | Purpose |
|---|---|
| `bert_semcom.py` | Shared library: BERT extractor, transceiver model, channel, MAML helpers |
| `cl_experiments.py` | Held-out split, joint-trained configurations, ToDMA token-domain benchmark, linear probe, latency |
| `cl_maml_all.py` | SNR-aware MAML training for every reported configuration (including the conventional orthogonal scheme) |
| `cl_maml_extra.py` | MAML K sweep (K = 1, 2, 8) and DistilBERT replication |
| `replot_cl.py` | Regenerates Figs. 2 and 3 of the letter from the stored JSON results |
| `probe_vs_cosine.py` | Supplementary probe-accuracy-versus-cosine-similarity analysis |
| `fig_cl/*.json`, `fig_cl/*.csv` | Stored raw results behind every figure and quoted number |
## Reproducing
Requirements: Python 3.10+, PyTorch (CUDA), `transformers`, `datasets`,
`matplotlib`, `numpy`. AG News loads from the Hugging Face hub
(`fancyzhx/ag_news` fallback included).
```bash
python cl_experiments.py --save-dir fig_cl # joint runs + ToDMA benchmark (~3 h on a laptop GPU)
python cl_maml_all.py --save-dir fig_cl # MAML runs (~9 h)
python cl_maml_extra.py --save-dir fig_cl # MAML K sweep + DistilBERT (~6 h)
python replot_cl.py # Figs. 2 and 3 from stored results
python probe_vs_cosine.py # supplementary analysis below
```
All experiments fix their random seeds (training seed 42, evaluation
seed 123, ToDMA seed 7) and evaluate on a held-out test split of 2,000
AG News sentences disjoint from the 8,000-sentence training pool.
`replot_cl.py` and `probe_vs_cosine.py` read only the stored results,
so every figure is regenerable without rerunning the experiments.
## Figures of the letter
**Fig. 2 - per-user cosine similarity vs. SNR** (proposed scheme for
U = 1..4 at K = 4, the conventional orthogonal scheme, the
matched-budget schemes, and the joint training of the earlier JSAC
paper, all on the held-out test set):
![Fig. 2](fig_cl/cl_fig_mux.png)
**Fig. 3 - aggregate fidelity across load** (SNR-aware MAML vs. joint
training at 20 dB, with the fully loaded orthogonal reference):
![Fig. 3](fig_cl/cl_fig_agg.png)
## Supplementary: probe accuracy vs. cosine similarity
The letter measures semantic fidelity by the cosine similarity of the
recovered embeddings and corroborates it with a downstream perception
metric: the AG News topic accuracy of a linear probe trained on clean
training-pool embeddings and applied to the recovered test embeddings
(clean reference about 0.855, sampling error about +/-0.01).
Across 7 schemes x 7 SNRs (49 operating points), probe accuracy tracks
cosine similarity with a Pearson correlation of **r = 0.903**:
![Probe accuracy vs. cosine similarity](fig_cl/probe_vs_cosine.png)
Two readings follow. First, the low-SNR advantage of the analog
embedding schemes over the token-domain scheme appears in both metrics
(for example 0.848 vs. 0.772 in CosSim and 0.805 vs. 0.762 in accuracy
at 5 dB). Second, schemes within about 0.01 of each other in CosSim
differ in accuracy only on the order of the sampling error, so the
cosine metric used throughout the letter is consistent with downstream
perception on this task.
bert_semcom.py
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# =========================================================
# bert_semcom.py
# BERT-based Multi-User Semantic Communication
# with Expanded Shared Embedding + User-Wise Attention
#
# Key Idea:
# Each user's BERT embedding (768-dim) is EXPANDED to a
# larger shared embedding space (e.g., 768*K where K=4),
# enabling statistical multiplexing gain through masking
# in the over-provisioned shared space.
#
# Architecture:
# Tx:
# b_u = BERT(text_u) # (d_bert,) e.g., 768
# e_u = W_tx(b_u) # (d_shared,) e.g., 3072
# x_u = e_u ⊙ m_u # user-masked in expanded space
# y = Σ_u x_u # over-the-air superposition
# y_rx = channel(y)
#
# Rx (User-wise attention demux):
# R_i = y_rx ⊙ m_i # candidate projections
# a_{u,i} = softmax( <norm(R_i), norm(q_u)> / sqrt(d) )
# z_u = Σ_i a_{u,i} R_i
# b̂_u = W_rx(z_u) # (d_bert,) recovered
#
# Loss:
# L = (1-λ) * MSE(b_u, b̂_u) + λ * (1 - CosSim(b_u, b̂_u))
#
# Metrics: Cosine similarity, MSE
#
# Statistical Multiplexing Gain:
# Even with U=2 users, projecting to d_shared = d_bert*4
# provides over-provisioned subspace for each user's mask,
# reducing inter-user interference and improving separation.
#
# Train modes: joint / maml_decoder / maml_full
# =========================================================
import argparse
import math
import random
import csv
import os
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
try:
from torch.func import functional_call
except Exception:
from torch.nn.utils.stateless import functional_call
# =========================================================
# BERT Embedding Extractor (frozen)
# =========================================================
class BertEmbeddingExtractor:
"""
Extract sentence-level embeddings from pre-trained BERT.
Uses mean pooling over non-padding tokens to mitigate
BERT [CLS] anisotropy. Returns un-normalized embeddings;
centering and L2-norm are applied at the cache level.
"""
def __init__(self, model_name="bert-base-uncased", device="cpu"):
from transformers import BertModel, BertTokenizer
self.tokenizer = BertTokenizer.from_pretrained(model_name)
self.model = BertModel.from_pretrained(model_name).to(device)
self.model.eval()
self.device = device
self.embed_dim = self.model.config.hidden_size # 768
@torch.no_grad()
def encode(self, texts):
"""Mean-pooled token embedding (excluding padding)."""
inputs = self.tokenizer(
texts, padding=True, truncation=True,
max_length=64, return_tensors="pt"
).to(self.device)
outputs = self.model(**inputs)
last_hidden = outputs.last_hidden_state # (B, T, d)
mask = inputs["attention_mask"].unsqueeze(-1).float() # (B, T, 1)
summed = (last_hidden * mask).sum(dim=1) # (B, d)
count = mask.sum(dim=1).clamp(min=1.0) # (B, 1)
mean_emb = summed / count # (B, d)
return mean_emb
# =========================================================
# Text Dataset
# =========================================================
def load_sentences(source="ag_news", max_sentences=50000,
min_len=5, max_len=30):
"""Load sentences from various sources."""
sentences = []
if source == "europarl":
try:
from datasets import load_dataset
ds = load_dataset("wmt14", "de-en", split="train", streaming=True)
for example in ds:
sent = example["translation"]["en"]
words = sent.split()
if min_len <= len(words) <= max_len:
sentences.append(sent)
if len(sentences) >= max_sentences:
break
except Exception:
print("[INFO] WMT14 not available, trying ag_news...")
source = "ag_news"
if source == "ag_news":
try:
from datasets import load_dataset
ds = load_dataset("ag_news", split="train")
for example in ds:
sent = example["text"]
first_sent = sent.split(".")[0].strip()
words = first_sent.split()
if min_len <= len(words) <= max_len:
sentences.append(first_sent)
if len(sentences) >= max_sentences:
break
except Exception:
print("[INFO] ag_news not available, using synthetic...")
source = "synthetic"
if source == "synthetic" or len(sentences) < 1000:
print("[INFO] Generating synthetic diverse sentences...")
templates = [
"The {} {} the {} in the {}.",
"A {} {} quickly {} the {}.",
"Several {} {} near the {} {}.",
"The {} and {} {} {} together.",
"Every {} must {} its own {}.",
"Under the {}, a {} {} {} softly.",
"The {} of {} {} a {} signal.",
"Without {}, the {} cannot {} properly.",
"A new {} {} from the {} {}.",
"The {} {} through the {} channel.",
]
nouns = ["system", "signal", "network", "channel", "user", "device",
"antenna", "receiver", "transmitter", "waveform", "protocol",
"data", "packet", "frequency", "power", "noise", "beam",
"satellite", "tower", "base station", "terminal", "sensor",
"message", "code", "sequence", "spectrum", "bandwidth"]
verbs = ["processes", "transmits", "receives", "analyzes", "encodes",
"decodes", "modulates", "filters", "amplifies", "detects",
"estimates", "optimizes", "adapts", "allocates", "schedules"]
adjs = ["wireless", "digital", "analog", "robust", "adaptive",
"cognitive", "massive", "distributed", "cooperative", "mobile",
"reliable", "efficient", "dynamic", "intelligent", "semantic"]
for _ in range(max(max_sentences, 50000)):
tmpl = random.choice(templates)
n_slots = tmpl.count("{}")
fillers = [random.choice(random.choice([nouns, verbs, adjs]))
for _ in range(n_slots)]
sentences.append(tmpl.format(*fillers))
random.shuffle(sentences)
return sentences[:max_sentences]
# =========================================================
# Channel
# =========================================================
def apply_channel(y, snr_db, channel="rayleigh"):
"""
y: (d_shared,) real-valued, power-normalized embedding vector.
Rayleigh: magnitude fading |h| where h ~ CN(0,1).
"""
snr_lin = 10 ** (snr_db / 10.0)
noise_var = 1.0 / snr_lin
if channel == "rayleigh":
# Complex Rayleigh fading -> magnitude |h|
h_real = torch.randn((), device=y.device)
h_imag = torch.randn((), device=y.device)
h_mag = torch.sqrt(h_real**2 + h_imag**2) / math.sqrt(2.0)
y = h_mag * y
noise = torch.randn_like(y) * math.sqrt(noise_var)
return y + noise
# =========================================================
# Model: Expanded Shared Embedding + User-Wise Attention
# =========================================================
class BertSemComMux(nn.Module):
"""
BERT-based Multi-User Semantic Communication.
Key design: BERT embedding (d_bert=768) is EXPANDED to
d_shared = d_bert * mux_factor (e.g., 768*4=3072) to
provide statistical multiplexing gain.
Even with fewer users than mux_factor, the expanded space
gives each user more "room" for orthogonal mask allocation,
reducing inter-user interference.
"""
def __init__(self, U, d_bert, d_shared, hidden=512):
super().__init__()
self.U = U
self.d_bert = d_bert # 768
self.d_shared = d_shared # e.g., 768*4 = 3072
# Tx: single-layer linear projection to the shared space
# (no sub-d_bert bottleneck, no costly d_shared x d_shared layer).
# Followed by LayerNorm for numerical stability.
self.tx_proj = nn.Sequential(
nn.Linear(d_bert, d_shared),
nn.LayerNorm(d_shared),
)
# User-specific masks in expanded shared space
self.user_mask = nn.Embedding(U, d_shared)
# Rx: user-wise attention queries in shared space
self.user_query = nn.Embedding(U, d_shared)
# Rx: single-layer linear projection back to BERT space.
self.rx_proj = nn.Sequential(
nn.LayerNorm(d_shared),
nn.Linear(d_shared, d_bert),
)
nn.init.normal_(self.user_mask.weight, std=0.5)
nn.init.normal_(self.user_query.weight, std=0.5)
def forward(self, bert_embs, snr_db, channel, params=None,
return_intermediate=False):
"""
bert_embs: (U, d_bert) - BERT sentence embeddings
Returns: (U, d_bert) - recovered embeddings
"""
dev = bert_embs.device
# ----- Tx: Project to expanded shared space -----
if params is None:
e = self.tx_proj(bert_embs) # (U, d_shared)
m = self.user_mask.weight # (U, d_shared)
else:
e = functional_call(self.tx_proj, params["tx_proj"],
(bert_embs,))
m = functional_call(
self.user_mask, params["user_mask"],
(torch.arange(self.U, device=dev),))
# Masking in expanded space
x = e * m # (U, d_shared)
y_tx = x.sum(dim=0) # (d_shared,)
# ----- Power normalization -----
# OFDM model: each subcarrier (dimension) has unit power.
# Larger d_shared = more subcarriers = more bandwidth.
# Per-dimension SNR is the same regardless of d_shared.
# This models the bandwidth-quality trade-off:
# d_shared=128: compressed (low bandwidth, lossy)
# d_shared=768: matched (1:1)
# d_shared=3072: expanded (high bandwidth, room for separation)
y_tx = y_tx / (torch.sqrt((y_tx ** 2).mean() + 1e-8))
# ----- Channel -----
y_rx = apply_channel(y_tx, snr_db, channel)
# ----- Rx: user-wise attention -----
R = y_rx.unsqueeze(0) * m # (U, d_shared)
Rn = F.normalize(R, p=2, dim=-1)
recovered = []
attn_weights = []
for u in range(self.U):
if params is None:
q = self.user_query(torch.tensor(u, device=dev))
else:
q = functional_call(
self.user_query, params["user_query"],
(torch.tensor(u, device=dev),))
qn = F.normalize(q, p=2, dim=-1)
# Native cosine-similarity scores (no extra 1/sqrt(d_s)
# damping). Since Rn, qn are unit-norm, the raw dot product
# is already in [-1, 1] and softmax-friendly. The standard
# 1/sqrt(d) attention temperature would collapse softmax to
# near-uniform when d_s is large (e.g., 3072).
scores = (Rn @ qn) * 8.0 # mild temperature sharpening
attn = F.softmax(scores, dim=0)
attn_weights.append(attn.detach())
z_u = (attn.unsqueeze(-1) * R).sum(dim=0) # (d_shared,)
# Reverse projection: shared space -> BERT space
if params is None:
b_hat_u = self.rx_proj(z_u)
else:
b_hat_u = functional_call(self.rx_proj, params["rx_proj"],
(z_u,))
recovered.append(b_hat_u)
result = torch.stack(recovered, dim=0) # (U, d_bert)
if return_intermediate:
return result, {
"y_tx": y_tx.detach(),
"y_rx": y_rx.detach(),
"attn": torch.stack(attn_weights, dim=0), # (U, U)
"masks": m.detach(),
"projected": e.detach(),
}
return result
# =========================================================
# Loss Function
# =========================================================
def semantic_loss(b_orig, b_hat, lam=0.5):
"""
Combined MSE + Cosine Similarity loss.
b_orig, b_hat: (U, d_bert)
"""
mse = F.mse_loss(b_hat, b_orig)
cos_sim = F.cosine_similarity(b_hat, b_orig, dim=-1).mean()
loss = (1.0 - lam) * mse + lam * (1.0 - cos_sim)
return loss, mse.item(), cos_sim.item()
# =========================================================
# MAML Helpers (same structure as original maml.py)
# =========================================================
def split_params(model: BertSemComMux):
return {
"tx_proj": dict(model.tx_proj.named_parameters()),
"user_mask": dict(model.user_mask.named_parameters()),
"user_query": dict(model.user_query.named_parameters()),
"rx_proj": dict(model.rx_proj.named_parameters()),
}
def select_inner_keys(train_mode: str):
if train_mode == "maml_decoder":
return ["user_query", "rx_proj"]
if train_mode == "maml_full":
return ["tx_proj", "user_mask", "user_query", "rx_proj"]
return []
def ordered_param_items(param_dict: dict):
return [(k, param_dict[k]) for k in sorted(param_dict.keys())]
def gather_inner_params(fast_params: dict, inner_keys: list):
flat_list, meta_index = [], []
for key in inner_keys:
for name, p in ordered_param_items(fast_params[key]):
flat_list.append(p)
meta_index.append((key, name))
return flat_list, meta_index
def apply_inner_update(fast_params, inner_keys, meta_index, grads,
inner_lr, maml_order):
new_fast = {k: dict(v) for k, v in fast_params.items()}
for (key, name), g in zip(meta_index, grads):
if maml_order == "first":
g = g.detach()
new_fast[key][name] = new_fast[key][name] - inner_lr * g
return new_fast
# =========================================================
# Embedding Cache (pre-compute BERT embeddings)
# =========================================================
class EmbeddingCache:
"""Pre-compute, center, and L2-normalize BERT embeddings.
Centering removes the dominant mean direction to reduce
anisotropy; L2 normalization gives unit-norm embeddings
suitable for cosine-similarity-based loss/metrics.
"""
def __init__(self, bert_extractor, sentences, batch_size=64):
self.embeddings = []
self.sentences = sentences
print(f"[INFO] Pre-computing BERT embeddings for "
f"{len(sentences)} sentences...")
for i in range(0, len(sentences), batch_size):
batch = sentences[i:i+batch_size]
emb = bert_extractor.encode(batch)
self.embeddings.append(emb.cpu())
self.embeddings = torch.cat(self.embeddings, dim=0)
# Center (subtract mean direction) to reduce anisotropy
self.mean = self.embeddings.mean(dim=0, keepdim=True)
self.embeddings = self.embeddings - self.mean
# L2-normalize
self.embeddings = F.normalize(self.embeddings, p=2, dim=-1)
# Sanity check: random pairwise cos sim should be near 0
n_check = min(500, len(self.embeddings))
idx1 = torch.randperm(len(self.embeddings))[:n_check]
idx2 = torch.randperm(len(self.embeddings))[:n_check]
cs = F.cosine_similarity(self.embeddings[idx1],
self.embeddings[idx2], dim=-1)
print(f"[INFO] Cached {self.embeddings.shape[0]} embeddings, "
f"shape={self.embeddings.shape}")
print(f"[INFO] After centering: random pair cos sim "
f"mean={cs.mean():.4f}, std={cs.std():.4f}")
def sample(self, U, device):
"""Sample U random embeddings."""
idx = torch.randint(0, len(self.embeddings), (U,))
return self.embeddings[idx].to(device), idx
def get_sentences(self, idx):
"""Get sentences by index."""
