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/losses.py

@@ -0,0 +1,53 @@
+"""Loss functions."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SIGReg(nn.Module):
+    """Sliced characteristic function regularizer (Balestriero & LeCun 2025)."""
+
+    def __init__(self, knots=17, n_slices=256, t_max=3.0):
+        super().__init__()
+        self.n_slices = n_slices
+        t = torch.linspace(0, t_max, knots)
+        dt = t_max / (knots - 1)
+        w = torch.full((knots,), 2 * dt)
+        w[[0, -1]] = dt
+        self.register_buffer("t", t)
+        self.register_buffer("phi", torch.exp(-t**2 / 2))
+        self.register_buffer("weights", w * torch.exp(-t**2 / 2))
+
+    def forward(self, h):
+        """h: (V, B, N) -> scalar."""
+        flat = h.flatten(0, 1)
+        A = F.normalize(torch.randn(flat.size(-1), self.n_slices, device=flat.device), dim=0)
+        xt = (flat @ A).unsqueeze(-1) * self.t
+        err = (xt.cos().mean(0) - self.phi) ** 2 + xt.sin().mean(0) ** 2
+        return (err @ self.weights).mean() * flat.size(0)
+
+
+def whitening_loss(h):
+    """||Cov(h) - I||²_F.  h: (V, B, N) -> scalar."""
+    flat = h.flatten(0, 1)
+    flat = flat - flat.mean(dim=0)
+    cov = (flat.T @ flat) / (flat.shape[0] - 1)
+    return (cov - torch.eye(flat.shape[1], device=h.device)).square().mean()
+
+
+def alignment_loss(h):
+    """Pull positive-pair views together. h: (V, B, N) -> scalar."""
+    return (h.mean(0) - h).square().mean()
+
+
+def infonce_loss(h, sigma):
+    """Symmetric Gaussian-kernel InfoNCE: sim(u, v) = -||u - v||² / (2σ²).
+    h: (V, B, N) with V=2 views. Negatives are other batch elements.
+    """
+    h1, h2 = h[0], h[1]                                              # (B, N) each
+    d12 = ((h1.unsqueeze(1) - h2.unsqueeze(0)) ** 2).sum(-1)         # (B, B)
+    sim = -d12 / (2 * sigma ** 2)
+    loss_a = -(sim.diag() - torch.logsumexp(sim, dim=1)).mean()
+    loss_b = -(sim.diag() - torch.logsumexp(sim, dim=0)).mean()
+    return 0.5 * (loss_a + loss_b)