Bridging signals||POJ1631

针对大规模集成电路设计中信号交叉问题,提出一种通过寻找最长非交叉连接子序列来最小化信号桥接数量的方法。该方法利用最长递增子序列(LIS)算法解决信号路由设计难题,避免信号线在芯片上相互交叉。

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link:http://poj.org/problem?id=1631
Description

'Oh no, they've done it again', cries the chief designer at the Waferland chip factory. Once more the routing designers have screwed up completely, making the signals on the chip connecting the ports of two functional blocks cross each other all over the place. At this late stage of the process, it is too expensive to redo the routing. Instead, the engineers have to bridge the signals, using the third dimension, so that no two signals cross. However, bridging is a complicated operation, and thus it is desirable to bridge as few signals as possible. The call for a computer program that finds the maximum number of signals which may be connected on the silicon surface without crossing each other, is imminent. Bearing in mind that there may be thousands of signal ports at the boundary of a functional block, the problem asks quite a lot of the programmer. Are you up to the task?

A typical situation is schematically depicted in figure 1. The ports of the two functional blocks are numbered from 1 to p, from top to bottom. The signal mapping is described by a permutation of the numbers 1 to p in the form of a list of p unique numbers in the range 1 to p, in which the i:th number specifies which port on the right side should be connected to the i:th port on the left side.Two signals cross if and only if the straight lines connecting the two ports of each pair do.

Input

On the first line of the input, there is a single positive integer n, telling the number of test scenarios to follow. Each test scenario begins with a line containing a single positive integer p < 40000, the number of ports on the two functional blocks. Then follow p lines, describing the signal mapping:On the i:th line is the port number of the block on the right side which should be connected to the i:th port of the block on the left side.

Output

For each test scenario, output one line containing the maximum number of signals which may be routed on the silicon surface without crossing each other.

Sample Input

4
6
4
2
6
3
1
5
10
2
3
4
5
6
7
8
9
10
1
8
8
7
6
5
4
3
2
1
9
5
8
9
2
3
1
7
4
6

Sample Output

3
9
1
4

题解:最长子序列的长度,LIS
AC代码:

#include<cstdio>
#include<cstring>
#include<cstdlib>
#include<cmath>
#include<algorithm>
#include<iostream>
using namespace std;
int INF=0x3f3f3f3f;
int dp[40005],a[40005];
int main()
{
    int i,n;
    int t;
    scanf("%d",&t);
    while(t--)
    {
        scanf("%d",&n);
        for(i=0;i<n;i++)
        {
            scanf("%d",&a[i]);
            dp[i]=INF;//先给dp数组初始化INF 
        }
        for(i=0;i<n;i++)
        {
            *lower_bound(dp,dp+n,a[i])=a[i];
            //该函数是搜索dp数组内,第一个大于等于 a【i】的元素地址 
        }
        printf("%d\n",lower_bound(dp,dp+n,INF)-dp);//第一个为INF的地址减去首地址就是长度 
    }
return 0;
}
### Bridging CNN in Deep Learning Architecture and Implementation In the context of deep learning architectures, bridging Convolutional Neural Networks (CNNs) refers to methods that integrate or connect different components within a network structure to enhance performance. For instance, while Faster R-CNN uses shared feature maps for object detection tasks[^1], other models like Mask R-CNN extend this concept by adding additional branches specifically designed for mask prediction. For bridging CNN implementations, one approach involves designing multi-task frameworks where multiple sub-networks share common layers but diverge at later stages to handle specific objectives such as classification, regression, or segmentation. Another method includes incorporating skip connections between encoder-decoder structures which help preserve spatial information across various resolutions during processing. Additionally, when considering natural language processing applications, CNN-based sentence modeling techniques demonstrate how convolution operations can serve as combination operators with local selection functions. Through progressively deeper layers, these models capture broader contexts within sentences leading towards fixed-dimensional vector representations[^4]. To implement bridged CNN architectures effectively: - Utilize pre-trained weights on large datasets whenever possible. - Experiment with varying depths and widths of convolutions based upon task requirements. - Incorporate normalization strategies including batch norm after each layer activation. ```python import torch.nn as nn class BridgeCNN(nn.Module): def __init__(self): super(BridgeCNN, self).__init__() # Shared Feature Extraction Layers self.shared_conv = nn.Sequential( nn.Conv2d(3, 64, kernel_size=7), nn.ReLU(), nn.MaxPool2d(kernel_size=2)) # Task Specific Branches self.branch_a = ... # Define branch A specifics self.branch_b = ... # Define branch B specifics def forward(self, x): features = self.shared_conv(x) out_a = self.branch_a(features) out_b = self.branch_b(features) return out_a, out_b ```
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