return [self.sentences[i] for i in idx]
# =========================================================
# Evaluation
# =========================================================
@torch.no_grad()
def evaluate(model, cache, args, device):
model.eval()
total_cos, total_mse, n_trials = 0.0, 0.0, 0
if not args.eval_adapt:
for _ in range(args.eval_trials):
b, _ = cache.sample(args.users, device)
b_hat = model(b, args.snr, args.channel, params=None)
cos = F.cosine_similarity(b_hat, b, dim=-1).mean().item()
mse = F.mse_loss(b_hat, b).item()
total_cos += cos
total_mse += mse
n_trials += 1
return total_cos / n_trials, total_mse / n_trials
# Few-shot adaptation
inner_keys = select_inner_keys(args.eval_adapt_mode)
base_params = split_params(model)
for _ in range(args.eval_trials):
b_sup, _ = cache.sample(args.users, device)
fast_params = {k: {n: p for n, p in v.items()}
for k, v in base_params.items()}
with torch.enable_grad():
for _ in range(args.eval_inner_steps):
b_hat_sup = model(b_sup, args.snr, args.channel,
params=fast_params)
loss_sup, _, _ = semantic_loss(b_sup, b_hat_sup,
args.loss_lambda)
flat_list, meta_index = gather_inner_params(
fast_params, inner_keys)
grads = torch.autograd.grad(loss_sup, flat_list,
create_graph=False)
fast_params = apply_inner_update(
fast_params, inner_keys, meta_index, grads,
inner_lr=args.eval_inner_lr, maml_order="first")
b_q, _ = cache.sample(args.users, device)
b_hat_q = model(b_q, args.snr, args.channel, params=fast_params)
cos = F.cosine_similarity(b_hat_q, b_q, dim=-1).mean().item()
mse = F.mse_loss(b_hat_q, b_q).item()
total_cos += cos
total_mse += mse
n_trials += 1
return total_cos / n_trials, total_mse / n_trials
# =========================================================
# Train & Eval
# =========================================================
def train_and_eval(args):
if args.cuda and torch.cuda.is_available():
device = torch.device("cuda")
elif torch.backends.mps.is_available():
device = torch.device("mps")
else:
device = torch.device("cpu")
torch.manual_seed(args.seed)
random.seed(args.seed)
np.random.seed(args.seed)
# ---- Load BERT and pre-compute embeddings ----
bert = BertEmbeddingExtractor(args.bert_model, device)
d_bert = bert.embed_dim # 768
# Compute d_shared = d_bert * mux_factor
d_shared = d_bert * args.mux_factor
print(f"[INFO] d_bert={d_bert}, mux_factor={args.mux_factor}, "
f"d_shared={d_shared}")
sentences = load_sentences(args.data_source, args.max_sentences)
print(f"[INFO] Loaded {len(sentences)} sentences")
cache = EmbeddingCache(bert, sentences,
batch_size=args.bert_batch_size)
# Free BERT from GPU
del bert
if torch.cuda.is_available():
torch.cuda.empty_cache()
# ---- Build model ----
model = BertSemComMux(args.users, d_bert, d_shared,
args.hidden).to(device)
opt = torch.optim.Adam(model.parameters(), lr=args.lr)
train_snr_list = list(args.train_snr)
inner_keys = select_inner_keys(args.train_mode)
# ---- Config ----
print("=" * 60)
print(" BERT Semantic Communication - Expanded Shared Embedding")
print("=" * 60)
print(f" Device : {device}")
print(f" Train mode : {args.train_mode}")
print(f" MAML order : {args.maml_order}")
print(f" Users (U) : {args.users}")
print(f" BERT dim (d_bert) : {d_bert}")
print(f" Mux factor (K) : {args.mux_factor}")
print(f" Shared dim (d_shared): {d_shared}")
print(f" Hidden (MLP) : {args.hidden}")
print(f" Channel : {args.channel}")
print(f" Loss lambda : {args.loss_lambda}")
print("-" * 60)
print(f" Train SNRs (dB) : {train_snr_list}")
print(f" Eval SNR (dB) : {args.snr}")
print("-" * 60)
print(f" Epochs : {args.epochs}")
print(f" Steps/epoch : {args.train_steps_per_epoch}")
print(f" Eval trials : {args.eval_trials}")
if args.train_mode != "joint":
print("-" * 60)
print(f" Inner steps : {args.inner_steps}")
print(f" Inner LR : {args.inner_lr}")
print(f" Meta-batch : {args.meta_batch}")
print(f" Inner keys : {inner_keys}")
print("=" * 60)
# ---- CSV ----
os.makedirs(args.save_dir, exist_ok=True)
csv_path = os.path.join(
args.save_dir,
f"bert_{args.train_mode}_{args.users}U_K{args.mux_factor}.csv"
)
if not os.path.exists(csv_path):
with open(csv_path, "w", newline="") as f:
writer = csv.writer(f)
writer.writerow([
"epoch", "train_mode", "maml_order",
"users", "d_bert", "mux_factor", "d_shared",
"channel", "train_snrs", "eval_snr",
"inner_steps", "inner_lr", "meta_batch",
"avg_loss", "cos_sim", "mse"
])
# ---- Training ----
for ep in range(1, args.epochs + 1):
model.train()
loss_meter = 0.0
for _ in range(args.train_steps_per_epoch):
if args.train_mode == "joint":
b, _ = cache.sample(args.users, device)
snr = float(random.choice(train_snr_list))
b_hat = model(b, snr, args.channel, params=None)
loss, _, _ = semantic_loss(b, b_hat, args.loss_lambda)
opt.zero_grad(set_to_none=True)
loss.backward()
opt.step()
loss_meter += loss.item()
else:
# MAML
if args.meta_batch > len(train_snr_list):
snr_tasks = [float(random.choice(train_snr_list))
for _ in range(args.meta_batch)]
else:
snr_tasks = [float(x) for x in
random.sample(train_snr_list,
args.meta_batch)]
base_params = split_params(model)
meta_loss = torch.tensor(0.0, device=device)
for snr in snr_tasks:
fast_params = {
k: {n: p for n, p in v.items()}
for k, v in base_params.items()
}
for _ in range(args.inner_steps):
b_sup, _ = cache.sample(args.users, device)
b_hat_sup = model(b_sup, snr, args.channel,
params=fast_params)
loss_sup, _, _ = semantic_loss(
b_sup, b_hat_sup, args.loss_lambda)
flat_list, meta_index = gather_inner_params(
fast_params, inner_keys)
create_graph = (args.maml_order == "second")
grads = torch.autograd.grad(
loss_sup, flat_list,
create_graph=create_graph)
fast_params = apply_inner_update(
fast_params, inner_keys, meta_index, grads,
inner_lr=args.inner_lr,
maml_order=args.maml_order)
b_q, _ = cache.sample(args.users, device)
b_hat_q = model(b_q, snr, args.channel,
params=fast_params)
qloss, _, _ = semantic_loss(
b_q, b_hat_q, args.loss_lambda)
meta_loss = meta_loss + qloss
meta_loss = meta_loss / float(args.meta_batch)
opt.zero_grad(set_to_none=True)
meta_loss.backward()
opt.step()
loss_meter += meta_loss.item()
avg_loss = loss_meter / float(args.train_steps_per_epoch)
# ---- Eval ----
cos_sim, mse = evaluate(model, cache, args, device)
print(f"[Epoch {ep:03d}/{args.epochs}] loss={avg_loss:.4f} | "
f"SNR={args.snr:.1f}dB | CosSim={cos_sim:.4f} | "
f"MSE={mse:.4e}")
with open(csv_path, "a", newline="") as f:
writer = csv.writer(f)
writer.writerow([
ep, args.train_mode, args.maml_order,
args.users, d_bert, args.mux_factor, d_shared,
args.channel, train_snr_list, args.snr,
(args.inner_steps if args.train_mode != "joint" else ""),
(args.inner_lr if args.train_mode != "joint" else ""),
(args.meta_batch if args.train_mode != "joint" else ""),
avg_loss, cos_sim, mse
])
print(f"\n✅ Results saved to: {csv_path}")
return model
# =========================================================
# Main
# =========================================================
if __name__ == "__main__":
parser = argparse.ArgumentParser(
description="BERT-based Multi-User Semantic Communication "
"with Expanded Shared Embedding")
# training mode
parser.add_argument("--train-mode",
choices=["joint", "maml_decoder", "maml_full"],
default="joint")
parser.add_argument("--maml-order", choices=["first", "second"],
default="first")
# model
parser.add_argument("--users", type=int, default=4)
parser.add_argument("--mux-factor", type=int, default=4,
help="Shared dim = d_bert * mux_factor "
"(e.g., 4 -> 768*4=3072)")
parser.add_argument("--hidden", type=int, default=512)
# BERT
parser.add_argument("--bert-model", type=str,
default="bert-base-uncased")
parser.add_argument("--bert-batch-size", type=int, default=64)
# data
parser.add_argument("--data-source",
choices=["europarl", "ag_news", "synthetic"],
default="ag_news")
parser.add_argument("--max-sentences", type=int, default=50000)
# channel
parser.add_argument("--channel", choices=["awgn", "rayleigh"],
default="rayleigh")
# loss
parser.add_argument("--loss-lambda", type=float, default=0.5)
# training
parser.add_argument("--epochs", type=int, default=100)
parser.add_argument("--train-steps-per-epoch", type=int, default=2000)
parser.add_argument("--train-snr", type=float, nargs="+",
default=[0, 5, 10, 15, 20, 25])
parser.add_argument("--lr", type=float, default=1e-3)
# MAML
parser.add_argument("--inner-lr", type=float, default=1e-3)
parser.add_argument("--inner-steps", type=int, default=1)
parser.add_argument("--meta-batch", type=int, default=4)
# evaluation
parser.add_argument("--snr", type=float, default=10.0)
parser.add_argument("--eval-trials", type=int, default=5000)
# test-time adaptation
parser.add_argument("--eval-adapt", action="store_true")
parser.add_argument("--eval-adapt-mode",
choices=["maml_decoder", "maml_full"],
default="maml_decoder")
parser.add_argument("--eval-inner-steps", type=int, default=1)
parser.add_argument("--eval-inner-lr", type=float, default=1e-3)
# misc
parser.add_argument("--save-dir", type=str, default="results_bert")
parser.add_argument("--cuda", action="store_true")
parser.add_argument("--seed", type=int, default=0)
parser.add_argument("--debug-maml", action="store_true")
args = parser.parse_args()
train_and_eval(args)
"""
========================
Example Commands
========================
[1] Joint training (U=4, K=4 -> d_shared=3072, Rayleigh)
python3 bert_semcom.py \
--train-mode joint \
--users 4 --mux-factor 4 \
--snr 10 --channel rayleigh \
--data-source ag_news \
--epochs 50 --train-steps-per-epoch 2000 \
--eval-trials 5000 --cuda
[2] Statistical mux gain: U=2, still K=4 (over-provisioned)
python3 bert_semcom.py \
--train-mode joint \
--users 2 --mux-factor 4 \
--snr 10 --channel rayleigh \
--epochs 50 --cuda
[3] Compare mux factors: K=1,2,4,8
for K in 1 2 4 8; do
python3 bert_semcom.py \
--train-mode joint \
--users 4 --mux-factor $K \
--snr 10 --epochs 50 --cuda
done
[4] Decoder-only MAML (U=4, K=4)
python3 bert_semcom.py \
--train-mode maml_decoder --maml-order first \
--users 4 --mux-factor 4 \
--meta-batch 4 --inner-lr 5e-4 --inner-steps 1 \
--snr 10 --epochs 50 --cuda
[5] Full MAML
python3 bert_semcom.py \
--train-mode maml_full --maml-order first \
--users 4 --mux-factor 4 \
--meta-batch 4 --inner-lr 5e-4 --inner-steps 1 \
--snr 10 --epochs 50 --cuda
[6] SNR sweep
for snr in 0 5 10 15 20 25 30; do
python3 bert_semcom.py \
--train-mode joint \
--users 4 --mux-factor 4 \
--snr $snr --epochs 50 --cuda
done
[7] User count comparison (all with K=4)
for u in 2 4 8 16; do
python3 bert_semcom.py \
--train-mode joint \
--users $u --mux-factor 4 \
--snr 10 --epochs 50 --cuda
done
"""
cl_experiments.py
Executable
+538
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@@ -0,0 +1,538 @@
# =========================================================
# cl_experiments.py — IEEE Communications Letters revision
#
# New experiments addressing TVT reviewer comments:
# R2-2 : held-out train/test split (8000/2000, disjoint)
# R1-2 : bandwidth-expansion sweep K = 1, 2, 4, 8 at U = 4
# R1-5c: static random-projection mask baseline (frozen masks)
# R2-3 : ToDMA-style token-domain CS baseline (same channel budget)
# R1-5b: DistilBERT generalization check
# R2-4 : downstream AG News topic accuracy (linear probe)
# R1-3 : runtime latency / parameter count
#
# All evaluations are on the held-out test split.
# Outputs: fig_cl/cl_results.json, fig_cl/cl_convergence.csv
# =========================================================
import argparse, os, json, csv, math, random, time, itertools
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
from bert_semcom import (
BertSemComMux, semantic_loss,
split_params, select_inner_keys, gather_inner_params,
apply_inner_update
)
EVAL_SNRS = (0, 5, 10, 15, 20, 25, 30)
TRAIN_SNRS = [0, 5, 10, 15, 20, 25]
def set_seed(seed=42):
torch.manual_seed(seed)
random.seed(seed)
np.random.seed(seed)
# ---------------------------------------------------------
# Data with labels + disjoint split
# ---------------------------------------------------------
def load_agnews_labeled(n_train=8000, n_test=2000,
min_len=5, max_len=30, seed=42):
from datasets import load_dataset
try:
ds = load_dataset("ag_news", split="train")
except Exception:
ds = load_dataset("fancyzhx/ag_news", split="train")
items = []
for ex in ds:
first = ex["text"].split(".")[0].strip()
w = first.split()
if min_len <= len(w) <= max_len:
items.append((first, int(ex["label"])))
if len(items) >= (n_train + n_test):
break
rng = random.Random(seed)
rng.shuffle(items)
train = items[:n_train]
test = items[n_train:n_train + n_test]
return train, test
class Extractor:
"""Frozen encoder (BERT or DistilBERT), mean-pooled."""
def __init__(self, model_name, device):
from transformers import AutoModel, AutoTokenizer
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModel.from_pretrained(model_name).to(device)
self.model.eval()
self.device = device
self.embed_dim = self.model.config.hidden_size
@torch.no_grad()
def encode(self, texts, max_length=64):
inputs = self.tokenizer(texts, padding=True, truncation=True,
max_length=max_length,
return_tensors="pt").to(self.device)
out = self.model(**inputs)
h = out.last_hidden_state
mask = inputs["attention_mask"].unsqueeze(-1).float()
return (h * mask).sum(1) / mask.sum(1).clamp(min=1.0)
class SplitCache:
"""Train/test embedding caches. Centering mean computed on the
TRAIN pool only and reused for the test split (R2-2)."""
def __init__(self, extractor, train_items, test_items, bs=64):
self.train_texts = [t for t, _ in train_items]
self.train_labels = torch.tensor([l for _, l in train_items])
self.test_texts = [t for t, _ in test_items]
self.test_labels = torch.tensor([l for _, l in test_items])
def enc_all(texts):
embs = []
for i in range(0, len(texts), bs):
embs.append(extractor.encode(texts[i:i + bs]).cpu())
return torch.cat(embs, 0)
print(f"[INFO] Encoding {len(self.train_texts)} train sentences...",
flush=True)
E_tr = enc_all(self.train_texts)
print(f"[INFO] Encoding {len(self.test_texts)} test sentences...",
flush=True)
E_te = enc_all(self.test_texts)
self.mu = E_tr.mean(0, keepdim=True)
self.train = F.normalize(E_tr - self.mu, p=2, dim=-1)
self.test = F.normalize(E_te - self.mu, p=2, dim=-1)
cs = F.cosine_similarity(
self.test[torch.randperm(len(self.test))[:500]],
self.test[torch.randperm(len(self.test))[:500]], dim=-1)
print(f"[INFO] test split: random-pair cos mean={cs.mean():.4f} "
f"std={cs.std():.4f}", flush=True)
def sample_train(self, U, device):
idx = torch.randint(0, len(self.train), (U,))
return self.train[idx].to(device), idx
def sample_test(self, U, device, gen=None):
idx = torch.randint(0, len(self.test), (U,), generator=gen)
return self.test[idx].to(device), idx
# ---------------------------------------------------------
# Training (train pool) + held-out evaluation (test pool)
# ---------------------------------------------------------
def train_config(cache, U, d_bert, K, device, mode="joint",
freeze_masks=False, epochs=200, steps=300,
lr=1e-3, inner_lr=5e-4, inner_steps=1, meta_batch=4,
lam=0.5, channel="rayleigh", conv_trials=50,
label="", seed=42):
set_seed(seed)
d_shared = d_bert * K
model = BertSemComMux(U, d_bert, d_shared, 512).to(device)
if freeze_masks:
model.user_mask.weight.requires_grad_(False)
opt = torch.optim.Adam(
[p for p in model.parameters() if p.requires_grad], lr=lr)
inner_keys = select_inner_keys(mode)
conv = []
t0 = time.time()
for ep in range(1, epochs + 1):
model.train()
loss_sum = 0.0
for _ in range(steps):
if mode == "joint":
b, _ = cache.sample_train(U, device)
snr = float(random.choice(TRAIN_SNRS))
b_hat = model(b, snr, channel)
loss, _, _ = semantic_loss(b, b_hat, lam)
opt.zero_grad(set_to_none=True)
loss.backward()
opt.step()
loss_sum += loss.item()
else:
snr_tasks = [float(x) for x in
random.sample(TRAIN_SNRS, meta_batch)]
base_params = split_params(model)
meta_loss = torch.tensor(0.0, device=device)
for snr in snr_tasks:
fp = {k: {n: p for n, p in v.items()}
for k, v in base_params.items()}
for _ in range(inner_steps):
b_s, _ = cache.sample_train(U, device)
bh = model(b_s, snr, channel, params=fp)
ls, _, _ = semantic_loss(b_s, bh, lam)
fl, mi = gather_inner_params(fp, inner_keys)
grads = torch.autograd.grad(ls, fl)
fp = apply_inner_update(fp, inner_keys, mi, grads,
inner_lr, "first")
b_q, _ = cache.sample_train(U, device)
bh_q = model(b_q, snr, channel, params=fp)
ql, _, _ = semantic_loss(b_q, bh_q, lam)
meta_loss = meta_loss + ql
meta_loss = meta_loss / float(meta_batch)
opt.zero_grad(set_to_none=True)
meta_loss.backward()
opt.step()
loss_sum += meta_loss.item()
# light held-out convergence eval @10 dB
model.eval()
with torch.no_grad():
c = 0.0
for _ in range(conv_trials):
b, _ = cache.sample_test(U, device)
bh = model(b, 10, channel)
c += F.cosine_similarity(bh, b, dim=-1).mean().item()
conv.append({"epoch": ep, "loss": loss_sum / steps,
"cos10": c / conv_trials})
if ep % 20 == 0 or ep == 1 or ep == epochs:
print(f" [{label} Ep {ep:03d}/{epochs}] "
f"loss={loss_sum/steps:.4f} "
f"cos@10dB(test)={c/conv_trials:.4f}", flush=True)
train_time = time.time() - t0
return model, conv, train_time
@torch.no_grad()
def final_eval(model, cache, U, device, channel="rayleigh",
trials=500, collect_at=None, seed=123):
"""Held-out SNR sweep. If collect_at is set (snr list), also return
recovered embeddings + label indices for the linear probe."""
