34. (判断题, 2分) 1.1 Considering the bursty data transferring, a circuit switching network outperforms a packet switching network. • A. 对 • B. 错 35. (判断题, 2分) 1.2 There is no network congestion in a circuit switching network. • A. 对 • B. 错 36. (判断题, 2分) 1.3 Consider an application that transmits data at a steady rate, and once this application starts, it will stay on for a relatively long period of time. According to the characteristic, a packet-switched network would be more appropriate for this application than a circuit-switched network. • A. 对 • B. 错
时间: 2024-04-05 17:33:49 浏览: 137
34. (判断题, 2分) 1.1 错 (Packet switching networks are better suited for bursty data transferring.)
35. (判断题, 2分) 1.2 错 (Network congestion can occur in a circuit switching network.)
36. (判断题, 2分) 1.3 错 (A circuit-switched network would be more appropriate for an application that transmits data at a steady rate for a long period of time.)
相关问题
Write a script to safely delete all tables in the correct order, considering foreign key relationships.
编写一个脚本来安全地按顺序删除所有表,考虑到它们之间的外键关系,通常涉及到以下几个步骤。这里我会给出一个通用的Python示例,使用SQLite数据库为例,其他数据库(如MySQL、PostgreSQL等)可能会有所不同,但基本思路是一样的:
```python
import sqlite3
# 连接到SQLite数据库
def connect_to_db(database):
conn = sqlite3.connect(database)
cursor = conn.cursor()
return conn, cursor
# 获取所有表名并按依赖关系排序
def get_tables_and_relations(conn):
cursor.execute("SELECT name FROM sqlite_master WHERE type='table'")
tables = [row[0] for row in cursor.fetchall()]
# 获取表间的依赖关系(例如外键)
relations = {} # 表名: 参考表名列表
cursor.execute("""
SELECT sql FROM sqlite_master
WHERE type='view' AND tbl_name IN ({})
""".format(','.join('?' * len(tables))))
for view_sql in cursor.fetchall():
for table_name in tables:
if table_name in view_sql[0]:
relations[table_name] = relations.get(table_name, []) + [view_sql[0].split('.')[0]]
# 按照外键依赖顺序排序
ordered_tables = []
while tables:
current_table = tables.pop(0)
if not relations.get(current_table):
ordered_tables.append(current_table)
else:
for ref_table in relations[current_table]:
if ref_table not in tables and ref_table not in ordered_tables:
tables.remove(ref_table)
return ordered_tables
# 安全删除表
def delete_tables(conn, ordered_tables):
for table_name in ordered_tables:
cursor.execute(f"DROP TABLE IF EXISTS {table_name};")
conn.commit()
# 示例使用
database_path = 'your_database.db'
conn, cursor = connect_to_db(database_path)
tables_to_delete = get_tables_and_relations(conn)
delete_tables(conn, tables_to_delete)
conn.close()
```
produce a fusion map that explicitly quantifies the fusion weights for the features in each modality.
Producing a fusion map to quantify feature fusion weights typically involves techniques from both deep learning and knowledge representation. In the context of multi-modal data analysis, where you have information from different sources like images (deep learning) and structured data like knowledge graphs (knowledge graph embeddings), a fusion method might involve:
1. **Deep Learning**[^1]:
\- Apply a deep neural network, such as convolutional or recurrent networks, on one or more modalities to extract high-level representations.
```python
from keras.models import Model
image_model = create_image_model()
text_model = create_text_model()
# Obtain modal-specific feature maps
image_features = image_model(image_data)
text_features = text_model(text_data)
```
2. **Set Functions and Equivariance**:
- Use set functions like mean pooling or attention mechanisms to aggregate the features across modalities while considering their relative importance. This step ensures that the fusion process is equivariant to transformations in individual modalities.
```python
fused_features = combine_features(image_features, text_features, set_function)
```
3. **Knowledge Graph Embeddings**:
- For knowledge graph embeddings, use methods like TransE, DistMult, or ComplEx to represent entities and relations as vectors, which can be concatenated with the learned modality-specific features.
```python
kg_embedding_layer = KnowledgeGraphEmbeddingLayer(knowledge_graph)
kg_features = kg_embedding_layer(entity_ids)
```
4. **Fusion Weights Quantification**:
- Finally, apply an additional layer, often a fully connected layer or a self-attention mechanism, to learn the fusion weights between the modalities.
```python
fusion_weights = Dense(1, activation='softmax')(concatenated_features)
fused_output = dot([fused_features, fusion_weights])
```
The output `fused_output` would then contain the fused representation, where the values in the fusion weights array indicate the contribution of each modality to the final decision.