```python
# model.py
# SFAN + CMEFS unified model for semantic music culture modeling
# Based on Feng Liu (2025)​:contentReference[oaicite:11]{index=11}​:contentReference[oaicite:12]{index=12}
import torch
import torch.nn as nn
import torch.nn.functional as F
class MultiHeadAttention(nn.Module):
"""Multi-head self-attention for multimodal fusion."""
def __init__(self, dim, heads=4):
super().__init__()
self.qkv = nn.Linear(dim, dim * 3)
```python
# model.py
# GEMFEN + AKIS for Intelligent Rural Evaluation System
# Based on Zhu et al. (2025)​:contentReference[oaicite:11]{index=11}
import torch
import torch.nn as nn
import torch.nn.functional as F
class GraphConvLayer(nn.Module):
"""Basic GNN layer (Eq. 1 & 4)."""
def __init__(self, in_dim, out_dim):
super().__init__()
self.linear = nn.Linear(in_dim, out_dim)
def forward(self, x, adj):
h = torch.matmul(adj, x)
```python
# model.py
# Implementation of EVCAN + AVPAM for Art Style Recognition
# Based on equations and architecture in Shi & He (2025) :contentReference[oaicite:9]{index=9}
import torch
import torch.nn as nn
import torch.nn.functional as F
import math
class MultiScaleConv(nn.Module):
"""Multi-scale feature extraction (Eq. 7–10)."""
def __init__(self, in_ch, out_ch, scales=(3,5,7)):
super().__init__()
self.branches = nn.ModuleList([
nn.Conv2
```python
# model.py
# TSLM + AKIS (minimal PyTorch implementation)
# References to equations and sections follow the uploaded paper. :contentReference[oaicite:10]{index=10}
from typing import Optional, Tuple
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
class SinusoidalPositionalEncoding(nn.Module):
"""
Classic sinusoidal positional encodings (paper Eq. (9)-(10)). :contentReference[oaicite:11]{index=11}
"""
def __init__(self, d
```python
# model.py
# MPFN-AMFS: Multimodal Pose Fusion Network + Adaptive Multimodal Fusion Strategy
# Author: Chen Niyun (Macao Polytechnic University, 2024) :contentReference[oaicite:8]{index=8}
import torch
import torch.nn as nn
import torch.nn.functional as F
# ======== Encoders ========
class CNNEncoder(nn.Module):
"""Visual feature extractor (2D Conv + BN + ReLU)​:contentReference[oaicite:9]{index=9}"""
def __init__(self, in_ch=3, out_dim=128):
su
```python
# model.py
# DCMFN-AMFS: Deep Convolutional Multi-Scale Fusion Network with Adaptive Multi-Scale Fusion Strategy
# Based on Zhang et al. (2024), Comprehensive Evaluation of Green Building Indoor Environment :contentReference[oaicite:9]{index=9}
import torch
import torch.nn as nn
import torch.nn.functional as F
class ConvBlock(nn.Module):
"""Convolution + BN + ReLU"""
def __init__(self, in_ch, out_ch, k=3, s=1, p=1):
super().__init__()
self.conv
```python
# model.py
# RSAN-ARSAS: Residual Self-Attention Network with Adaptive Residual Strategy
# Based on: Zhou & Li, "Data-Driven Fault Diagnosis..." Jiangsu Normal University, 2024 :contentReference[oaicite:12]{index=12}
import torch
import torch.nn as nn
import torch.nn.functional as F
class ResidualBlock(nn.Module):
"""Standard residual block with GELU activation"""
def __init__(self, dim):
super().__init__()
self.fc1 = nn.Linear(dim, dim)
```python
# model.py
# SARP-ASAUE: Sparse Attention & Uncertainty Estimation for Risk Prediction
# Based on Bin Zhan (Zhejiang A&F University) 2024 :contentReference[oaicite:10]{index=10}
import torch
import torch.nn as nn
import torch.nn.functional as F
from typing import Optional, Tuple
class SparseAttention(nn.Module):
"""Sparse multi-head attention selecting top-k keys per query."""