model.eval()
gen = torch.Generator().manual_seed(seed)
out = {}
collected = {}
for snr in EVAL_SNRS:
ct, mt = 0.0, 0.0
rec, idxs = [], []
for _ in range(trials):
b, idx = cache.sample_test(U, device, gen=gen)
bh = model(b, snr, channel)
ct += F.cosine_similarity(bh, b, dim=-1).mean().item()
mt += F.mse_loss(bh, b).item()
if collect_at and snr in collect_at:
rec.append(bh.cpu())
idxs.append(idx)
out[snr] = {"cos": ct / trials, "mse": mt / trials}
if collect_at and snr in collect_at:
collected[snr] = (torch.cat(rec, 0), torch.cat(idxs, 0))
return out, collected
# ---------------------------------------------------------
# Linear probe (AG News 4-class) on clean train embeddings
# ---------------------------------------------------------
def train_probe(cache, device, epochs=300, lr=1e-2):
X = cache.train.to(device)
y = cache.train_labels.to(device)
W = nn.Linear(X.shape[1], 4).to(device)
opt = torch.optim.Adam(W.parameters(), lr=lr)
for _ in range(epochs):
opt.zero_grad()
loss = F.cross_entropy(W(X), y)
loss.backward()
opt.step()
with torch.no_grad():
acc_clean = (W(cache.test.to(device)).argmax(-1).cpu()
== cache.test_labels).float().mean().item()
print(f"[INFO] probe clean test accuracy = {acc_clean:.4f}", flush=True)
return W, acc_clean
@torch.no_grad()
def probe_accuracy(W, collected, cache, device):
accs = {}
for snr, (rec, idx) in collected.items():
pred = W(rec.to(device)).argmax(-1).cpu()
accs[snr] = (pred == cache.test_labels[idx]).float().mean().item()
return accs
# ---------------------------------------------------------
# ToDMA-style token-domain CS baseline (R2-3)
# - shared random Gaussian codebook over the BERT vocabulary
# - per-slot OMP detection (U iterations)
# - genie-aided token-source association (upper bound):
# a user's token is recovered iff it lies in the detected support
# - identical total channel budget: T * L = d_s = 3072 real uses
# - identical per-dimension sum power = 1 and noise var = 1/gamma
# ---------------------------------------------------------
@torch.no_grad()
def todma_eval(extractor, cache, device, U=4, T=24, L=128,
n_frames=200, channel="rayleigh", seed=7,
collect_at=None):
tok = extractor.tokenizer
V = tok.vocab_size
g = torch.Generator().manual_seed(seed)
C = torch.randn(V, L, generator=g)
C = F.normalize(C, p=2, dim=1).to(device) # unit-norm atoms
amp = math.sqrt(L / U) # per-user energy
# pre-tokenize test sentences (no special tokens), truncate to T
tok_ids = [tok(t, add_special_tokens=False)["input_ids"][:T]
for t in cache.test_texts]
results = {}
collected = {}
t_omp_total, n_omp = 0.0, 0
for snr in EVAL_SNRS:
sigma = math.sqrt(1.0 / (10 ** (snr / 10.0)))
cs_sum, n_sent = 0.0, 0
tok_err_sum, tok_cnt = 0, 0
rec_all, idx_all = [], []
rng = torch.Generator().manual_seed(seed + snr)
for fr in range(n_frames):
idx = torch.randint(0, len(cache.test), (U,), generator=rng)
seqs = [tok_ids[i] for i in idx.tolist()]
if channel == "rayleigh":
hr = torch.randn(U, generator=rng)
hi = torch.randn(U, generator=rng)
h = torch.sqrt(hr ** 2 + hi ** 2) / math.sqrt(2.0)
else:
h = torch.ones(U)
h = h.to(device)
det_ids = [[] for _ in range(U)]
t1 = time.time()
for t in range(T):
active = [(u, seqs[u][t]) for u in range(U)
if t < len(seqs[u])]
if not active:
break
y = torch.zeros(L, device=device)
for u, tid in active:
y = y + h[u] * amp * C[tid]
y = y + sigma * torch.randn(L, device=device)
# OMP: U iterations
residual = y.clone()
support = []
for _ in range(min(U, len(active))):
corr = torch.mv(C, residual).abs()
if support:
corr[torch.tensor(support, device=device)] = -1
k = int(corr.argmax().item())
support.append(k)
A = C[support].T * amp # (L, |S|)
coef, *_ = torch.linalg.lstsq(A, y.unsqueeze(1))
residual = y - (A @ coef).squeeze(1)
sset = set(support)
for u, tid in active:
tok_cnt += 1
if tid in sset:
det_ids[u].append(tid) # genie assoc.
else:
tok_err_sum += 1 # erasure
t_omp_total += time.time() - t1
n_omp += 1
texts = [tok.decode(d) if d else "[UNK]" for d in det_ids]
emb = extractor.encode(texts).cpu()
emb = F.normalize(emb - cache.mu, p=2, dim=-1)
ref = cache.test[idx]
cs_sum += F.cosine_similarity(emb, ref, dim=-1).sum().item()
n_sent += U
if collect_at and snr in collect_at:
rec_all.append(emb)
idx_all.append(idx)
results[snr] = {"cos": cs_sum / n_sent,
"token_err": tok_err_sum / max(tok_cnt, 1)}
if collect_at and snr in collect_at:
collected[snr] = (torch.cat(rec_all, 0), torch.cat(idx_all, 0))
print(f" [ToDMA T={T} L={L}] SNR={snr} "
f"cos={results[snr]['cos']:.4f} "
f"tokErr={results[snr]['token_err']:.4f}", flush=True)
results["omp_ms_per_frame"] = 1000.0 * t_omp_total / max(n_omp, 1)
return results, collected
# ---------------------------------------------------------
# Latency / parameter count (R1-3)
# ---------------------------------------------------------
@torch.no_grad()
def measure_latency(model, cache, U, device, n=200):
b, _ = cache.sample_test(U, device)
for _ in range(20):
model(b, 10, "rayleigh")
if device.type == "cuda":
torch.cuda.synchronize()
t0 = time.time()
for _ in range(n):
model(b, 10, "rayleigh")
if device.type == "cuda":
torch.cuda.synchronize()
ms = 1000.0 * (time.time() - t0) / n
n_params = sum(p.numel() for p in model.parameters())
return ms, n_params
@torch.no_grad()
def measure_bert_latency(extractor, texts, n=50):
for _ in range(5):
extractor.encode(texts[:4])
if extractor.device == "cuda" or (hasattr(extractor.device, "type")
and extractor.device.type == "cuda"):
torch.cuda.synchronize()
t0 = time.time()
for i in range(n):
extractor.encode([texts[i % len(texts)]])
torch.cuda.synchronize()
return 1000.0 * (time.time() - t0) / n
# ---------------------------------------------------------
# Main
# ---------------------------------------------------------
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--save-dir", default="fig_cl")
ap.add_argument("--epochs", type=int, default=200)
ap.add_argument("--steps", type=int, default=300)
ap.add_argument("--trials", type=int, default=500)
ap.add_argument("--todma-frames", type=int, default=200)
ap.add_argument("--skip-distil", action="store_true")
args = ap.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.save_dir, exist_ok=True)
print(f"[Device: {device}]", flush=True)
train_items, test_items = load_agnews_labeled()
print(f"[INFO] split: {len(train_items)} train / "
f"{len(test_items)} test", flush=True)
bert = Extractor("bert-base-uncased", device)
d_bert = bert.embed_dim
cache = SplitCache(bert, train_items, test_items)
R = {"meta": {"epochs": args.epochs, "steps": args.steps,
"trials": args.trials}}
conv_rows = []
# linear probe on clean train embeddings
probe, acc_clean = train_probe(cache, device)
R["probe_clean_acc"] = acc_clean
PROBE_SNRS = list(EVAL_SNRS)
# ---- (1) orthogonal baseline U=1, d=768 ----
# ---- (2) proposed U=1..6, K=4 ----
# ---- (3) K sweep U=4, K in {1,2,8} ----
# ---- (4) random frozen masks U=4, K=4 ----
# ---- (5) MAML full U=4, K=4 ----
configs = [
("baseline_U1_K1", dict(U=1, K=1)),
("prop_U1_K4", dict(U=1, K=4)),
("prop_U2_K4", dict(U=2, K=4)),
("prop_U3_K4", dict(U=3, K=4)),
("prop_U4_K4", dict(U=4, K=4)),
("prop_U5_K4", dict(U=5, K=4)),
("prop_U6_K4", dict(U=6, K=4)),
("ksweep_U4_K1", dict(U=4, K=1)),
("ksweep_U4_K2", dict(U=4, K=2)),
("ksweep_U4_K8", dict(U=4, K=8)),
("randmask_U4_K4", dict(U=4, K=4, freeze_masks=True)),
("maml_U4_K4", dict(U=4, K=4, mode="maml_full")),
]
masks_store = {}
for name, kw in configs:
print(f"\n=== {name} ===", flush=True)
U, K = kw.pop("U"), kw.pop("K")
model, conv, ttime = train_config(
cache, U, d_bert, K, device,
epochs=args.epochs, steps=args.steps, label=name, **kw)
collect = PROBE_SNRS if name in (
"baseline_U1_K1", "prop_U4_K4", "randmask_U4_K4") else None
sweep, collected = final_eval(model, cache, U, device,
trials=args.trials,
collect_at=collect)
entry = {"U": U, "K": K, "train_s": ttime,
"snr": {str(s): sweep[s] for s in EVAL_SNRS}}
if collect:
entry["probe_acc"] = {str(s): a for s, a in
probe_accuracy(probe, collected,
cache, device).items()}
if name in ("prop_U2_K4", "prop_U3_K4", "prop_U4_K4"):
m = model.user_mask.weight.detach().cpu()
mn = F.normalize(m, p=2, dim=1)
masks_store[name] = (mn @ mn.T).numpy().tolist()
if name == "prop_U4_K4":
ms_gpu, n_params = measure_latency(model, cache, U, device)
cpu_model = BertSemComMux(U, d_bert, d_bert * K, 512)
cpu_model.load_state_dict(model.state_dict())
cpu_dev = torch.device("cpu")
b_cpu, _ = cache.sample_test(U, cpu_dev)
for _ in range(10):
cpu_model(b_cpu, 10, "rayleigh")
t0 = time.time()
for _ in range(50):
cpu_model(b_cpu, 10, "rayleigh")
ms_cpu = 1000.0 * (time.time() - t0) / 50
R["latency"] = {"proposed_gpu_ms": ms_gpu,
"proposed_cpu_ms": ms_cpu,
"params": n_params}
print(f"[LATENCY] proposed frame: {ms_gpu:.2f} ms (GPU) "
f"{ms_cpu:.2f} ms (CPU), params={n_params/1e6:.2f}M",
flush=True)
R[name] = entry
for c in conv:
conv_rows.append([name, c["epoch"], c["loss"], c["cos10"]])
with open(os.path.join(args.save_dir, "cl_results.json"), "w") as f:
json.dump(R, f, indent=1)
R["mask_corr"] = masks_store
# ---- (6) ToDMA-style baseline, two budget splits ----
print("\n=== ToDMA-style baseline ===", flush=True)
R["bert_tx_ms"] = measure_bert_latency(bert, cache.test_texts)
print(f"[LATENCY] BERT encode per sentence: {R['bert_tx_ms']:.1f} ms",
flush=True)
for (T, L) in [(24, 128), (16, 192)]:
res, coll = todma_eval(bert, cache, device, U=4, T=T, L=L,
n_frames=args.todma_frames,
collect_at=PROBE_SNRS if T == 24 else None)
key = f"todma_T{T}_L{L}"
R[key] = {str(s): res[s] for s in EVAL_SNRS}
R[key]["omp_ms_per_frame"] = res["omp_ms_per_frame"]
if coll:
R[key]["probe_acc"] = {str(s): a for s, a in
probe_accuracy(probe, coll,
cache, device).items()}
with open(os.path.join(args.save_dir, "cl_results.json"), "w") as f:
json.dump(R, f, indent=1)
# ---- (7) DistilBERT generalization check ----
if not args.skip_distil:
print("\n=== DistilBERT check (U=4, K=4) ===", flush=True)
distil = Extractor("distilbert-base-uncased", device)
dcache = SplitCache(distil, train_items, test_items)
model, conv, _ = train_config(dcache, 4, distil.embed_dim, 4,
device, epochs=args.epochs,
steps=args.steps, label="distil")
sweep, _ = final_eval(model, dcache, 4, device, trials=args.trials)
R["distil_U4_K4"] = {"snr": {str(s): sweep[s] for s in EVAL_SNRS}}
for c in conv:
conv_rows.append(["distil_U4_K4", c["epoch"], c["loss"],
c["cos10"]])
with open(os.path.join(args.save_dir, "cl_results.json"), "w") as f:
json.dump(R, f, indent=1)
with open(os.path.join(args.save_dir, "cl_convergence.csv"), "w",
newline="") as f:
w = csv.writer(f)
w.writerow(["config", "epoch", "loss", "cos10_test"])
w.writerows(conv_rows)
print("\nAll CL experiments complete.", flush=True)
if __name__ == "__main__":
main()
cl_maml_all.py
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# =========================================================
# cl_maml_all.py — promote SNR-aware MAML to the default
# training procedure for all reported configurations.
#
# Adds MAML-trained counterparts of the load sweep, the
# conventional orthogonal scheme (fairness: both sides at
# their best), and the random-mask variant. The K sweep and
# DistilBERT stay joint-trained (sensitivity studies).
#
# Held-out evaluation identical to cl_experiments.py.
# Output: fig_cl/cl_results_maml.json (+ convergence CSV)
# =========================================================
import argparse, os, json, csv, time, random
import torch
import torch.nn.functional as F
from bert_semcom import (
BertSemComMux, semantic_loss, split_params,
gather_inner_params, apply_inner_update
)
from cl_experiments import (
load_agnews_labeled, Extractor, SplitCache,
final_eval, train_probe, probe_accuracy, set_seed,
EVAL_SNRS, TRAIN_SNRS, measure_latency
)
def train_maml(cache, U, d_bert, K, device, freeze_masks=False,
epochs=200, steps=300, lr=1e-3, inner_lr=5e-4,
meta_batch=4, lam=0.5, channel="rayleigh",
conv_trials=50, label="", seed=42):
set_seed(seed)
model = BertSemComMux(U, d_bert, d_bert * K, 512).to(device)
if freeze_masks:
model.user_mask.weight.requires_grad_(False)
inner_keys = ["tx_proj", "user_query", "rx_proj"]
else:
inner_keys = ["tx_proj", "user_mask", "user_query", "rx_proj"]
opt = torch.optim.Adam(
[p for p in model.parameters() if p.requires_grad], lr=lr)
conv = []
t0 = time.time()
for ep in range(1, epochs + 1):
model.train()
loss_sum = 0.0
for _ in range(steps):
snr_tasks = [float(x) for x in
random.sample(TRAIN_SNRS, meta_batch)]
base_params = split_params(model)
meta_loss = torch.tensor(0.0, device=device)
for snr in snr_tasks:
fp = {k: {n: p for n, p in v.items()}
for k, v in base_params.items()}
b_s, _ = cache.sample_train(U, device)
bh = model(b_s, snr, channel, params=fp)
ls, _, _ = semantic_loss(b_s, bh, lam)
fl, mi = gather_inner_params(fp, inner_keys)
grads = torch.autograd.grad(ls, fl)
fp = apply_inner_update(fp, inner_keys, mi, grads,
inner_lr, "first")
b_q, _ = cache.sample_train(U, device)
bh_q = model(b_q, snr, channel, params=fp)
ql, _, _ = semantic_loss(b_q, bh_q, lam)
meta_loss = meta_loss + ql
meta_loss = meta_loss / float(meta_batch)
opt.zero_grad(set_to_none=True)
meta_loss.backward()
opt.step()
loss_sum += meta_loss.item()
model.eval()
with torch.no_grad():
c = 0.0
for _ in range(conv_trials):
b, _ = cache.sample_test(U, device)
bh = model(b, 10, channel)
c += F.cosine_similarity(bh, b, dim=-1).mean().item()
conv.append({"epoch": ep, "loss": loss_sum / steps,
"cos10": c / conv_trials})
if ep % 20 == 0 or ep == 1 or ep == epochs:
print(f" [{label} Ep {ep:03d}/{epochs}] "
f"loss={loss_sum/steps:.4f} "
f"cos@10dB(test)={c/conv_trials:.4f}", flush=True)
return model, conv, time.time() - t0
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--save-dir", default="fig_cl")
ap.add_argument("--epochs", type=int, default=200)
ap.add_argument("--steps", type=int, default=300)
ap.add_argument("--trials", type=int, default=500)
args = ap.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.save_dir, exist_ok=True)
print(f"[Device: {device}]", flush=True)
train_items, test_items = load_agnews_labeled()
bert = Extractor("bert-base-uncased", device)
d_bert = bert.embed_dim
cache = SplitCache(bert, train_items, test_items)
del bert
torch.cuda.empty_cache()
probe, acc_clean = train_probe(cache, device)
R = {"probe_clean_acc": acc_clean}
PROBE_SNRS = list(EVAL_SNRS)
conv_rows = []
configs = [
("mamlB_U1_K1", dict(U=1, K=1)),
("mamlP_U1_K4", dict(U=1, K=4)),
("mamlP_U2_K4", dict(U=2, K=4)),
("mamlP_U3_K4", dict(U=3, K=4)),
("mamlP_U5_K4", dict(U=5, K=4)),
("mamlP_U6_K4", dict(U=6, K=4)),
("mamlR_U4_K4", dict(U=4, K=4, freeze_masks=True)),
]
masks_store = {}
for name, kw in configs:
print(f"\n=== {name} ===", flush=True)
U, K = kw.pop("U"), kw.pop("K")
model, conv, ttime = train_maml(
cache, U, d_bert, K, device,
epochs=args.epochs, steps=args.steps, label=name, **kw)
collect = PROBE_SNRS if name in (
"mamlB_U1_K1", "mamlR_U4_K4") else None
sweep, collected = final_eval(model, cache, U, device,
trials=args.trials,
collect_at=collect)
entry = {"U": U, "K": K, "train_s": ttime,
"snr": {str(s): sweep[s] for s in EVAL_SNRS}}
if collect:
entry["probe_acc"] = {str(s): a for s, a in
probe_accuracy(probe, collected,
cache, device).items()}
if name in ("mamlP_U2_K4", "mamlP_U3_K4"):
m = model.user_mask.weight.detach().cpu()
mn = F.normalize(m, p=2, dim=1)
masks_store[name] = (mn @ mn.T).numpy().tolist()
R[name] = entry
for c in conv:
conv_rows.append([name, c["epoch"], c["loss"], c["cos10"]])
R["mask_corr"] = masks_store
with open(os.path.join(args.save_dir,
"cl_results_maml.json"), "w") as f:
json.dump(R, f, indent=1)
# probe accuracies for the already-trained maml_U4_K4 are collected
# by re-training? No — retrain U=4 MAML for probe collection and
# mask correlation so every reported number comes from one protocol.
print("\n=== mamlP_U4_K4 (retrain for probe/masks) ===", flush=True)
model, conv, ttime = train_maml(cache, 4, d_bert, 4, device,
epochs=args.epochs, steps=args.steps,
label="mamlP_U4_K4")
sweep, collected = final_eval(model, cache, 4, device,
trials=args.trials,
collect_at=PROBE_SNRS)
entry = {"U": 4, "K": 4, "train_s": ttime,
"snr": {str(s): sweep[s] for s in EVAL_SNRS}}
entry["probe_acc"] = {str(s): a for s, a in
probe_accuracy(probe, collected,
cache, device).items()}
m = model.user_mask.weight.detach().cpu()
mn = F.normalize(m, p=2, dim=1)
masks_store["mamlP_U4_K4"] = (mn @ mn.T).numpy().tolist()
ms_gpu, n_params = measure_latency(model, cache, 4, device)
entry["lat_gpu_ms"] = ms_gpu
entry["params"] = n_params
R["mamlP_U4_K4"] = entry
R["mask_corr"] = masks_store
for c in conv:
conv_rows.append(["mamlP_U4_K4", c["epoch"], c["loss"],
c["cos10"]])
with open(os.path.join(args.save_dir, "cl_results_maml.json"),
"w") as f:
json.dump(R, f, indent=1)
with open(os.path.join(args.save_dir, "cl_convergence_maml.csv"),
"w", newline="") as f:
w = csv.writer(f)
w.writerow(["config", "epoch", "loss", "cos10_test"])
w.writerows(conv_rows)
print("\nAll MAML-default experiments complete.", flush=True)
if __name__ == "__main__":
main()
cl_maml_extra.py
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# cl_maml_extra.py — MAML-trained K sweep (K=1,2,8) and DistilBERT
# replication, completing the unified MAML protocol.
import argparse, os, json, csv
import torch
import torch.nn.functional as F
from cl_experiments import (
load_agnews_labeled, Extractor, SplitCache, final_eval, set_seed,
EVAL_SNRS
)
from cl_maml_all import train_maml
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--save-dir", default="fig_cl")
ap.add_argument("--epochs", type=int, default=200)
ap.add_argument("--steps", type=int, default=300)
ap.add_argument("--trials", type=int, default=500)
args = ap.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.save_dir, exist_ok=True)
print(f"[Device: {device}]", flush=True)
train_items, test_items = load_agnews_labeled()
bert = Extractor("bert-base-uncased", device)
d_bert = bert.embed_dim
cache = SplitCache(bert, train_items, test_items)
del bert
torch.cuda.empty_cache()
R = {}
conv_rows = []
for name, K in [("mamlK_U4_K1", 1), ("mamlK_U4_K2", 2),
("mamlK_U4_K8", 8)]:
print(f"\n=== {name} ===", flush=True)
model, conv, ttime = train_maml(cache, 4, d_bert, K, device,
epochs=args.epochs,
steps=args.steps, label=name)
sweep, _ = final_eval(model, cache, 4, device, trials=args.trials)
R[name] = {"U": 4, "K": K, "train_s": ttime,
"snr": {str(s): sweep[s] for s in EVAL_SNRS}}
for c in conv:
conv_rows.append([name, c["epoch"], c["loss"], c["cos10"]])
with open(os.path.join(args.save_dir,
"cl_results_maml2.json"), "w") as f:
json.dump(R, f, indent=1)
print("\n=== mamlD_U4_K4 (DistilBERT) ===", flush=True)
distil = Extractor("distilbert-base-uncased", device)
dcache = SplitCache(distil, train_items, test_items)
d_d = distil.embed_dim
del distil
torch.cuda.empty_cache()
model, conv, ttime = train_maml(dcache, 4, d_d, 4, device,
epochs=args.epochs,
steps=args.steps, label="mamlD")
sweep, _ = final_eval(model, dcache, 4, device, trials=args.trials)
R["mamlD_U4_K4"] = {"U": 4, "K": 4, "train_s": ttime,
"snr": {str(s): sweep[s] for s in EVAL_SNRS}}
for c in conv:
conv_rows.append(["mamlD_U4_K4", c["epoch"], c["loss"],
c["cos10"]])
with open(os.path.join(args.save_dir, "cl_results_maml2.json"),
"w") as f:
json.dump(R, f, indent=1)
with open(os.path.join(args.save_dir, "cl_convergence_maml2.csv"),
"w", newline="") as f:
w = csv.writer(f)
w.writerow(["config", "epoch", "loss", "cos10_test"])
w.writerows(conv_rows)
print("\nExtra MAML experiments complete.", flush=True)
if __name__ == "__main__":
main()
cl_todma_load.py
Executable
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# cl_todma_load.py — ToDMA load sweep (evaluation only, no training).
