Prompt Description
APPROVED Technical Illustration Request: Multi-modal Feature Fusion Neural Network Architecture Role: Technical Illustrator for Computer Science Research. Subject: A neural network architecture diagram illustrating 'Multi-modal Feature Fusion'. Style: Academic, IEEE standard, flat 2D vector, orthogonal lines, high contrast. White background. Layout & Components (Left to Right flow): 1. Input Phase (Left): Three parallel input vectors stacked vertically: * Top: A blue vector bar labeled '$V_{sem}$ (Semantic)'. * Middle: A green vector bar labeled '$V_{graph}$ (Graph)'. * Bottom: An orange vector bar labeled '$V_{stat}$ (Statistical)'. 2. Alignment Phase (Middle-Left): * The Top ($V_{sem}$) and Middle ($V_{graph}$) vectors pass through unchanged (identity). * The Bottom ($V_{stat}$) vector passes through a small neural network block labeled 'MLP Alignment'. * The output of this block is a new vector labeled '$H_{stat}$'. 3. Fusion Phase (Center): * Show the three vectors ($V_{sem}$, $V_{graph}$, $H_{stat}$) merging into one long vertical block. * Label this merging operation with the symbol '||' (Concatenation).
![```python
import torch
import torch.nn as nn
import torch.nn.functional as F
class LightGCNBase(nn.Module):
"""Inner LightGCN model, responsible for propagation on a single graph and outputting the embeddings of all nodes."""
def __init__(self, num_nodes: int, embedding_dim: int = 64, n_layers: int = 3):
super().__init__()
self.num_nodes = num_nodes
self.embedding_dim = embedding_dim
self.n_layers = n_layers
self.embedding = nn.Embedding(num_nodes, embedding_dim)
nn.init.normal_(self.embedding.weight, std=0.1)
def propagate(self, graph: torch.Tensor) -> torch.Tensor:
"""Performs K layers of LightGCN propagation on the given graph, returning the final embeddings of all nodes."""
all_emb = self.embedding.weight # [N, d]
embs = [all_emb]
for _ in range(self.n_layers):
all_emb = torch.sparse.mm(graph, all_emb) # [N, d]
embs.append(all_emb)
embs_stack = torch.stack(embs, dim=0) # [K+1, N, d]
out = torch.mean(embs_stack, dim=0) # [N, d]
return out
class MultiGraphLightGCN(nn.Module):
```](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2FKBimdOJW5qMAfEg0lBE9pzFIVeNktoy7%2F9984c727-08a9-4276-9a16-4f3639b94e55%2Ff9426d87-f474-4d36-8cdf-66efb9dfe471.png&w=3840&q=75)
![Automated deployment and maintenance are fundamental to reducing operational costs and improving efficiency. The primary goal is to replace traditional manual, repetitive tasks with standardized, automated processes for system deployment, version upgrades, security patch distribution, and equipment maintenance. This approach reduces the risk of human error, enhances operational efficiency, and lowers labor costs. Traditional manual methods suffer from complex processes, difficulties in cross-system collaboration, and long execution cycles, leading to high labor costs and inefficient resource allocation.
The core operational logic of this scenario follows a closed-loop management system: "Request Initiation - Solution Orchestration - Resource Scheduling - Automated Execution - Result Verification - Log Archiving." This creates a multi-level collaboration mechanism involving front-line execution and feedback, second-line solution design, and third-line standards development. Key constraints include task execution timeliness (efficient completion during off-peak hours), resource utilization thresholds (controlling hardware resource consumption), and process compliance rates. Standardized operation allows for the precise collection of core data such as hardware resource usage, labor input, and process efficiency, providing support for extracting cost-related metrics for feature systems. [An example diagram illustrating the closed-loop process of automated deployment and maintenance and multi-level collaboration should be inserted here, clearly showing the flow logic of each stage and the operational responsibilities of the first, second, and third lines of support.]](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2Fe5JPDHrDFElOkDEf6YrTWOd5gnW0wSso%2Fadcb87e4-8299-48de-9e25-01cc3de0b0ca%2Fa0475ca4-265c-4e97-90b2-3639e934dd48.png&w=3840&q=75)