refactor: 将子模块转为普通目录，移除外部 git 依赖

- 移除 JEPA/lejepa-identifiability 子模块 gitlink
- 移除 research/multiply/MultiPLY 子模块 gitlink
- 删除 .gitmodules（不再有外部 URL 依赖）
- 两个目录内容作为普通文件纳入主仓库追踪
- 删除各自内部 .git 目录，消除嵌套 git 仓库

JEPA/lejepa-identifiability/experiments/lejepa_id/data.py

@@ -0,0 +1,36 @@
+"""Data generation: latent sources and OU augmentation."""
+
+import math
+import torch
+
+
+def _gennorm_unit_var_scale(alpha):
+    """Scale β so gennorm(α, β) has unit variance: β = sqrt(Γ(1/α) / Γ(3/α))."""
+    return math.exp(0.5 * (math.lgamma(1.0 / alpha) - math.lgamma(3.0 / alpha)))
+
+
+def sample_latents(D, N, dist="gaussian", device="cuda", alpha=None):
+    """Sample D points in R^N (unit variance)."""
+    if dist == "gaussian":
+        return torch.randn(D, N, device=device)
+    elif dist == "laplace":
+        return torch.distributions.Laplace(0, 1 / (2 ** 0.5)).sample((D, N)).to(device)
+    elif dist == "gennorm":
+        if alpha is None:
+            raise ValueError("gennorm requires alpha")
+        scale = _gennorm_unit_var_scale(alpha)
+        u = torch.distributions.Gamma(1.0 / alpha, 1.0).sample((D, N)).to(device)
+        sign = torch.randint(0, 2, (D, N), device=device).float() * 2 - 1
+        return scale * sign * u.pow(1.0 / alpha)
+    else:
+        raise ValueError(f"Unknown distribution: {dist}")
+
+
+def ou_augment(z, rho, n_views=2, dist="gaussian", alpha=None):
+    """OU channel: z' = ρz + √(1-ρ²)η, η drawn from same dist as source.
+    Returns (V, B, N)."""
+    fac = (1 - rho ** 2) ** 0.5
+    D, N = z.shape
+    eta = sample_latents(n_views * D, N, dist=dist, device=z.device, alpha=alpha)
+    eta = eta.reshape(n_views, D, N)
+    return rho * z.unsqueeze(0) + fac * eta