#
# Evaluates the ToDMA token-domain scheme (T=24, L=128, genie-aided
# association) for U in {1,2,3,5,6} on the held-out test set, matching
# the U=4 run stored in cl_results.json, so that Fig. 3 can show the
# ToDMA aggregate fidelity across load.
import argparse, os, json
import torch
from cl_experiments import (
load_agnews_labeled, Extractor, SplitCache, todma_eval, EVAL_SNRS
)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--save-dir", default="fig_cl")
ap.add_argument("--frames", type=int, default=200)
args = ap.parse_args()
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
os.makedirs(args.save_dir, exist_ok=True)
print(f"[Device: {device}]", flush=True)
train_items, test_items = load_agnews_labeled()
bert = Extractor("bert-base-uncased", device)
cache = SplitCache(bert, train_items, test_items)
R = {}
for U in [1, 2, 3, 5, 6]:
print(f"\n=== ToDMA U={U} (T=24, L=128) ===", flush=True)
res, _ = todma_eval(bert, cache, device, U=U, T=24, L=128,
n_frames=args.frames)
key = f"todma_U{U}_T24_L128"
R[key] = {str(s): res[s] for s in EVAL_SNRS}
with open(os.path.join(args.save_dir,
"cl_results_todma_u.json"), "w") as f:
json.dump(R, f, indent=1)
print("\nToDMA load sweep complete.", flush=True)
if __name__ == "__main__":
main()
fig_cl/cl_convergence.csv
Executable
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fig_cl/cl_convergence_maml.csv
Executable
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fig_cl/cl_convergence_maml2.csv
Executable
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config,epoch,loss,cos10_test
mamlK_U4_K1,1,0.3395769918461641,0.5970553267002106
mamlK_U4_K1,2,0.21661824176708858,0.663407347202301
mamlK_U4_K1,3,0.19135330920418103,0.6789607079327107
mamlK_U4_K1,4,0.1688267623881499,0.7170000022649765
mamlK_U4_K1,5,0.1635581833620866,0.7278023535013198
mamlK_U4_K1,6,0.15552770368754865,0.7321064358949662
mamlK_U4_K1,7,0.14887250925103823,0.6956483569741249
mamlK_U4_K1,8,0.14149590991437436,0.7095583683252334
mamlK_U4_K1,9,0.13910339325666427,0.7492142748832703
mamlK_U4_K1,10,0.1388827312240998,0.7676383137702942
mamlK_U4_K1,11,0.13788577896853288,0.7622184693813324
mamlK_U4_K1,12,0.13349383287131786,0.7443693269789219
mamlK_U4_K1,13,0.1310288365681966,0.7836207801103592
mamlK_U4_K1,14,0.12814960218966007,0.7553350067138672
mamlK_U4_K1,15,0.12337705274422964,0.7800710442662239
mamlK_U4_K1,16,0.1274088058869044,0.7629242312908172
mamlK_U4_K1,17,0.12006955457230409,0.7605716925859451
mamlK_U4_K1,18,0.12000012544294199,0.7955571794509888
mamlK_U4_K1,19,0.12327828153967857,0.7970682632923126
mamlK_U4_K1,20,0.1210594833890597,0.7731149177253246
mamlK_U4_K1,21,0.12155996484061082,0.756924899816513
mamlK_U4_K1,22,0.12020212426781654,0.7779450225830078
mamlK_U4_K1,23,0.11663664345939954,0.8017365336418152
mamlK_U4_K1,24,0.11489173091948032,0.7824297499656677
mamlK_U4_K1,25,0.11486184932291507,0.794362416267395
mamlK_U4_K1,26,0.1178030712902546,0.8119315755367279
mamlK_U4_K1,27,0.11580229875942072,0.797225725799799
mamlK_U4_K1,28,0.10796747016410033,0.8044077944755554
mamlK_U4_K1,29,0.11533101240793864,0.7961653138697148
mamlK_U4_K1,30,0.11183028814693292,0.7978107976913452
mamlK_U4_K1,31,0.11258448326339324,0.7875527828931809
mamlK_U4_K1,32,0.10674354664981366,0.807917720079422
mamlK_U4_K1,33,0.10819576853265366,0.807927662730217
mamlK_U4_K1,34,0.11045404922217131,0.7892514216899872
mamlK_U4_K1,35,0.11241467177867889,0.7921020865440369
mamlK_U4_K1,36,0.10932925939559937,0.7903496680408716
mamlK_U4_K1,37,0.10381663547207912,0.7668627226352691
mamlK_U4_K1,38,0.1071135958780845,0.7730690225958824
mamlK_U4_K1,39,0.10774661825348933,0.8059287357330323
mamlK_U4_K1,40,0.10857624676078557,0.83376913189888
mamlK_U4_K1,41,0.10950486143430074,0.7742104935646057
mamlK_U4_K1,42,0.10545750390738248,0.810519278049469
mamlK_U4_K1,43,0.1052815372745196,0.8057966285943985
mamlK_U4_K1,44,0.10903738483786583,0.8267235445976258
mamlK_U4_K1,45,0.10730494531492392,0.7892061904072761
mamlK_U4_K1,46,0.10393029546986024,0.7977983325719833
mamlK_U4_K1,47,0.10761934834221999,0.823192930817604
mamlK_U4_K1,48,0.10633286371827126,0.7915622889995575
mamlK_U4_K1,49,0.10852537066986163,0.8068294554948807
mamlK_U4_K1,50,0.10527669581274192,0.8049935579299927
mamlK_U4_K1,51,0.10183198112994432,0.8283192348480225
mamlK_U4_K1,52,0.106179664482673,0.8038868260383606
mamlK_U4_K1,53,0.11007975281526645,0.7701723985373974
mamlK_U4_K1,54,0.10454111352562905,0.8153893089294434
mamlK_U4_K1,55,0.10165571812540293,0.8099441200494766
mamlK_U4_K1,56,0.10291118039439122,0.8030243682861328
mamlK_U4_K1,57,0.10704815806200108,0.8102884113788604
mamlK_U4_K1,58,0.10171569392085075,0.7968954205513
mamlK_U4_K1,59,0.10292136568576098,0.7862212884426117
mamlK_U4_K1,60,0.10662949436654647,0.7925063616037369
mamlK_U4_K1,61,0.10734868448227644,0.7984207433462143
mamlK_U4_K1,62,0.10304167098055284,0.8217874234914779
mamlK_U4_K1,63,0.10521399758756161,0.8244336760044098
mamlK_U4_K1,64,0.10094862267374992,0.8220500147342682
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mamlD_U4_K4,196,0.04879093502337734,0.927699345946312
mamlD_U4_K4,197,0.04918769733980298,0.9293132257461548
mamlD_U4_K4,198,0.050241120532155036,0.9447884798049927
mamlD_U4_K4,199,0.04578717742115259,0.930838119983673
mamlD_U4_K4,200,0.05384820915137728,0.911318576335907
1 config epoch loss cos10_test
2 mamlK_U4_K1 1 0.3395769918461641 0.5970553267002106
3 mamlK_U4_K1 2 0.21661824176708858 0.663407347202301
4 mamlK_U4_K1 3 0.19135330920418103 0.6789607079327107
5 mamlK_U4_K1 4 0.1688267623881499 0.7170000022649765
6 mamlK_U4_K1 5 0.1635581833620866 0.7278023535013198
7 mamlK_U4_K1 6 0.15552770368754865 0.7321064358949662
8 mamlK_U4_K1 7 0.14887250925103823 0.6956483569741249
9 mamlK_U4_K1 8 0.14149590991437436 0.7095583683252334
10 mamlK_U4_K1 9 0.13910339325666427 0.7492142748832703
11 mamlK_U4_K1 10 0.1388827312240998 0.7676383137702942
12 mamlK_U4_K1 11 0.13788577896853288 0.7622184693813324
13 mamlK_U4_K1 12 0.13349383287131786 0.7443693269789219
14 mamlK_U4_K1 13 0.1310288365681966 0.7836207801103592
15 mamlK_U4_K1 14 0.12814960218966007 0.7553350067138672
16 mamlK_U4_K1 15 0.12337705274422964 0.7800710442662239
17 mamlK_U4_K1 16 0.1274088058869044 0.7629242312908172
18 mamlK_U4_K1 17 0.12006955457230409 0.7605716925859451
19 mamlK_U4_K1 18 0.12000012544294199 0.7955571794509888
20 mamlK_U4_K1 19 0.12327828153967857 0.7970682632923126
21 mamlK_U4_K1 20 0.1210594833890597 0.7731149177253246
22 mamlK_U4_K1 21 0.12155996484061082 0.756924899816513
23 mamlK_U4_K1 22 0.12020212426781654 0.7779450225830078
24 mamlK_U4_K1 23 0.11663664345939954 0.8017365336418152
25 mamlK_U4_K1 24 0.11489173091948032 0.7824297499656677
26 mamlK_U4_K1 25 0.11486184932291507 0.794362416267395
27 mamlK_U4_K1 26 0.1178030712902546 0.8119315755367279
28 mamlK_U4_K1 27 0.11580229875942072 0.797225725799799
29 mamlK_U4_K1 28 0.10796747016410033 0.8044077944755554
30 mamlK_U4_K1 29 0.11533101240793864 0.7961653138697148
31 mamlK_U4_K1 30 0.11183028814693292 0.7978107976913452
32 mamlK_U4_K1 31 0.11258448326339324 0.7875527828931809
33 mamlK_U4_K1 32 0.10674354664981366 0.807917720079422
34 mamlK_U4_K1 33 0.10819576853265366 0.807927662730217
35 mamlK_U4_K1 34 0.11045404922217131 0.7892514216899872
36 mamlK_U4_K1 35 0.11241467177867889 0.7921020865440369
37 mamlK_U4_K1 36 0.10932925939559937 0.7903496680408716
38 mamlK_U4_K1 37 0.10381663547207912 0.7668627226352691
39 mamlK_U4_K1 38 0.1071135958780845 0.7730690225958824
40 mamlK_U4_K1 39 0.10774661825348933 0.8059287357330323
41 mamlK_U4_K1 40 0.10857624676078557 0.83376913189888
42 mamlK_U4_K1 41 0.10950486143430074 0.7742104935646057
43 mamlK_U4_K1 42 0.10545750390738248 0.810519278049469
44 mamlK_U4_K1 43 0.1052815372745196 0.8057966285943985
45 mamlK_U4_K1 44 0.10903738483786583 0.8267235445976258
46 mamlK_U4_K1 45 0.10730494531492392 0.7892061904072761
47 mamlK_U4_K1 46 0.10393029546986024 0.7977983325719833
48 mamlK_U4_K1 47 0.10761934834221999 0.823192930817604
49 mamlK_U4_K1 48 0.10633286371827126 0.7915622889995575
50 mamlK_U4_K1 49 0.10852537066986163 0.8068294554948807
51 mamlK_U4_K1 50 0.10527669581274192 0.8049935579299927
52 mamlK_U4_K1 51 0.10183198112994432 0.8283192348480225
53 mamlK_U4_K1 52 0.106179664482673 0.8038868260383606
54 mamlK_U4_K1 53 0.11007975281526645 0.7701723985373974
55 mamlK_U4_K1 54 0.10454111352562905 0.8153893089294434
56 mamlK_U4_K1 55 0.10165571812540293 0.8099441200494766
57 mamlK_U4_K1 56 0.10291118039439122 0.8030243682861328
58 mamlK_U4_K1 57 0.10704815806200108 0.8102884113788604
59 mamlK_U4_K1 58 0.10171569392085075 0.7968954205513
60 mamlK_U4_K1 59 0.10292136568576098 0.7862212884426117
61 mamlK_U4_K1 60 0.10662949436654647 0.7925063616037369
62 mamlK_U4_K1 61 0.10734868448227644 0.7984207433462143
63 mamlK_U4_K1 62 0.10304167098055284 0.8217874234914779
64 mamlK_U4_K1 63 0.10521399758756161 0.8244336760044098
65 mamlK_U4_K1 64 0.10094862267374992 0.8220500147342682
66 mamlK_U4_K1 65 0.1031661332398653 0.8266524529457092
67 mamlK_U4_K1 66 0.10353197903682788 0.8127677541971207
68 mamlK_U4_K1 67 0.10081918518990278 0.8079964489489794
69 mamlK_U4_K1 68 0.10363816128422816 0.8064543125033379
70 mamlK_U4_K1 69 0.10104108888655901 0.8417371332645416
71 mamlK_U4_K1 70 0.10481970300277074 0.8107023566961289
72 mamlK_U4_K1 71 0.10354454388221104 0.806251373887062
73 mamlK_U4_K1 72 0.10152822078516086 0.8281261730194092
74 mamlK_U4_K1 73 0.10400646410882473 0.7800723713636398
75 mamlK_U4_K1 74 0.10384751554578543 0.8151430320739746
76 mamlK_U4_K1 75 0.10166229440520207 0.8314117884635925
77 mamlK_U4_K1 76 0.10101484221716722 0.8196522581577301
78 mamlK_U4_K1 77 0.10100059510519108 0.8323004174232483
79 mamlK_U4_K1 78 0.10188165108362833 0.8349729001522064
80 mamlK_U4_K1 79 0.10232309172550837 0.8102788186073303
81 mamlK_U4_K1 80 0.10161228114118179 0.8061175668239593
82 mamlK_U4_K1 81 0.10679626700778802 0.7957542228698731
83 mamlK_U4_K1 82 0.09985721929619709 0.8155868190526963
84 mamlK_U4_K1 83 0.10131263419985771 0.8271038043498993
85 mamlK_U4_K1 84 0.10077717038492362 0.7987181535363197
86 mamlK_U4_K1 85 0.10207743760198355 0.802321862578392
87 mamlK_U4_K1 86 0.09966132553915183 0.7935655745863914
88 mamlK_U4_K1 87 0.09967112574726343 0.8238284683227539
89 mamlK_U4_K1 88 0.10276158565034468 0.8025999909639359
90 mamlK_U4_K1 89 0.10255109138786793 0.8074301856756211
91 mamlK_U4_K1 90 0.10022635678450266 0.7628305122256279
92 mamlK_U4_K1 91 0.10245673291385174 0.8166504371166229
93 mamlK_U4_K1 92 0.09916387628763915 0.8074478885531425
94 mamlK_U4_K1 93 0.10233928474287192 0.841915146112442
95 mamlK_U4_K1 94 0.10203774685660998 0.8012312103807926
96 mamlK_U4_K1 95 0.1000142144287626 0.8060261923074722
97 mamlK_U4_K1 96 0.10193064155677954 0.8143401873111725
98 mamlK_U4_K1 97 0.09882214875270923 0.8182688345015049
99 mamlK_U4_K1 98 0.10450279959787925 0.8467787909507751
100 mamlK_U4_K1 99 0.10176315414408843 0.7878685969673097
101 mamlK_U4_K1 100 0.1001277572909991 0.8041835770010948
102 mamlK_U4_K1 101 0.10305923895289501 0.8290009769797325
103 mamlK_U4_K1 102 0.10232010785490274 0.8266846370697022
104 mamlK_U4_K1 103 0.10178076980014641 0.8354748725891114
105 mamlK_U4_K1 104 0.09648833476006985 0.8330800247192383
106 mamlK_U4_K1 105 0.10118337649852037 0.8440955173969269
107 mamlK_U4_K1 106 0.10032680181165536 0.8250223326683045
108 mamlK_U4_K1 107 0.09837850441535313 0.8044747364521027
109 mamlK_U4_K1 108 0.10179255776107311 0.8232489228248596
110 mamlK_U4_K1 109 0.10068597142895062 0.821098415851593
111 mamlK_U4_K1 110 0.10075642358511687 0.8095153947174549
112 mamlK_U4_K1 111 0.1003301202878356 0.7819948774576188
113 mamlK_U4_K1 112 0.09819000442822774 0.7827690821886063
114 mamlK_U4_K1 113 0.0978364572301507 0.8342666244506836
115 mamlK_U4_K1 114 0.10085415149728458 0.8147234088182449
116 mamlK_U4_K1 115 0.09945026468485593 0.8218076515197754
117 mamlK_U4_K1 116 0.09839986380189657 0.8327551233768463
118 mamlK_U4_K1 117 0.10070577573031186 0.8058769750595093
119 mamlK_U4_K1 118 0.09746629691372315 0.8393159770965576
120 mamlK_U4_K1 119 0.10106155103693405 0.8233879512548447
121 mamlK_U4_K1 120 0.09595092674096425 0.8033852116763591
122 mamlK_U4_K1 121 0.09811688131342332 0.8218545391410589
123 mamlK_U4_K1 122 0.09794915122290453 0.8211795264482498
124 mamlK_U4_K1 123 0.09805149165292582 0.8193078821897507
125 mamlK_U4_K1 124 0.10025263633579015 0.8406993782520295
126 mamlK_U4_K1 125 0.09808727153887352 0.8137885761260987
127 mamlK_U4_K1 126 0.10215222335110108 0.8289594221115112
128 mamlK_U4_K1 127 0.099190032693247 0.8155477011203766
129 mamlK_U4_K1 128 0.09742029951264461 0.8220733726024627
130 mamlK_U4_K1 129 0.09838224243372679 0.8051702699810267
131 mamlK_U4_K1 130 0.10047449295719464 0.8209642623364926
132 mamlK_U4_K1 131 0.0995165882135431 0.8328473436832428
133 mamlK_U4_K1 132 0.09756886714448532 0.8399988865852356
134 mamlK_U4_K1 133 0.09982176166027784 0.8373800277709961
135 mamlK_U4_K1 134 0.09614672601222991 0.7943533003330231
136 mamlK_U4_K1 135 0.09809338885049025 0.830563662648201
137 mamlK_U4_K1 136 0.09748522025843462 0.8175740486383438
138 mamlK_U4_K1 137 0.10037818850328525 0.8242804265022278
139 mamlK_U4_K1 138 0.09970037418107192 0.8384668493270874
140 mamlK_U4_K1 139 0.09720127459615469 0.8469519829750061
141 mamlK_U4_K1 140 0.09952644258737564 0.8203377610445023
142 mamlK_U4_K1 141 0.09824217007805904 0.8339519834518433
143 mamlK_U4_K1 142 0.0983365482588609 0.8353655660152435
144 mamlK_U4_K1 143 0.09846711347500484 0.8336063784360885
145 mamlK_U4_K1 144 0.09600669844696919 0.838597549200058
146 mamlK_U4_K1 145 0.09839047902574141 0.8190236341953278
147 mamlK_U4_K1 146 0.09458484131842852 0.7950071096420288
148 mamlK_U4_K1 147 0.10206967469304801 0.8337981808185577
149 mamlK_U4_K1 148 0.09619337748736143 0.8229763522744179
150 mamlK_U4_K1 149 0.09837168955554565 0.8226958364248276
151 mamlK_U4_K1 150 0.09784457499782244 0.831935510635376
152 mamlK_U4_K1 151 0.09839328055580457 0.8018084388971328
153 mamlK_U4_K1 152 0.10174448204537233 0.8154962623119354
154 mamlK_U4_K1 153 0.09933581923445066 0.8247149229049683
155 mamlK_U4_K1 154 0.09851899307221174 0.8396154713630676
156 mamlK_U4_K1 155 0.0967815853158633 0.8192743968963623
157 mamlK_U4_K1 156 0.0967003645375371 0.8307345020771026
158 mamlK_U4_K1 157 0.09744728475809097 0.8354449117183685
159 mamlK_U4_K1 158 0.09778129361569882 0.8469332373142242
160 mamlK_U4_K1 159 0.0973689887051781 0.8357069301605224
161 mamlK_U4_K1 160 0.09487996938327949 0.8366256988048554
162 mamlK_U4_K1 161 0.09986218032737573 0.8537843728065491
163 mamlK_U4_K1 162 0.09857570384939511 0.8117210727185011
164 mamlK_U4_K1 163 0.09914655891557535 0.8150099718570709
165 mamlK_U4_K1 164 0.09809189323335886 0.8190731909871102
166 mamlK_U4_K1 165 0.09726417265832424 0.8113209700584412
167 mamlK_U4_K1 166 0.09697653084993363 0.7949187976121902
