Super Brain

本文介绍了一档科学真人秀节目SuperBrain中的一项挑战任务——参赛者需在短时间内记忆两组整数序列,并找出唯一一个在这两组序列中都出现的整数。文章提供了用于解决此问题的C++程序示例,该程序能够处理多个测试用例并输出正确答案。

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Super Brain is a famous scientific reality and talent show aiming to find people with exceptional brainpower.

In one of the challenges, two integer sequences  and  of length  are given to the contestant. It's guaranteed that  and  hold for all , and there is exactly one integer in the first sequence which also appears in the second sequence. The contestant has to memorize the two sequences in a very short time, and find the integer which appears in both sequences correctly.

As a technical staff of the show, you are required to write a program and find out the correct integer.

Input

There are multiple test cases. The first line of the input contains an integer , indicating the number of test cases. For each test case:

The first line contains an integer  (), indicating the length of the sequence.

The second line contains  integers  (), indicating the first sequence.

The third line contains  integers  (), indicating the second sequence.

It's guaranteed that  and  hold for all , and there is exactly one integer in the first sequence which also appears in the second sequence.

It's also guaranteed that the sum of  over all test cases will not exceed .

<h4< dd="">Output

For each test case output one line containing one integer, indicating the integer which appears in both sequences.

<h4< dd="">Sample Input
3
3
3 1 2
5 3 4
2
38324 14122
38323 14122
1
180310
180310
<h4< dd="">Sample Output
3
14122
180310

这道题我能说我wa了好几次嘛w(゚Д゚)w,老是超时......

#include<cstring>
#include<cstdio>
#include<iostream>
#include<cstdlib>
using namespace std;
int hash[1000001];
int main()
{
    int t,n;
    int x;
    scanf("%d",&t);
    while(t--)
    {
        memset(hash,0,sizeof(hash));
        scanf("%d",&n);
        for(int i=0;i<2*n;i++)
          {
              scanf("%d",&x);
              hash[x]++;
              if(hash[x]==2)
                  printf("%d\n",x);
          }
    }
    return 0;
}

### 2D U-Net在脑部图像分割中的应用 #### 背景介绍 U-Net是一种专门为生物医学图像分割设计的卷积神经网络架构,在处理具有挑战性的医疗成像任务方面表现出色。该模型通过引入跳跃连接(skip connections),有效地解决了下采样过程中丢失的空间信息问题,从而提高了分割精度[^2]。 #### 架构特点 对于二维(2D)版本的U-Net而言,其核心结构保持不变——由编码器路径和解码器路径组成。前者负责特征提取并逐渐减少空间分辨率;后者则逐步恢复原始输入尺寸的同时融合来自编码阶段的信息。这种对称的设计使得U-Net特别适合于需要精确边界定位的任务,如脑部MRI扫描中的病变区域识别。 具体到脑部图像处理领域,2D U-Net可以应用于多种场景: - **肿瘤检测**:自动标记恶性或良性肿块的位置及其范围。 - **白质/灰质分类**:区分大脑内部不同类型的组织成分。 - **血管分析**:描绘血流通道的具体形态学特征。 为了适应特定的应用需求,研究者们还开发了许多改进版的U-Net变种,例如加入了递归机制来增强局部细节捕捉能力的R2U-Net[^1],以及利用多尺度跳过链接优化特征表达力更强的UNet++[^3]。 #### 实现示例 下面给出一段简单的Python代码片段用于构建基础形式下的2D U-Net,并训练它来进行脑部CT影像的数据集上的简单实验: ```python import torch.nn as nn from torchvision import models class UNet(nn.Module): def __init__(self, num_classes=1): super().__init__() self.encoder = models.resnet18(pretrained=True) layers = list(self.encoder.children())[:-2] self.encoder_layers = nn.Sequential(*layers) self.decoder_blocks = [] filters = [512, 256, 128, 64] for i in range(len(filters)-1): block = nn.ConvTranspose2d( in_channels=filters[i], out_channels=filters[i+1], kernel_size=(2, 2), stride=(2, 2)) self.decoder_blocks.append(block) self.final_conv = nn.Conv2d(in_channels=64, out_channels=num_classes, kernel_size=(1, 1)) def forward(self, x): enc_outputs = [] # Encoder path for layer in self.encoder_layers: x = layer(x) enc_outputs.insert(0, x.clone()) # Decoder with skip connections dec_output = None for idx, decoder_block in enumerate(self.decoder_blocks): if idx == 0: dec_output = decoder_block(enc_outputs[idx]) else: dec_output = torch.cat([dec_output, enc_outputs[idx]], dim=1) dec_output = decoder_block(dec_output) output = self.final_conv(dec_output) return output ``` 此段代码定义了一个简化版的2DU-Net实现方式,其中采用了预训练好的ResNet作为编码部分的基础框架,并通过反卷积操作实现了逐层上采样的过程。需要注意的是实际项目中可能还需要考虑更多因素比如损失函数的选择、正则化手段等等。
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