def __init__(self, dim: int, num_heads: int = 4, topk: int = 16):
super()._
```python
# model.py
# CMARL-COS: Collaborative Multi-Agent RL with Constraint-Aware Optimization
# Author: Legend Co., Ltd. (Sharon's workspace)
# Inspired by: Yu Caixia, "Multi-Agent Collaboration and Reinforcement Learning-Driven Framework..." :contentReference[oaicite:5]{index=5}
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
def mlp(sizes: List[int], activation=nn.ReLU, out_
```python
# model.py
# Hybrid Attention-Fusion Network (HAFN)
# Based on Wang & Wu (2024)
import torch
import torch.nn as nn
import torch.nn.functional as F
# ----- Attention Modules -----
class CrossAttention(nn.Module):
def __init__(self, in_e, in_g, hidden):
super().__init__()
self.W_e = nn.Linear(in_e, hidden)
self.W_g = nn.Linear(in_g, hidden)
def forward(self, He, Hg):
# Compute attention maps
Ae = F.softmax(torch.matmu
```python
# model.py
# Temporal-Spatial Attention-Enhanced Reading Model (TSA-ERM)
# Based on Cuiqin Sun (2024): integrates BiRNN, CNN spatial encoding, and reinforcement learning optimization.
import torch
import torch.nn as nn
import torch.nn.functional as F
# ---------- Temporal Attention ----------
class TemporalAttention(nn.Module):
def __init__(self, hidden_dim):
super().__init__()
self.W = nn.Linear(hidden_dim, hidden_dim)
self.v = nn.Paramete
```python
# model.py
# Adaptive Channel Attention and Feature Fusion Network (ACAFNet)
# Implements ACAM, HFFM, CPL, and ACAFFS as per the paper.
import torch
import torch.nn as nn
import torch.nn.functional as F
# ---------- Adaptive Channel Attention Module ----------
class AdaptiveChannelAttention(nn.Module):
def __init__(self, channels, reduction=16):
super().__init__()
self.fc1 = nn.Linear(channels, channels // reduction)
self.fc2 = nn.Linear(ch
```python
# model.py
# Cross-Space Graph Attention Network (CSGAN)
# Implements multimodal encoding, cross-space attention, and graph convolution.
import torch
import torch.nn as nn
import torch.nn.functional as F
class CrossSpaceAttention(nn.Module):
"""Computes cross-space attention weights."""
def __init__(self, in_dim, attn_dim):
super().__init__()
self.query = nn.Linear(in_dim, attn_dim)
self.key = nn.Linear(in_dim, attn_dim)
self.v
```python
# model.py
# Reference implementation of MACEN:
# Multi-Scale Attention + Contextual Embedding + Feature Fusion + ACAS
# Input: (B, C=1, T, F) spectrogram-like tensors
# Output: class logits (or you can adapt for regression)
from typing import Sequence, Tuple
import torch
import torch.nn as nn
import torch.nn.functional as F
def _to_tokens(x: torch.Tensor) -> torch.Tensor:
"""
Convert (B, C, T, F) -> (B, N, C) tokens where N = T*F
We keep channels as feat
function my_custom_password_form() {
global $post;
$label = 'pwbox-' . (empty($post->ID) ? rand() : $post->ID);
$output = '
<div class="protected-content">
<h2>Private Access</h2>
<p>This page is password-protected. Please enter your password to continue:</p>
<form action="' . esc_url( site_url( 'wp-login.php?action=postpass', 'login_post' ) ) . '" method="post">
<label for="' . $label . '">Password:</label>
<input name="
// requestIdleCallbackを使ってブラウザのアイドル状態を検知するサンプル
// ブラウザがアイドル状態になるとコールバック関数が実行される
// タイムアウトを設定することも可能
requestIdleCallback(
(deadline) => {
console.log("タイムアウトで実行されました");
console.log(`残り時間: ${deadline.timeRemaining()}ms`);
console.log(`タイムアウトによる実行: ${deadline.didTimeout}`);
},
{ timeout: 2000 } // 2秒後に強制的に実行
);