168 mamlK_U4_K1 167 0.0962723487491409 0.8570070743560791
169 mamlK_U4_K1 168 0.09573295542349418 0.8191429078578949
170 mamlK_U4_K1 169 0.10149526163935661 0.8467233288288116
171 mamlK_U4_K1 170 0.10138127674659093 0.805179414153099
172 mamlK_U4_K1 171 0.09879167282332976 0.8121675479412079
173 mamlK_U4_K1 172 0.09812746625393629 0.8377763235569
174 mamlK_U4_K1 173 0.09515141988794008 0.7970901311933994
175 mamlK_U4_K1 174 0.09954407181590795 0.8092442405223846
176 mamlK_U4_K1 175 0.099028791214029 0.8327612978219986
177 mamlK_U4_K1 176 0.0947954143707951 0.8233170545101166
178 mamlK_U4_K1 177 0.10073009827484687 0.8353170520067215
179 mamlK_U4_K1 178 0.09687755049516757 0.8225724085792899
180 mamlK_U4_K1 179 0.09585505502919356 0.8360891532897949
181 mamlK_U4_K1 180 0.095084415388604 0.8301138612627983
182 mamlK_U4_K1 181 0.09646117344498634 0.8068984220921993
183 mamlK_U4_K1 182 0.09664699154595534 0.8370879560708999
184 mamlK_U4_K1 183 0.09913663035879533 0.8427578961849213
185 mamlK_U4_K1 184 0.09319478750228882 0.8245357871055603
186 mamlK_U4_K1 185 0.09237765906999508 0.8484940003603697
187 mamlK_U4_K1 186 0.09722080772121747 0.8101367428898811
188 mamlK_U4_K1 187 0.09665202200412751 0.8398332393169403
189 mamlK_U4_K1 188 0.09336812485009433 0.8260139811038971
190 mamlK_U4_K1 189 0.09813577774912119 0.7999353009462357
191 mamlK_U4_K1 190 0.09862468458712101 0.8128549668937921
192 mamlK_U4_K1 191 0.0958941849321127 0.8338201177120209
193 mamlK_U4_K1 192 0.0979670520250996 0.8215850901603698
194 mamlK_U4_K1 193 0.09594314058621724 0.844558732509613
195 mamlK_U4_K1 194 0.09298188497622807 0.793527118563652
196 mamlK_U4_K1 195 0.09657523507873217 0.8037195234373212
197 mamlK_U4_K1 196 0.09581249800821146 0.8372018289193511
198 mamlK_U4_K1 197 0.09729957985381285 0.8372089326381683
199 mamlK_U4_K1 198 0.09797621207932632 0.8609340286254883
200 mamlK_U4_K1 199 0.09215858279416958 0.8411007606983185
201 mamlK_U4_K1 200 0.10175504104544719 0.8155054910480977
202 mamlK_U4_K2 1 0.33009643415609996 0.6404661571979523
203 mamlK_U4_K2 2 0.19751283938686054 0.6994542908668518
204 mamlK_U4_K2 3 0.17039189050594966 0.7177034470438958
205 mamlK_U4_K2 4 0.14683493509888648 0.7549881333112717
206 mamlK_U4_K2 5 0.14214098391433558 0.7775750946998596
207 mamlK_U4_K2 6 0.13506576031446457 0.7752245557308197
208 mamlK_U4_K2 7 0.12746123112738134 0.7501543098688126
209 mamlK_U4_K2 8 0.11961887220541637 0.7510023427009582
210 mamlK_U4_K2 9 0.11707525449494521 0.7949222242832183
211 mamlK_U4_K2 10 0.1160093462963899 0.8069861221313477
212 mamlK_U4_K2 11 0.11613710142672062 0.8104732394218445
213 mamlK_U4_K2 12 0.11103422440588474 0.7845612713694572
214 mamlK_U4_K2 13 0.10783838329215845 0.829844833612442
215 mamlK_U4_K2 14 0.1055389762793978 0.8013944083452225
216 mamlK_U4_K2 15 0.10058609920243422 0.825480352640152
217 mamlK_U4_K2 16 0.10422575173278649 0.8074200689792633
218 mamlK_U4_K2 17 0.0977033347884814 0.8080903881788254
219 mamlK_U4_K2 18 0.09779747154563666 0.8424500906467438
220 mamlK_U4_K2 19 0.10029875294615825 0.8373576498031616
221 mamlK_U4_K2 20 0.09697070773690938 0.822427898645401
222 mamlK_U4_K2 21 0.09771630429973205 0.8044733768701553
223 mamlK_U4_K2 22 0.09742746957888206 0.8173106171935797
224 mamlK_U4_K2 23 0.0940728797763586 0.8494027149677277
225 mamlK_U4_K2 24 0.09212705266972383 0.8311388707160949
226 mamlK_U4_K2 25 0.09275115575641393 0.8395859718322753
227 mamlK_U4_K2 26 0.09503940892716249 0.8576833415031433
228 mamlK_U4_K2 27 0.09258263846238454 0.8396793757379055
229 mamlK_U4_K2 28 0.08518289295335611 0.8477391874790192
230 mamlK_U4_K2 29 0.0926529020195206 0.8371598333120346
231 mamlK_U4_K2 30 0.08894754163920879 0.838513525724411
232 mamlK_U4_K2 31 0.09006083675970634 0.8333994972705842
233 mamlK_U4_K2 32 0.08352234294017157 0.8553407979011536
234 mamlK_U4_K2 33 0.08576047949492932 0.8528991091251373
235 mamlK_U4_K2 34 0.08683417117844025 0.8335630869865418
236 mamlK_U4_K2 35 0.08950441027681033 0.8431726503372192
237 mamlK_U4_K2 36 0.08654742788523435 0.8442901051044465
238 mamlK_U4_K2 37 0.08113688422987858 0.8236390700936318
239 mamlK_U4_K2 38 0.08492758976916472 0.8210196077823639
240 mamlK_U4_K2 39 0.08577215957144896 0.8526832580566406
241 mamlK_U4_K2 40 0.08500203839192788 0.8752001440525055
242 mamlK_U4_K2 41 0.0875444091608127 0.832539399266243
243 mamlK_U4_K2 42 0.08181945122778415 0.8507491916418075
244 mamlK_U4_K2 43 0.0819054234897097 0.855202659368515
245 mamlK_U4_K2 44 0.08617788020521402 0.8713641655445099
246 mamlK_U4_K2 45 0.08566913985957703 0.8247881694883108
247 mamlK_U4_K2 46 0.08179564539343119 0.845501013994217
248 mamlK_U4_K2 47 0.08488825548440218 0.8631465661525727
249 mamlK_U4_K2 48 0.08409984057148298 0.8472438031435012
250 mamlK_U4_K2 49 0.08584492423882087 0.8509540104866028
251 mamlK_U4_K2 50 0.08346907168626785 0.8511415147781372
252 mamlK_U4_K2 51 0.07990209377060334 0.8689575326442719
253 mamlK_U4_K2 52 0.08410337423284849 0.8508306980133057
254 mamlK_U4_K2 53 0.0876126375173529 0.8162707450985909
255 mamlK_U4_K2 54 0.08224932204931974 0.8585407197475433
256 mamlK_U4_K2 55 0.07941148596505324 0.8515629267692566
257 mamlK_U4_K2 56 0.08050316243122022 0.8528003656864166
258 mamlK_U4_K2 57 0.08418187294155359 0.8606536877155304
259 mamlK_U4_K2 58 0.07984441542377074 0.8503201460838318
260 mamlK_U4_K2 59 0.08024106806765 0.8378407073020935
261 mamlK_U4_K2 60 0.08427959077060222 0.8442935156822204
262 mamlK_U4_K2 61 0.08412512136002381 0.8403443485498429
263 mamlK_U4_K2 62 0.08018926820407311 0.8648817265033721
264 mamlK_U4_K2 63 0.0827678702150782 0.8695954430103302
265 mamlK_U4_K2 64 0.07892433275779089 0.8678577411174774
266 mamlK_U4_K2 65 0.0809398095185558 0.875778557062149
267 mamlK_U4_K2 66 0.08101528149098158 0.8603648030757904
268 mamlK_U4_K2 67 0.07788739221791427 0.8478634390234947
269 mamlK_U4_K2 68 0.08084647183616957 0.8469989037513733
270 mamlK_U4_K2 69 0.07827158911774555 0.8807501482963562
271 mamlK_U4_K2 70 0.08120915131022532 0.8573072373867034
272 mamlK_U4_K2 71 0.08105391882359982 0.8508509695529938
273 mamlK_U4_K2 72 0.07951286915689706 0.8748018336296082
274 mamlK_U4_K2 73 0.07970122794310251 0.8358339044451714
275 mamlK_U4_K2 74 0.0810572030643622 0.8612137591838837
276 mamlK_U4_K2 75 0.07919702704995871 0.8776061081886292
277 mamlK_U4_K2 76 0.07858887599160273 0.86496661901474
278 mamlK_U4_K2 77 0.07765222957978646 0.8753225308656692
279 mamlK_U4_K2 78 0.07947905781368414 0.8770309418439866
280 mamlK_U4_K2 79 0.07928254247953494 0.8619640278816223
281 mamlK_U4_K2 80 0.07918823627134164 0.8512284314632416
282 mamlK_U4_K2 81 0.08322223893056313 0.8404453146457672
283 mamlK_U4_K2 82 0.07693830855190754 0.8610380899906158
284 mamlK_U4_K2 83 0.07837418059508006 0.8755573546886444
285 mamlK_U4_K2 84 0.07821022428572177 0.8421213680505752
286 mamlK_U4_K2 85 0.07899662643671036 0.8438133424520493
287 mamlK_U4_K2 86 0.07688808182875316 0.8383115620538593
288 mamlK_U4_K2 87 0.07651500377804041 0.8694952100515365
289 mamlK_U4_K2 88 0.08036992385983467 0.8497046792507171
290 mamlK_U4_K2 89 0.0797459103539586 0.8506973469257355
291 mamlK_U4_K2 90 0.07704815251131852 0.814814225435257
292 mamlK_U4_K2 91 0.07920575883239507 0.8628109717369079
293 mamlK_U4_K2 92 0.0758451631044348 0.8538785624504089
294 mamlK_U4_K2 93 0.08011396041760842 0.8848290908336639
295 mamlK_U4_K2 94 0.07990490927050511 0.8457126104831696
296 mamlK_U4_K2 95 0.07785357461621364 0.8530737908184528
297 mamlK_U4_K2 96 0.07866809281210105 0.8682119965553283
298 mamlK_U4_K2 97 0.07645941035201152 0.8568860089033842
299 mamlK_U4_K2 98 0.08080260435740153 0.8890300059318542
300 mamlK_U4_K2 99 0.07903780496368805 0.8393355929851531
301 mamlK_U4_K2 100 0.07707939996073643 0.8576977205276489
302 mamlK_U4_K2 101 0.07986088172843059 0.8742614844441414
303 mamlK_U4_K2 102 0.07917260184884072 0.8708767062425613
304 mamlK_U4_K2 103 0.07859754203508297 0.8767391210794448
305 mamlK_U4_K2 104 0.0729678421964248 0.8789809787273407
306 mamlK_U4_K2 105 0.078183617045482 0.8899980449676513
307 mamlK_U4_K2 106 0.07746515375872454 0.8696281227469445
308 mamlK_U4_K2 107 0.0752659889558951 0.8551996064186096
309 mamlK_U4_K2 108 0.07916913042465845 0.8740159738063812
310 mamlK_U4_K2 109 0.0784177461018165 0.865906138420105
311 mamlK_U4_K2 110 0.07712566965570053 0.8549001354724168
312 mamlK_U4_K2 111 0.07774128833164771 0.8428320151567459
313 mamlK_U4_K2 112 0.07541980938365062 0.8340257841348648
314 mamlK_U4_K2 113 0.07555310793220997 0.8776209282875062
315 mamlK_U4_K2 114 0.07783369682729244 0.8660740208625793
316 mamlK_U4_K2 115 0.07674313404907783 0.8622547125816346
317 mamlK_U4_K2 116 0.07566199239343405 0.8766952967643737
318 mamlK_U4_K2 117 0.07742926287154357 0.8505650591850281
319 mamlK_U4_K2 118 0.07415519341826439 0.8777812373638153
320 mamlK_U4_K2 119 0.07849369635184605 0.8740108346939087
321 mamlK_U4_K2 120 0.07369030458231766 0.8489554125070572
322 mamlK_U4_K2 121 0.0749422255034248 0.8669265341758728
323 mamlK_U4_K2 122 0.07500049248337745 0.861528360247612
324 mamlK_U4_K2 123 0.07508466145644585 0.866158322095871
325 mamlK_U4_K2 124 0.07766974025716385 0.8837520587444305
326 mamlK_U4_K2 125 0.07510182210554679 0.8593252754211426
327 mamlK_U4_K2 126 0.07917135852078597 0.877312958240509
328 mamlK_U4_K2 127 0.07660577376683553 0.8634454041719437
329 mamlK_U4_K2 128 0.07532547718534867 0.8676647818088532
330 mamlK_U4_K2 129 0.07596221605936686 0.8548863589763641
331 mamlK_U4_K2 130 0.077189445545276 0.8642340287566185
332 mamlK_U4_K2 131 0.07637575346976519 0.8782050967216491
333 mamlK_U4_K2 132 0.07474295294533173 0.8849943315982819
334 mamlK_U4_K2 133 0.07758577205240727 0.8829876577854157
335 mamlK_U4_K2 134 0.07335151422768832 0.8453819990158081
336 mamlK_U4_K2 135 0.07516057658940553 0.8734569084644318
337 mamlK_U4_K2 136 0.07502908387531837 0.8600116789340972
338 mamlK_U4_K2 137 0.07770987803737323 0.8648132121562958
339 mamlK_U4_K2 138 0.07721029867728552 0.8802397286891938
340 mamlK_U4_K2 139 0.07497594498097897 0.887419992685318
341 mamlK_U4_K2 140 0.07795365696152051 0.8670073223114013
342 mamlK_U4_K2 141 0.0765503795693318 0.8800798547267914
343 mamlK_U4_K2 142 0.07600562581171592 0.8807691311836243
344 mamlK_U4_K2 143 0.07625699379791816 0.8786227405071259
345 mamlK_U4_K2 144 0.07385005053132772 0.8826266646385192
346 mamlK_U4_K2 145 0.07578502754370371 0.8639318192005158
347 mamlK_U4_K2 146 0.07244726891318957 0.8444139909744263
348 mamlK_U4_K2 147 0.08025744455556075 0.8742389309406281
349 mamlK_U4_K2 148 0.07382677593578894 0.858665382862091
350 mamlK_U4_K2 149 0.07600895341485739 0.8650531363487244
351 mamlK_U4_K2 150 0.07478892865280311 0.8782731914520263
352 mamlK_U4_K2 151 0.07528852264086405 0.8503567552566529
353 mamlK_U4_K2 152 0.07859414044767618 0.8611944818496704
354 mamlK_U4_K2 153 0.07693977219363053 0.8736291658878327
355 mamlK_U4_K2 154 0.07658144281556209 0.8795509481430054
356 mamlK_U4_K2 155 0.07420840689291557 0.8625108706951141
357 mamlK_U4_K2 156 0.07445351575811704 0.8700329601764679
358 mamlK_U4_K2 157 0.07533411675443252 0.8796775019168854
359 mamlK_U4_K2 158 0.07505279827862978 0.8864823198318481
360 mamlK_U4_K2 159 0.07462280741582314 0.8799738252162933
361 mamlK_U4_K2 160 0.07364192470908165 0.8728257191181182
362 mamlK_U4_K2 161 0.07715789119402568 0.8933019423484803
363 mamlK_U4_K2 162 0.0766900130485495 0.8530915887653827
364 mamlK_U4_K2 163 0.07709434696783622 0.8557748764753341
365 mamlK_U4_K2 164 0.07601463935027519 0.8647226530313492
366 mamlK_U4_K2 165 0.07520459307978551 0.8626968514919281
367 mamlK_U4_K2 166 0.07480562787503003 0.8410029348731041
368 mamlK_U4_K2 167 0.07382521180436015 0.8970024859905243
369 mamlK_U4_K2 168 0.0741474213451147 0.8644079852104187
370 mamlK_U4_K2 169 0.07877997777114312 0.8915906190872193
371 mamlK_U4_K2 170 0.07819596990942955 0.8531449621915818
372 mamlK_U4_K2 171 0.07584197665254275 0.8599755465984344
373 mamlK_U4_K2 172 0.07572295269618431 0.8826698207855225
374 mamlK_U4_K2 173 0.07274133364359538 0.8454445844888687
375 mamlK_U4_K2 174 0.07673519194126129 0.8589300870895386
376 mamlK_U4_K2 175 0.07668255116790533 0.8670554399490357
377 mamlK_U4_K2 176 0.07312167560060819 0.864880884885788
378 mamlK_U4_K2 177 0.07731563272575537 0.8784602761268616
379 mamlK_U4_K2 178 0.07467003023872773 0.8690685838460922
380 mamlK_U4_K2 179 0.07420870902637641 0.8789830446243286
381 mamlK_U4_K2 180 0.07308677614976962 0.8737964344024658
382 mamlK_U4_K2 181 0.0745140562703212 0.85428745418787
383 mamlK_U4_K2 182 0.07427270623544852 0.8767592340707779
384 mamlK_U4_K2 183 0.07721575987835726 0.8856483566761016
385 mamlK_U4_K2 184 0.07093844663351774 0.8691560482978821
386 mamlK_U4_K2 185 0.0701368560642004 0.8931281465291977
387 mamlK_U4_K2 186 0.0739590199291706 0.856842908859253
388 mamlK_U4_K2 187 0.07421348863591751 0.8778711783885956
389 mamlK_U4_K2 188 0.07115227452168862 0.8655005168914794
390 mamlK_U4_K2 189 0.07617760819693406 0.8469596752524375
391 mamlK_U4_K2 190 0.07654514133930207 0.8594996654987335
392 mamlK_U4_K2 191 0.07330102030187845 0.878177763223648
393 mamlK_U4_K2 192 0.07693664040416479 0.8629058760404587
394 mamlK_U4_K2 193 0.07394562887648741 0.8856277394294739
395 mamlK_U4_K2 194 0.07082576231410107 0.842761322259903
396 mamlK_U4_K2 195 0.07486165312429269 0.8504951947927475
397 mamlK_U4_K2 196 0.07434852339327336 0.8827377158403397
398 mamlK_U4_K2 197 0.0752689757073919 0.8782028257846832
399 mamlK_U4_K2 198 0.07601615959157547 0.900384577512741
400 mamlK_U4_K2 199 0.07087323567519585 0.8825874209403992
401 mamlK_U4_K2 200 0.07997377313052614 0.8654782837629318
402 mamlK_U4_K8 1 0.4779631220300992 0.5218560743331909
403 mamlK_U4_K8 2 0.23905222594738007 0.6792358481884002
404 mamlK_U4_K8 3 0.18147014014422894 0.7208914625644683
405 mamlK_U4_K8 4 0.15055276423692704 0.7708174192905426
406 mamlK_U4_K8 5 0.14128380725781123 0.7926760315895081
407 mamlK_U4_K8 6 0.12824171476066112 0.8032137858867645
408 mamlK_U4_K8 7 0.11776311251024406 0.7746306386590004
409 mamlK_U4_K8 8 0.1074277855704228 0.7891612917184829
410 mamlK_U4_K8 9 0.10303358559807142 0.8299821317195892
411 mamlK_U4_K8 10 0.10054248961309592 0.8459873366355896
412 mamlK_U4_K8 11 0.0977603208522002 0.8454595196247101
413 mamlK_U4_K8 12 0.09262214314192534 0.8343739348649979
414 mamlK_U4_K8 13 0.088804429123799 0.8629938220977783
415 mamlK_U4_K8 14 0.08515530495593945 0.8525386583805085
416 mamlK_U4_K8 15 0.07906328241030375 0.8669246089458466
417 mamlK_U4_K8 16 0.08032574063787858 0.8604840934276581
418 mamlK_U4_K8 17 0.07496412508189679 0.8630585432052612
419 mamlK_U4_K8 18 0.07406545748313267 0.8816358673572541
420 mamlK_U4_K8 19 0.07546724513173103 0.8858630764484405
421 mamlK_U4_K8 20 0.07205935560166836 0.8764482045173645
422 mamlK_U4_K8 21 0.07199544588724772 0.8631847333908081
423 mamlK_U4_K8 22 0.07193882702539364 0.8703910311311484
424 mamlK_U4_K8 23 0.0686386434858044 0.8941591203212738
425 mamlK_U4_K8 24 0.06663852225989103 0.8849314951896667
426 mamlK_U4_K8 25 0.06567058954387903 0.8925490248203277
427 mamlK_U4_K8 26 0.06764999195933342 0.9016448211669922
428 mamlK_U4_K8 27 0.06502218319724004 0.8826518581807613
429 mamlK_U4_K8 28 0.05909692148367564 0.9007911098003387
430 mamlK_U4_K8 29 0.06351759045074383 0.888796900510788
431 mamlK_U4_K8 30 0.060910760474701724 0.8955352973937988
432 mamlK_U4_K8 31 0.06196829771002134 0.8874075281620025
433 mamlK_U4_K8 32 0.05644836450616519 0.9074052202701569
434 mamlK_U4_K8 33 0.057740512217084566 0.9028492844104767
435 mamlK_U4_K8 34 0.05792104292660952 0.8965217685699463
436 mamlK_U4_K8 35 0.06055275437732537 0.9030590569972992
437 mamlK_U4_K8 36 0.05747922111302614 0.8954496139287949
438 mamlK_U4_K8 37 0.054461800654729206 0.8852898293733596
439 mamlK_U4_K8 38 0.05669976758460204 0.8828799104690552
440 mamlK_U4_K8 39 0.057326951970656716 0.9069823884963989
441 mamlK_U4_K8 40 0.05562142616758744 0.9228259885311126
442 mamlK_U4_K8 41 0.056953598024944464 0.8979138338565826
443 mamlK_U4_K8 42 0.05238298699259758 0.9023919695615769
444 mamlK_U4_K8 43 0.05281001061201095 0.9102864873409271
445 mamlK_U4_K8 44 0.056227827581266564 0.9165967273712158
446 mamlK_U4_K8 45 0.05671533782655994 0.8842040574550629
447 mamlK_U4_K8 46 0.05140675840899348 0.911913492679596
448 mamlK_U4_K8 47 0.053315665225187936 0.9172633159160614
449 mamlK_U4_K8 48 0.054122454139093557 0.9081482481956482
450 mamlK_U4_K8 49 0.056235977640996374 0.9098136830329895
451 mamlK_U4_K8 50 0.05336583575854699 0.909479022026062
452 mamlK_U4_K8 51 0.050722224302589894 0.922106202840805
453 mamlK_U4_K8 52 0.052740548557291426 0.9128444647789001
454 mamlK_U4_K8 53 0.05585971401383479 0.8837545025348663
455 mamlK_U4_K8 54 0.05109009997919202 0.9184091424942017
456 mamlK_U4_K8 55 0.04981609096750617 0.9129302978515625
457 mamlK_U4_K8 56 0.05034565056363741 0.9072384810447693
458 mamlK_U4_K8 57 0.05392729749282201 0.915323349237442
459 mamlK_U4_K8 58 0.04984411620224516 0.9051778745651246
460 mamlK_U4_K8 59 0.04943579879278938 0.9060387516021728
461 mamlK_U4_K8 60 0.05210168012107412 0.9042724931240081
462 mamlK_U4_K8 61 0.053020734402040644 0.9040648138523102
463 mamlK_U4_K8 62 0.0493498490874966 0.9204268443584442
464 mamlK_U4_K8 63 0.05151050047948957 0.9245637631416321
465 mamlK_U4_K8 64 0.048734364062547685 0.9229525899887085
466 mamlK_U4_K8 65 0.04999003152052561 0.9255816066265106
467 mamlK_U4_K8 66 0.04980439253772299 0.9184465742111206
468 mamlK_U4_K8 67 0.04705699571718772 0.9034612339735031
469 mamlK_U4_K8 68 0.04975722283124924 0.9102222228050232
470 mamlK_U4_K8 69 0.047271586464097105 0.9334056138992309
471 mamlK_U4_K8 70 0.04922019510840376 0.9129670470952987
472 mamlK_U4_K8 71 0.049509449706723295 0.9152740627527237
473 mamlK_U4_K8 72 0.04765419605498512 0.928230996131897
474 mamlK_U4_K8 73 0.04799282111848394 0.9038844287395478
475 mamlK_U4_K8 74 0.048781947984049716 0.9238899147510529
476 mamlK_U4_K8 75 0.04753747931371133 0.9296437633037568
477 mamlK_U4_K8 76 0.047252054599424206 0.9271022891998291
478 mamlK_U4_K8 77 0.04558431272705396 0.9272428321838379
479 mamlK_U4_K8 78 0.0481410486313204 0.929426521062851
480 mamlK_U4_K8 79 0.04766047046830257 0.9206611275672912
481 mamlK_U4_K8 80 0.047961000899473825 0.9129423344135285
482 mamlK_U4_K8 81 0.05075534423813224 0.9032941746711731
483 mamlK_U4_K8 82 0.04508847988521059 0.9260913872718811
484 mamlK_U4_K8 83 0.04695217610026399 0.931098209619522
485 mamlK_U4_K8 84 0.04728463528056939 0.902416033744812
486 mamlK_U4_K8 85 0.046226587016135454 0.9134788656234741
487 mamlK_U4_K8 86 0.04504619709526499 0.904334981366992
488 mamlK_U4_K8 87 0.043947319140036904 0.9281575644016266
489 mamlK_U4_K8 88 0.04702356739590565 0.9173953318595887
490 mamlK_U4_K8 89 0.04712517554561297 0.9085967606306076
491 mamlK_U4_K8 90 0.045631353799253704 0.8883070302009582
492 mamlK_U4_K8 91 0.04667507576445738 0.9227060890197754
493 mamlK_U4_K8 92 0.04412580339858929 0.9164861834049225
494 mamlK_U4_K8 93 0.04830271106213331 0.937560864686966
495 mamlK_U4_K8 94 0.04687190885345141 0.9109290826320648
496 mamlK_U4_K8 95 0.04599330607180794 0.9103593727946282
497 mamlK_U4_K8 96 0.04639992384240031 0.9260409581661224
498 mamlK_U4_K8 97 0.045589470819880566 0.9152871699631214
499 mamlK_U4_K8 98 0.048632444112251205 0.9368037819862366
500 mamlK_U4_K8 99 0.04776907954365015 0.9071453016996384
501 mamlK_U4_K8 100 0.045159862879663705 0.9187269639968872
502 mamlK_U4_K8 101 0.04775585522254308 0.9269299256801605
503 mamlK_U4_K8 102 0.0464935635526975 0.9279921305179596
504 mamlK_U4_K8 103 0.04451344801733891 0.9344207990169525
505 mamlK_U4_K8 104 0.042567297207812466 0.9329907190799713
506 mamlK_U4_K8 105 0.045497030957291525 0.9426866602897644
507 mamlK_U4_K8 106 0.04576344070956111 0.921606302857399
508 mamlK_U4_K8 107 0.042682510353624824 0.9218176829814911
509 mamlK_U4_K8 108 0.04616001067683101 0.9318635451793671
510 mamlK_U4_K8 109 0.046299802145610254 0.920188130736351
511 mamlK_U4_K8 110 0.044982517628620065 0.9136863873898983
512 mamlK_U4_K8 111 0.044354827615122 0.9123714852333069
513 mamlK_U4_K8 112 0.04368219742551446 0.9059731781482696
514 mamlK_U4_K8 113 0.04377598489324252 0.9350841248035431
515 mamlK_U4_K8 114 0.045482099012782176 0.9279284679889679
516 mamlK_U4_K8 115 0.04554533720016479 0.9237250006198883
517 mamlK_U4_K8 116 0.044231869392096994 0.9316341328620911
518 mamlK_U4_K8 117 0.045082571376115085 0.9117023973166942
519 mamlK_U4_K8 118 0.04259504518782099 0.9364819550514221
520 mamlK_U4_K8 119 0.045883758161216974 0.9326616275310516
521 mamlK_U4_K8 120 0.04125504024947683 0.9134474474191666
522 mamlK_U4_K8 121 0.041810687780380246 0.9241221788525581
523 mamlK_U4_K8 122 0.04282815216730038 0.9292815673351288
524 mamlK_U4_K8 123 0.04266041820247968 0.9280487847328186
525 mamlK_U4_K8 124 0.044364120395233235 0.936419312953949
526 mamlK_U4_K8 125 0.04247118879109621 0.9227976202964783
527 mamlK_U4_K8 126 0.0460307758487761 0.9341438019275665
528 mamlK_U4_K8 127 0.04377921528493365 0.924741443991661
529 mamlK_U4_K8 128 0.043116927767793335 0.9314820396900177
530 mamlK_U4_K8 129 0.04339456543947259 0.9138414144515992
531 mamlK_U4_K8 130 0.043798081732044615 0.9210628312826157
532 mamlK_U4_K8 131 0.043559580196936926 0.9359981536865234
533 mamlK_U4_K8 132 0.04170446819315354 0.9426365578174591
534 mamlK_U4_K8 133 0.04446362294877569 0.9384375298023224
535 mamlK_U4_K8 134 0.0418021728284657 0.9175786828994751
536 mamlK_U4_K8 135 0.043493542969226834 0.9279124981164932
537 mamlK_U4_K8 136 0.042993830156823 0.9264187860488892
538 mamlK_U4_K8 137 0.044605811287959415 0.9323660790920257
539 mamlK_U4_K8 138 0.044286446180194616 0.9380956745147705
540 mamlK_U4_K8 139 0.04319346609835823 0.942953290939331
541 mamlK_U4_K8 140 0.04432633345325788 0.929705913066864
542 mamlK_U4_K8 141 0.04371677356461684 0.9349844694137573
543 mamlK_U4_K8 142 0.04353654883181055 0.9383136737346649
544 mamlK_U4_K8 143 0.04367546576385697 0.9354643368721008
545 mamlK_U4_K8 144 0.041613030085961025 0.9381922364234925
546 mamlK_U4_K8 145 0.04262268448248505 0.9285154962539672
547 mamlK_U4_K8 146 0.04042875016729037 0.9156563115119934
548 mamlK_U4_K8 147 0.046729983209321896 0.9331570887565612
549 mamlK_U4_K8 148 0.04209206994002064 0.9224640035629272
550 mamlK_U4_K8 149 0.043427394926548006 0.9270243394374847
551 mamlK_U4_K8 150 0.04198032721877098 0.9402234518527984
552 mamlK_U4_K8 151 0.04226738051821788 0.9176333004236221
553 mamlK_U4_K8 152 0.04550106109430393 0.9295529711246491
554 mamlK_U4_K8 153 0.04412522895882527 0.9327342891693116
555 mamlK_U4_K8 154 0.04346378943572442 0.9387084555625915
556 mamlK_U4_K8 155 0.040683036719759307 0.9233161008358002
557 mamlK_U4_K8 156 0.042999583793183166 0.9294557863473892
558 mamlK_U4_K8 157 0.04285053016617894 0.9395448267459869
559 mamlK_U4_K8 158 0.042279991923520964 0.9420728731155396
560 mamlK_U4_K8 159 0.04184378054613869 0.938912742137909
561 mamlK_U4_K8 160 0.040890439618378877 0.9331023383140564
562 mamlK_U4_K8 161 0.044424817965676384 0.9469376182556153
563 mamlK_U4_K8 162 0.04358690325791637 0.9232855385541916
564 mamlK_U4_K8 163 0.04441141473129392 0.9210169994831086
565 mamlK_U4_K8 164 0.04287119815746943 0.9287175559997558
566 mamlK_U4_K8 165 0.04223649861291051 0.9247390687465668
567 mamlK_U4_K8 166 0.04204685707266132 0.9069620609283447
568 mamlK_U4_K8 167 0.041341771067430574 0.9463892436027527
569 mamlK_U4_K8 168 0.041193387092401584 0.9297560715675354
570 mamlK_U4_K8 169 0.0442957843405505 0.9429472196102142
571 mamlK_U4_K8 170 0.04473894990359743 0.9180112385749817
572 mamlK_U4_K8 171 0.04344633239631852 0.923888647556305
573 mamlK_U4_K8 172 0.04251364936431249 0.9378248107433319
574 mamlK_U4_K8 173 0.04013625531767805 0.9099926733970642
575 mamlK_U4_K8 174 0.04395431150992712 0.922145824432373
576 mamlK_U4_K8 175 0.04366889546935757 0.927090163230896
577 mamlK_U4_K8 176 0.04034995252887408 0.93624183177948
578 mamlK_U4_K8 177 0.044224687373886504 0.933515704870224
579 mamlK_U4_K8 178 0.04161312356591225 0.9275222307443619
580 mamlK_U4_K8 179 0.04161462208256125 0.9307364004850388
581 mamlK_U4_K8 180 0.04066770110279322 0.9321164637804031
582 mamlK_U4_K8 181 0.040599294907102984 0.9210462868213654
583 mamlK_U4_K8 182 0.04146114056929946 0.9348333179950714
584 mamlK_U4_K8 183 0.043211357481777665 0.942536209821701
585 mamlK_U4_K8 184 0.039732915591448544 0.9329590368270874
586 mamlK_U4_K8 185 0.03888906336079041 0.9380114656686783
587 mamlK_U4_K8 186 0.04069843170543512 0.9172812688350678
588 mamlK_U4_K8 187 0.041107887911299865 0.9397376418113709
589 mamlK_U4_K8 188 0.03884282003467281 0.9309276950359344
590 mamlK_U4_K8 189 0.04263716956600547 0.914151092171669
591 mamlK_U4_K8 190 0.042940374420334895 0.9162731644511223
592 mamlK_U4_K8 191 0.04099056159456571 0.9358823013305664
593 mamlK_U4_K8 192 0.04398662489528457 0.9257796490192414
594 mamlK_U4_K8 193 0.04205015392974019 0.9392344427108764
595 mamlK_U4_K8 194 0.03937658442805211 0.9096033501625062
596 mamlK_U4_K8 195 0.04244757450496157 0.9171342733502388
597 mamlK_U4_K8 196 0.04186845818534493 0.9388355338573455
598 mamlK_U4_K8 197 0.04185678826024135 0.9393337917327881
599 mamlK_U4_K8 198 0.04370603165278832 0.9497280025482178
600 mamlK_U4_K8 199 0.03947611791392167 0.9407294797897339
601 mamlK_U4_K8 200 0.046147463197509446 0.923610799908638
602 mamlD_U4_K4 1 0.33226974586645763 0.7072442746162415
603 mamlD_U4_K4 2 0.17677475623786448 0.7602711629867553
604 mamlD_U4_K4 3 0.1448475058376789 0.783854731619358
605 mamlD_U4_K4 4 0.12020382655163606 0.814984410405159
606 mamlD_U4_K4 5 0.11537383700410525 0.8354198861122132
607 mamlD_U4_K4 6 0.10766624535123508 0.8278951597213745
608 mamlD_U4_K4 7 0.10021830345193546 0.8006813091039657
609 mamlD_U4_K4 8 0.09247000811000665 0.8177136993408203
610 mamlD_U4_K4 9 0.09013969005395969 0.8477715253829956
611 mamlD_U4_K4 10 0.0891475497931242 0.8643319392204285
612 mamlD_U4_K4 11 0.08907465686400731 0.8655763745307923
613 mamlD_U4_K4 12 0.08367027290165424 0.8450695098564028
614 mamlD_U4_K4 13 0.08100369604925314 0.8799751842021942
615 mamlD_U4_K4 14 0.07926372083524863 0.8626924604177475
616 mamlD_U4_K4 15 0.07365076714505751 0.8779079747200013
617 mamlD_U4_K4 16 0.0767112746834755 0.8699885833263398
618 mamlD_U4_K4 17 0.07051228248824676 0.8677393686771393
619 mamlD_U4_K4 18 0.07126029013345639 0.8892756295204163
620 mamlD_U4_K4 19 0.07396258503198623 0.8958798789978027
621 mamlD_U4_K4 20 0.0701787693053484 0.8781686514616013
622 mamlD_U4_K4 21 0.07021890625357628 0.8641251504421235
623 mamlD_U4_K4 22 0.07097932344923417 0.8709613773971796
624 mamlD_U4_K4 23 0.067776012532413 0.8977204287052154
625 mamlD_U4_K4 24 0.06593359860281149 0.8864236354827881
626 mamlD_U4_K4 25 0.06577742870897055 0.8942761540412902
627 mamlD_U4_K4 26 0.0682281036178271 0.9049929404258727
628 mamlD_U4_K4 27 0.0659653755525748 0.8859848852455616
629 mamlD_U4_K4 28 0.0599904407809178 0.9027950048446656
630 mamlD_U4_K4 29 0.0653015545134743 0.8889790588617325
631 mamlD_U4_K4 30 0.062404386351505914 0.8966039460897446
632 mamlD_U4_K4 31 0.06378314279019832 0.8865257000923157
633 mamlD_U4_K4 32 0.05806770576785008 0.906207126379013
634 mamlD_U4_K4 33 0.059646516144275664 0.9038380932807922
635 mamlD_U4_K4 34 0.0600526828939716 0.8928085041046142
636 mamlD_U4_K4 35 0.06303705995281537 0.8979040801525116
637 mamlD_U4_K4 36 0.06020684272671739 0.8944093811511994
638 mamlD_U4_K4 37 0.056072589127967754 0.8780849593877792
639 mamlD_U4_K4 38 0.0591437874486049 0.8754855984449387
640 mamlD_U4_K4 39 0.06000196353842815 0.9038444465398788
641 mamlD_U4_K4 40 0.05908978711813688 0.9190565812587738
642 mamlD_U4_K4 41 0.0600906153023243 0.8876453137397766
643 mamlD_U4_K4 42 0.055646294547865786 0.8961232960224151
644 mamlD_U4_K4 43 0.05668414675320188 0.9031804275512695
645 mamlD_U4_K4 44 0.06053298554072777 0.9184056341648101
646 mamlD_U4_K4 45 0.059651808571070435 0.8761727607250214
647 mamlD_U4_K4 46 0.055535307048509515 0.9027179086208343
648 mamlD_U4_K4 47 0.05750930075223247 0.9118513488769531
649 mamlD_U4_K4 48 0.05781642417733868 0.8970184171199799
650 mamlD_U4_K4 49 0.059838524299363295 0.9061286163330078
651 mamlD_U4_K4 50 0.05751658910885453 0.9048182320594788
652 mamlD_U4_K4 51 0.054772061283389725 0.9153175806999206
653 mamlD_U4_K4 52 0.057227188299099604 0.9023577749729157
654 mamlD_U4_K4 53 0.06031815850486358 0.8746447360515595
655 mamlD_U4_K4 54 0.055270860698074104 0.9127678370475769
656 mamlD_U4_K4 55 0.05397566540166736 0.906368271112442
657 mamlD_U4_K4 56 0.05474784299110373 0.8978208255767822
658 mamlD_U4_K4 57 0.05862275324140986 0.9143995618820191
659 mamlD_U4_K4 58 0.05436739023774862 0.9001333135366439
660 mamlD_U4_K4 59 0.054555159453302624 0.8980947136878967
661 mamlD_U4_K4 60 0.05685729818418622 0.8991078412532807
662 mamlD_U4_K4 61 0.058151018271843595 0.8931859922409058
663 mamlD_U4_K4 62 0.05398393329853813 0.9139244389533997
664 mamlD_U4_K4 63 0.056122340777268014 0.9154566299915313
665 mamlD_U4_K4 64 0.05301577487339576 0.9189777481555939
666 mamlD_U4_K4 65 0.054918188334753114 0.9212599790096283
667 mamlD_U4_K4 66 0.05467476099729538 0.9101110601425171
668 mamlD_U4_K4 67 0.05194011902436614 0.8963859722018241
669 mamlD_U4_K4 68 0.0549555604532361 0.9001881390810013
670 mamlD_U4_K4 69 0.05250753067433834 0.9276944887638092
671 mamlD_U4_K4 70 0.05503080276151498 0.9016196215152741
672 mamlD_U4_K4 71 0.05488684472317497 0.9066729021072387
673 mamlD_U4_K4 72 0.05359344453240434 0.9201527678966522
674 mamlD_U4_K4 73 0.05427974117298921 0.8956168949604034
675 mamlD_U4_K4 74 0.05475098236153523 0.9159560787677765
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677 mamlD_U4_K4 76 0.05264789244780938 0.9185412907600403
678 mamlD_U4_K4 77 0.051409392865995565 0.9221062177419662
679 mamlD_U4_K4 78 0.05349850337331494 0.9248423945903778
680 mamlD_U4_K4 79 0.05382979394868016 0.9127719509601593
681 mamlD_U4_K4 80 0.053457854880640907 0.9004097282886505
682 mamlD_U4_K4 81 0.056950522201756636 0.8939985719323158
683 mamlD_U4_K4 82 0.05090698417276144 0.914024121761322
684 mamlD_U4_K4 83 0.0529921709621946 0.922695838212967
685 mamlD_U4_K4 84 0.053284227320303516 0.8929305797815323
686 mamlD_U4_K4 85 0.05285409294068813 0.9010648596286773
687 mamlD_U4_K4 86 0.05130229050914446 0.8942721292749047
688 mamlD_U4_K4 87 0.05001533610746264 0.9206917119026184
689 mamlD_U4_K4 88 0.05392462734753887 0.9077650117874145
690 mamlD_U4_K4 89 0.05362904659161965 0.9001771813631058
691 mamlD_U4_K4 90 0.052035900683452686 0.8782391160726547
692 mamlD_U4_K4 91 0.052584297036131225 0.9124891722202301
693 mamlD_U4_K4 92 0.05022508302082618 0.9077244758605957
694 mamlD_U4_K4 93 0.054565017335116865 0.9335999476909638
695 mamlD_U4_K4 94 0.0531883321578304 0.9036381077766419
696 mamlD_U4_K4 95 0.05216884396970272 0.9015415945649147
697 mamlD_U4_K4 96 0.0530705667535464 0.9171244978904725
698 mamlD_U4_K4 97 0.05148339337358872 0.9067597711086273
699 mamlD_U4_K4 98 0.05486183729643623 0.931991959810257
700 mamlD_U4_K4 99 0.053760130659987526 0.8953824180364609
701 mamlD_U4_K4 100 0.05132714407518506 0.9088386845588684
702 mamlD_U4_K4 101 0.053888792774329584 0.9230603194236755
703 mamlD_U4_K4 102 0.0527850770081083 0.9184340858459472
704 mamlD_U4_K4 103 0.05152679332221548 0.9252682280540466
705 mamlD_U4_K4 104 0.04825870852296551 0.9282627916336059
706 mamlD_U4_K4 105 0.05150640400747458 0.9349008071422577
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708 mamlD_U4_K4 107 0.04935547518233458 0.909106433391571
709 mamlD_U4_K4 108 0.052696935342003905 0.9248205316066742
710 mamlD_U4_K4 109 0.05243582926069697 0.9151817715167999
711 mamlD_U4_K4 110 0.05094705957919359 0.9041129250079394
712 mamlD_U4_K4 111 0.05081009749944011 0.8941559112071991
713 mamlD_U4_K4 112 0.05004351894681652 0.8896460205316543
714 mamlD_U4_K4 113 0.050028850734233854 0.928802660703659
715 mamlD_U4_K4 114 0.05225728334859014 0.9190279865264892
716 mamlD_U4_K4 115 0.05163899037986994 0.9149557113647461
717 mamlD_U4_K4 116 0.05058057485769192 0.9194704449176788
718 mamlD_U4_K4 117 0.05200697081784407 0.905641396343708
719 mamlD_U4_K4 118 0.048949986242999635 0.9267030274868011
720 mamlD_U4_K4 119 0.052872367488841214 0.9211409950256347
721 mamlD_U4_K4 120 0.04799026660621166 0.899388080239296
722 mamlD_U4_K4 121 0.048778135298440856 0.9122915583848953
723 mamlD_U4_K4 122 0.049206985756754876 0.9172151899337768
724 mamlD_U4_K4 123 0.04927736043309172 0.9171838593482972
725 mamlD_U4_K4 124 0.05120278758307298 0.9308539128303528
726 mamlD_U4_K4 125 0.04889334943766395 0.9104605150222779
727 mamlD_U4_K4 126 0.052975892921288806 0.9274392080307007
728 mamlD_U4_K4 127 0.050311327235152324 0.9167815577983857
729 mamlD_U4_K4 128 0.049772387879590194 0.9209587621688843
730 mamlD_U4_K4 129 0.04997918299088876 0.9006694430857897
731 mamlD_U4_K4 130 0.05016263517240683 0.912883588373661
732 mamlD_U4_K4 131 0.05020913464327653 0.9251600801944733
733 mamlD_U4_K4 132 0.04885625268643101 0.9323220777511597
734 mamlD_U4_K4 133 0.051198460465917986 0.9299926042556763
735 mamlD_U4_K4 134 0.04823922702421745 0.9025299048423767
736 mamlD_U4_K4 135 0.049459049670646585 0.9139180135726929
737 mamlD_U4_K4 136 0.049267774193237225 0.9145679724216461
738 mamlD_U4_K4 137 0.05096803734699885 0.9186458110809326
739 mamlD_U4_K4 138 0.05099628301337361 0.9309629034996033
740 mamlD_U4_K4 139 0.04935644599298636 0.9355896592140198
741 mamlD_U4_K4 140 0.05138297356665134 0.9187753987312317
742 mamlD_U4_K4 141 0.05071673834696412 0.926981908082962
743 mamlD_U4_K4 142 0.05012833488484224 0.9287077867984772
744 mamlD_U4_K4 143 0.050881257249663275 0.9253663563728333
745 mamlD_U4_K4 144 0.0484694933022062 0.9285207271575928
746 mamlD_U4_K4 145 0.0493666319300731 0.9173754668235778
747 mamlD_U4_K4 146 0.047217747842272124 0.9045787739753723
748 mamlD_U4_K4 147 0.05380128918215633 0.9247326457500458
749 mamlD_U4_K4 148 0.04834152360757192 0.9107429003715515
750 mamlD_U4_K4 149 0.0503358512185514 0.9167406404018402
751 mamlD_U4_K4 150 0.04861355886484186 0.9314720213413239
752 mamlD_U4_K4 151 0.049152215557793776 0.9025949084758759
753 mamlD_U4_K4 152 0.05237987481678526 0.9191000699996948
754 mamlD_U4_K4 153 0.050817638095468284 0.924338185787201
755 mamlD_U4_K4 154 0.05072446197271347 0.9293682527542114
756 mamlD_U4_K4 155 0.04761107598741849 0.9084386062622071
757 mamlD_U4_K4 156 0.04906454727674524 0.9223668110370636
758 mamlD_U4_K4 157 0.0496505764623483 0.9320311737060547
759 mamlD_U4_K4 158 0.049232835943500204 0.9339154553413391
760 mamlD_U4_K4 159 0.04919609226907293 0.9278384900093078
761 mamlD_U4_K4 160 0.04731551618004839 0.9251616835594177
762 mamlD_U4_K4 161 0.051343967095017436 0.9402533411979676
763 mamlD_U4_K4 162 0.050502766985446215 0.9112370407581329
764 mamlD_U4_K4 163 0.051261143249770005 0.9096662175655365
765 mamlD_U4_K4 164 0.04977377268796166 0.9198521399497985
766 mamlD_U4_K4 165 0.048813525419682265 0.9133789455890655
767 mamlD_U4_K4 166 0.04894803042834004 0.8936040103435516
768 mamlD_U4_K4 167 0.04804897318904599 0.9391674268245697
769 mamlD_U4_K4 168 0.047860090658068656 0.9183463680744172
770 mamlD_U4_K4 169 0.05172237353399396 0.9353018832206726
771 mamlD_U4_K4 170 0.0518186297826469 0.907518447637558
772 mamlD_U4_K4 171 0.050250552849223216 0.9118054795265198
773 mamlD_U4_K4 172 0.04976588887472948 0.9315401470661163
774 mamlD_U4_K4 173 0.04685487166047096 0.9027909225225449
775 mamlD_U4_K4 174 0.05097919645408789 0.9141061782836915
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fig_cl/cl_results_maml.json
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"cos": 0.9761006777286529,
"mse": 0.00262830908736214
},
"30": {
"cos": 0.9766125748157501,
"mse": 0.0026144605481531473
}
}
},
"mamlP_U2_K4": {
"U": 2,
"K": 4,
"train_s": 2099.9137415885925,
"snr": {
"0": {
"cos": 0.7969576192647219,
"mse": 0.005527409416157752
},
"5": {
"cos": 0.8958459490537644,
"mse": 0.003535830914741382
},
"10": {
"cos": 0.929745109051466,
"mse": 0.003008644887479022
},
"15": {
"cos": 0.9538345046043396,
"mse": 0.0025697590378113092
},
"20": {
"cos": 0.9618748840093613,
"mse": 0.0023491571710910646
},
"25": {
"cos": 0.9661516258716584,
"mse": 0.0023275921714957805
},
"30": {
"cos": 0.9667309571504593,
"mse": 0.0023136503512505443
}
}
},
"mamlP_U3_K4": {
"U": 3,
"K": 4,
"train_s": 2672.6384410858154,
"snr": {
"0": {
"cos": 0.7566024445956573,
"mse": 0.0063944466987159105
},
"5": {
"cos": 0.8714750450849533,
"mse": 0.004172216270584613
},
"10": {
"cos": 0.9123313600271941,
"mse": 0.003467886578524485
},
"15": {
"cos": 0.9431116962432862,
"mse": 0.002887097867205739
},
"20": {
"cos": 0.9531054661273957,
"mse": 0.002762469650944695
},
"25": {
"cos": 0.9579436681270599,
"mse": 0.0026892934129573404
},
"30": {
"cos": 0.9595861222743988,
"mse": 0.0026592298224568365
}
}
},
"mamlP_U5_K4": {
"U": 5,
"K": 4,
"train_s": 5819.801905632019,
"snr": {
"0": {
"cos": 0.7050216964315623,
"mse": 0.008047915536677466
},
"5": {
"cos": 0.8337775056660175,
"mse": 0.00538398580555804
},
"10": {
"cos": 0.8850797078460455,
"mse": 0.004388278966536745
},
"15": {
"cos": 0.9219333533644676,
"mse": 0.0036538032251410184
},
"20": {
"cos": 0.9351386456489563,
"mse": 0.003277393272612244
},
"25": {
"cos": 0.9412116003036499,
"mse": 0.003101464889012277
},
"30": {
"cos": 0.9433053629398346,
"mse": 0.0030744528616778555
}
}
},
"mamlP_U6_K4": {
"U": 6,
"K": 4,
"train_s": 6192.80019235611,
"snr": {
"0": {
"cos": 0.6876412417516112,
"mse": 0.008159141696058215
},
"5": {
"cos": 0.819275195479393,
"mse": 0.005689024343388155
},
"10": {
"cos": 0.8724823503941298,
"mse": 0.004490903354482725
},
"15": {
"cos": 0.9126055588126183,
"mse": 0.003712299307342619
},
"20": {
"cos": 0.9261461035013199,
"mse": 0.0033560030788648875
},
"25": {
"cos": 0.9330184719562531,
"mse": 0.0032503474755212662
},
"30": {
"cos": 0.9347505280971528,
"mse": 0.003245274756103754
}
}
},
"mamlR_U4_K4": {
"U": 4,
"K": 4,
"train_s": 5354.718959569931,
"snr": {
"0": {
"cos": 0.7151452112868428,
"mse": 0.005541059164330364
},
"5": {
"cos": 0.8414680245518684,
"mse": 0.0062078876094892625
},
"10": {
"cos": 0.8914026174843311,
"mse": 0.006805360369384289
},
"15": {
"cos": 0.9260668660402298,
"mse": 0.007245949216187
},
"20": {
"cos": 0.9384899387359619,
"mse": 0.0074955790508538485
},
"25": {
"cos": 0.9446654134988784,
"mse": 0.007596960405819118
},
"30": {
"cos": 0.9462161396741867,
"mse": 0.007674253049306572
}
},
"probe_acc": {
"0": 0.7509999871253967,
"5": 0.7875000238418579,
"10": 0.8324999809265137,
"15": 0.8184999823570251,
"20": 0.8335000276565552,
"25": 0.8389999866485596,
"30": 0.8395000100135803
}
},
"mamlP_U4_K4": {
"U": 4,
"K": 4,
"train_s": 5796.766438961029,
"snr": {
"0": {
"cos": 0.7272935093110428,
"mse": 0.006946617619832978
},
"5": {
"cos": 0.8484958949685096,
"mse": 0.004622220577439293
},
"10": {
"cos": 0.8977697676718235,
"mse": 0.0038188065083231775
},
"15": {
"cos": 0.9313238598108292,
"mse": 0.0032185151416342706
},
"20": {
"cos": 0.9433529702425003,
"mse": 0.0029843107901979236
},
"25": {
"cos": 0.9491406964063644,
"mse": 0.002911557617597282
},
"30": {
"cos": 0.9507425274848939,
"mse": 0.002883736375719309
}
},
"probe_acc": {
"0": 0.753000020980835,
"5": 0.8054999709129333,
"10": 0.8395000100135803,
"15": 0.8320000171661377,
"20": 0.8385000228881836,
"25": 0.8500000238418579,
"30": 0.8475000262260437
},
"lat_gpu_ms": 1.7452812194824219,
"params": 4759296
}
}
fig_cl/cl_results_maml2.json
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{
"mamlK_U4_K1": {
"U": 4,
"K": 1,
"train_s": 5965.1762001514435,
"snr": {
"0": {
"cos": 0.5903875235728919,
"mse": 0.006068825869821012
},
"5": {
"cos": 0.7422410520464182,
"mse": 0.003918564808322117
},
"10": {
"cos": 0.8164008051753044,
"mse": 0.0027814168038312347
},
"15": {
"cos": 0.8704769560098649,
"mse": 0.002142983589321375
},
"20": {
"cos": 0.8900961854457855,
"mse": 0.0018683849333319812
},
"25": {
"cos": 0.9010164388418198,
"mse": 0.0017202156076673418
},
"30": {
"cos": 0.9042909728288651,
"mse": 0.0016732445545494556
}
}
},
"mamlK_U4_K2": {
"U": 4,
"K": 2,
"train_s": 5568.518344163895,
"snr": {
"0": {
"cos": 0.6585480758547783,
"mse": 0.00562618494220078
},
"5": {
"cos": 0.8004514102339745,
"mse": 0.004138905008789152
},
"10": {
"cos": 0.8616232509315014,
"mse": 0.0032424702036660162
},
"15": {
"cos": 0.9067922315597534,
"mse": 0.002654184835962951
},
"20": {
"cos": 0.9229051151275635,
"mse": 0.0024960036822594704
},
"25": {
"cos": 0.93115289914608,
"mse": 0.002353294101310894
},
"30": {
"cos": 0.9336031366586686,
"mse": 0.0023293261982034893
}
}
},
"mamlK_U4_K8": {
"U": 4,
"K": 8,
"train_s": 5635.996988534927,
"snr": {
"0": {
"cos": 0.7966103934645653,
"mse": 0.007616082121618092
},
"5": {
"cos": 0.8945015166401863,
"mse": 0.004124689696356655
},
"10": {
"cos": 0.9269100804030895,
"mse": 0.003506762736942619
},
"15": {
"cos": 0.9509689708948136,
"mse": 0.0027781075756065548
},
"20": {
"cos": 0.959079176902771,
"mse": 0.00257474225317128
},
"25": {
"cos": 0.9628741838932038,
"mse": 0.0025569569268263878
},
"30": {
"cos": 0.9636240524053573,
"mse": 0.002519237624946982
}
}
},
"mamlD_U4_K4": {
"U": 4,
"K": 4,
"train_s": 5619.015917301178,
"snr": {
"0": {
"cos": 0.7611450293697417,
"mse": 0.006763041405472904
},
"5": {
"cos": 0.8720456777811051,
"mse": 0.00445777553319931
},
"10": {
"cos": 0.9139666106104851,
"mse": 0.0036739176262635736
},
"15": {
"cos": 0.9426191251277923,
"mse": 0.00309066718025133
},
"20": {
"cos": 0.9524768965244294,
"mse": 0.0028845382735598834
},
"25": {
"cos": 0.9571917120218277,
"mse": 0.002803497422719374
},
"30": {
"cos": 0.9582184610366822,
"mse": 0.002791161079891026
}
}
}
}
fig_cl/cl_results_todma_u.json
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{
"todma_U1_T24_L128": {
"0": {
"cos": 0.8074489551107399,
"token_err": 0.14848412508952016
},
"5": {
"cos": 0.8736505889799446,
"token_err": 0.0687116564417178
},
"10": {
"cos": 0.9246965700667351,
"token_err": 0.010946241790318657
},
"15": {
"cos": 0.9398762492835522,
"token_err": 0.006066734074823054
},
"20": {
"cos": 0.9469921693205834,
"token_err": 0.0009987515605493133
},
"25": {
"cos": 0.9429717653989792,
"token_err": 0.0
},
"30": {
"cos": 0.9374454534053802,
"token_err": 0.0
}
},
"todma_U2_T24_L128": {
"0": {
"cos": 0.7007968422397971,
"token_err": 0.2686438592173271
},
"5": {
"cos": 0.8507629196345806,
"token_err": 0.1031242062484125
},
"10": {
"cos": 0.8946104854345321,
"token_err": 0.048921749845964264
},
"15": {
"cos": 0.931503138691187,
"token_err": 0.010676156583629894
},
"20": {
"cos": 0.9400034978985786,
"token_err": 0.0031005829095870022
},
"25": {
"cos": 0.9471021571755409,
"token_err": 0.0013839959738298944
},
"30": {
"cos": 0.9430845794081688,
"token_err": 0.0003755633450175263
}
},
"todma_U3_T24_L128": {
"0": {
"cos": 0.5810406875486175,
"token_err": 0.3917525773195876
},
"5": {
"cos": 0.8211166375875473,
"token_err": 0.13069732814955745
},
"10": {
"cos": 0.8936332259575526,
"token_err": 0.047603305785123964
},
"15": {
"cos": 0.923269739151001,
"token_err": 0.02220045706823376
},
"20": {
"cos": 0.9388238374392192,
"token_err": 0.003470787538219982
},
"25": {
"cos": 0.9452669457594554,
"token_err": 8.306337735692334e-05
},
"30": {
"cos": 0.9416227678457896,
"token_err": 8.228420966016621e-05
}
},
"todma_U5_T24_L128": {
"0": {
"cos": 0.42701429841667415,
"token_err": 0.5537061371489755
},
"5": {
"cos": 0.7187287476658821,
"token_err": 0.2355310262529833
},
"10": {
"cos": 0.8738067576885223,
"token_err": 0.08148368836585729
},
"15": {
"cos": 0.9239599657058716,
"token_err": 0.021345568799561117
},
"20": {
"cos": 0.9326754882335663,
"token_err": 0.007691549156887881
},
"25": {
"cos": 0.9385586471557618,
"token_err": 0.005969412925505673
},
"30": {
"cos": 0.9386945986747741,
"token_err": 0.0015876953609526173
}
},
"todma_U6_T24_L128": {
"0": {
"cos": 0.34397646331538756,
"token_err": 0.6302765647743813
},
"5": {
"cos": 0.6786150354146957,
"token_err": 0.2800496688741722
},
"10": {
"cos": 0.8405428379774094,
"token_err": 0.10923335657738217
},
"15": {
"cos": 0.9074154017368953,
"token_err": 0.0349873293550233
},
"20": {
"cos": 0.9348968795935313,
"token_err": 0.008653014133256417
},
"25": {
"cos": 0.9384897446632385,
"token_err": 0.008099842962228283
},
"30": {
"cos": 0.9415062765280405,
"token_err": 0.0011555922410235245
}
}
}
fig_cl/probe_vs_cosine.pdf
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probe_vs_cosine.py
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# probe_vs_cosine.py - supplementary analysis for the letter.
#
# Scatter of downstream probe accuracy (AG News topic classification,
# linear probe trained on clean training-pool embeddings) against the
# cosine similarity of the recovered embeddings, across schemes and
# SNRs, with the Pearson correlation. Shows that the cosine metric used
# in the letter is consistent with downstream perception.
import json
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 14, 'axes.linewidth': 1.2})
with open("fig_cl/cl_results.json") as f:
RJ = json.load(f)
with open("fig_cl/cl_results_maml.json") as f:
RM = json.load(f)
SNRS = ["0", "5", "10", "15", "20", "25", "30"]
# (label, cos-source, acc-source, marker, color)
def sweep_pairs(entry, todma=False):
cos, acc = [], []
for s in SNRS:
a = entry.get("probe_acc", {}).get(s)
if a is None:
continue
c = entry[s]["cos"] if todma else entry["snr"][s]["cos"]
cos.append(c)
acc.append(a)
return cos, acc
SCHEMES = [
("Proposed (MAML)", RM["mamlP_U4_K4"], False, "v", "#d62728"),
("Training w/o MAML [5]", RJ["prop_U4_K4"], False, "x", "#8c564b"),
("Random-projection mask (MAML)", RM["mamlR_U4_K4"], False, "s",
"#984ea3"),
("Conventional orthogonal (MAML)", RM["mamlB_U1_K1"], False, "o",
"#1a1a1a"),
("Conventional orthogonal (joint)", RJ["baseline_U1_K1"], False, "P",
"#7f7f7f"),
("Random-projection mask (joint)", RJ["randmask_U4_K4"], False, "D",
"#c994c7"),
("ToDMA 24x128", RJ["todma_T24_L128"], True, "^", "#4393c3"),
]
all_cos, all_acc = [], []
fig = plt.figure(figsize=(7.0, 5.4))
ax = fig.add_axes([0.12, 0.12, 0.83, 0.83])
for lab, entry, todma, mk, col in SCHEMES:
cos, acc = sweep_pairs(entry, todma)
ax.scatter(cos, acc, marker=mk, s=70, color=col, label=lab,
zorder=3, alpha=0.9)
all_cos += cos
all_acc += acc
all_cos = np.array(all_cos)
all_acc = np.array(all_acc)
r = np.corrcoef(all_cos, all_acc)[0, 1]
b, a = np.polyfit(all_cos, all_acc, 1)
xg = np.linspace(all_cos.min(), all_cos.max(), 50)
ax.plot(xg, b * xg + a, color="#888888", linewidth=1.5, linestyle="--",
zorder=2, label=f"Linear fit (Pearson $r$={r:.3f})")
clean = RM.get("probe_clean_acc", RJ.get("probe_clean_acc"))
ax.axhline(clean, color="#bbbbbb", linewidth=1.2, linestyle=":",
zorder=1)
ax.text(all_cos.min(), clean + 0.004,
f"Clean-embedding reference ({clean:.3f})", fontsize=11,
color="#888888")
ax.set_xlabel("Cosine similarity of recovered embeddings", fontsize=15)
ax.set_ylabel("Downstream probe accuracy", fontsize=15)
ax.grid(True, alpha=0.3)
ax.legend(fontsize=10.5, loc="lower right")
fig.savefig("fig_cl/probe_vs_cosine.png", dpi=150)
fig.savefig("fig_cl/probe_vs_cosine.pdf", dpi=200)
print(f"Saved probe_vs_cosine.(png|pdf) Pearson r = {r:.4f} "
f"over {len(all_cos)} scheme-SNR points")
# Markdown table for the repository README
print("\n| Scheme | CosSim 5 dB | Acc 5 dB | CosSim 20 dB | Acc 20 dB |")
print("|---|---|---|---|---|")
for lab, entry, todma, _, _ in SCHEMES:
def get(s):
c = entry[s]["cos"] if todma else entry["snr"][s]["cos"]
return c, entry.get("probe_acc", {}).get(s, float("nan"))
c5, a5 = get("5")
c20, a20 = get("20")
print(f"| {lab} | {c5:.3f} | {a5:.3f} | {c20:.3f} | {a20:.3f} |")
replot_cl.py
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# Plot CL-letter figures from fig_cl/cl_results*.json
#
# Geometry rule (paper_requirement): every result figure uses the same
# fixed canvas and the same 8:6 axes box, and no tight bounding box is
# applied at save time. Scheme names avoid the banned word "baseline".
#
# Figure set (single large graph per figure):
# Fig. 2 (cl_fig_mux.pdf) : one SNR sweep merging the load sweep
# (conventional + proposed U=1..4) and the
# matched-budget comparison (random mask,
# ToDMA x2) at U=4.
# Fig. 3 (cl_fig_agg.pdf) : aggregate fidelity bars for U=1..6 with
# per-user CosSim and the fully loaded
# orthogonal reference.
import json
import numpy as np
import matplotlib
matplotlib.use("Agg")
import matplotlib.pyplot as plt
plt.rcParams.update({'font.size': 15, 'axes.linewidth': 1.2})
with open("fig_cl/cl_results.json") as f:
R = json.load(f)
# MAML-trained results (reported default protocol) overlay the joint
# runs: every reported transceiver key is remapped to its MAML twin.
with open("fig_cl/cl_results_maml.json") as f:
R.update(json.load(f))
try:
with open("fig_cl/cl_results_maml2.json") as f:
R.update(json.load(f))
except FileNotFoundError:
pass
KEYMAP = {
"baseline_U1_K1": "mamlB_U1_K1",
"prop_U1_K4": "mamlP_U1_K4", "prop_U2_K4": "mamlP_U2_K4",
"prop_U3_K4": "mamlP_U3_K4", "prop_U4_K4": "mamlP_U4_K4",
"prop_U5_K4": "mamlP_U5_K4", "prop_U6_K4": "mamlP_U6_K4",
"randmask_U4_K4": "mamlR_U4_K4",
"ksweep_U4_K1": "mamlK_U4_K1", "ksweep_U4_K2": "mamlK_U4_K2",
"ksweep_U4_K8": "mamlK_U4_K8",
}
# Preserve the joint-trained runs before remapping: they appear in the
# figures as the joint-training ablation (same architecture, no MAML).
JOINT = {k: R[k] for k in list(KEYMAP.keys()) if k in R}
for old, new in KEYMAP.items():
if new in R:
R[old] = R[new]
SNRS = [0, 5, 10, 15, 20, 25, 30]
# Single-graph geometry shared by both result figures: canvas
# 7.5 x 5.55 in, axes box 6.0 x 4.5 in (exactly 8:6).
FIGSIZE = (7.5, 5.55)
AX_RECT = [0.105, 0.115, 0.77, 0.7804]
def one_panel():
fig = plt.figure(figsize=FIGSIZE)
return fig, fig.add_axes(AX_RECT)
def cos_curve(key):
return [R[key]["snr"][str(s)]["cos"] for s in SNRS]
# ============================================================
# Fig 2: merged SNR sweep (load sweep + matched-budget comparison)
# ============================================================
fig, ax = one_panel()
curves = [
("prop_U1_K4", "Proposed $U$=1", "s", "-", "#1a9641", 2),
("prop_U2_K4", "Proposed $U$=2", "^", "-", "#2166ac", 2),
("prop_U3_K4", "Proposed $U$=3", "D", "-", "#d95f02", 2),
("prop_U4_K4", "Proposed $U$=4", "v", "-", "#d62728", 2.5),
("JOINT:prop_U4_K4", "Training w/o MAML [5]", "x",
(0, (5, 2)), "#8c564b", 2),
("baseline_U1_K1", "Conventional orthogonal", "o", "--", "#1a1a1a", 2),
("randmask_U4_K4", "Random-projection mask", "s", "-.", "#984ea3", 2),
("todma_T24_L128", "ToDMA $24\\times128$", "^", ":",
"#4393c3", 2),
("todma_T16_L192", "ToDMA $16\\times192$", "D", ":",
"#92c5de", 2),
]
for key, lab, mk, ls, col, lw in curves:
if key.startswith("todma"):
vals = [R[key][str(s)]["cos"] for s in SNRS]
elif key.startswith("JOINT:"):
vals = [JOINT[key[6:]]["snr"][str(s)]["cos"] for s in SNRS]
else:
vals = cos_curve(key)
ax.plot(SNRS, vals, marker=mk, linestyle=ls, color=col,
linewidth=lw, markersize=8, label=lab)
ax.set_xlabel("SNR (dB)", fontsize=17)
ax.set_ylabel("Cosine Similarity", fontsize=17)
ax.set_ylim([0.45, 1.0])
ax.legend(fontsize=12.5, loc="lower right", ncol=1)
ax.grid(True, alpha=0.3)
fig.savefig("fig_cl/cl_fig_mux.pdf", dpi=200)
fig.savefig("fig_cl/cl_fig_mux.png", dpi=150)
plt.close(fig)
print("Saved cl_fig_mux.pdf")
# ============================================================
# Fig 3: aggregate fidelity across load (bars + per-user line)
# ============================================================
fig, ax2 = one_panel()
from matplotlib.patches import Patch
snr_show = "10"
conv_cos = R["baseline_U1_K1"]["snr"][snr_show]["cos"]
per_user = [R[f"prop_U{U}_K4"]["snr"][snr_show]["cos"] for U in range(1, 7)]
joint_pu = [JOINT[f"prop_U{U}_K4"]["snr"][snr_show]["cos"]
for U in range(1, 7)]
thr = [U * c for U, c in zip(range(1, 7), per_user)]
thr_j = [U * c for U, c in zip(range(1, 7), joint_pu)]
xs = np.arange(1, 7)
# Single color per scheme so the bars match the legend patches.
C_PROP = "#d62728"
with open("fig_cl/cl_results_todma_u.json") as f:
RT = json.load(f)
todma_pu = [RT[f"todma_U{U}_T24_L128"][snr_show]["cos"] if U != 4
else R["todma_T24_L128"][snr_show]["cos"] for U in range(1, 7)]
thr_t = [U * c for U, c in zip(range(1, 7), todma_pu)]
ax2.bar([0], [conv_cos], width=0.55, color="#1a1a1a", alpha=0.85)
ax2.text(0, conv_cos + 0.08, f"{conv_cos:.2f}", ha="center",
va="bottom", fontsize=12)
ax2.bar(xs - 0.27, thr, width=0.26, color=C_PROP, alpha=0.9)
ax2.bar(xs, thr_j, width=0.26, color=C_PROP, alpha=0.4,
hatch="//", edgecolor="#555555", linewidth=0.5)
ax2.bar(xs + 0.27, thr_t, width=0.26, color="#4393c3", alpha=0.75,
hatch="..", edgecolor="#1f5f8b", linewidth=0.5)
for x, val in zip(xs, thr):
ax2.text(x - 0.27, val + 0.08, f"{val:.2f}", ha="center",
va="bottom", fontsize=11)
# Fully loaded orthogonal aggregate (4 blocks x 768 uses = same budget)
ax2.axhline(4 * conv_cos, linestyle="-.", color="#555555", linewidth=2)
ax2.text(-0.45, 4 * conv_cos + 0.13, "Fully loaded orthogonal",
fontsize=12.5, color="#555555")
ax2.set_xticks([0] + list(xs))
ax2.set_xticklabels(["Conv.\n$U$=1"] + [f"Multi.\n$U$={U}"
for U in range(1, 7)], fontsize=12)
ax2.set_ylabel(r"Aggregate fidelity ($U \!\cdot\! \mathrm{CosSim}$)",
fontsize=16)
ax2.grid(True, alpha=0.3, axis="y")
ax2.set_ylim([0, max(thr) * 1.22])
handles = [Patch(facecolor=C_PROP, alpha=0.9, label="Proposed"),
Patch(facecolor=C_PROP, alpha=0.4, hatch="//",
edgecolor="#555555", label="Training w/o MAML [5]"),
Patch(facecolor="#4393c3", alpha=0.75, hatch="..",
edgecolor="#1f5f8b", label="ToDMA $24\\times128$")]
ax2.legend(handles=handles, fontsize=11, loc="center left",
bbox_to_anchor=(0.02, 0.44))
fig.savefig("fig_cl/cl_fig_agg.pdf", dpi=200)
fig.savefig("fig_cl/cl_fig_agg.png", dpi=150)
plt.close(fig)
print("Saved cl_fig_agg.pdf")
# ============================================================
# Print the numbers quoted in the letter
# ============================================================
print("\n===== NUMBERS FOR TEXT (MAML default, held-out) =====")
print("conv per-user@20:", round(conv_cos, 3),
" fully loaded aggregate:", round(4 * conv_cos, 2))
for U in range(1, 7):
v = R[f"prop_U{U}_K4"]["snr"]["20"]["cos"]
print(f"U={U}: per-user {v:.3f} aggregate {U*v:.2f}")
print("overload ratio:",
round(6 * R["prop_U6_K4"]["snr"]["20"]["cos"] / (4 * conv_cos), 2))
print("randmask@20:", round(R["randmask_U4_K4"]["snr"]["20"]["cos"], 3))
for s in ["0", "5", "15", "20", "30"]:
print(f"todma24@{s}: {R['todma_T24_L128'][s]['cos']:.3f} "
f"prop@{s}: {R['prop_U4_K4']['snr'][s]['cos']:.3f}")
for k in ["ksweep_U4_K1", "ksweep_U4_K2", "prop_U4_K4", "ksweep_U4_K8"]:
print(k, "0dB:", round(R[k]["snr"]["0"]["cos"], 3),
"20dB:", round(R[k]["snr"]["20"]["cos"], 3))
if "mamlD_U4_K4" in R:
print("distil(MAML)@20:", round(R["mamlD_U4_K4"]["snr"]["20"]["cos"], 3))