A segmentation fault is a common run-time error for C programs. Use examples (several lines of C code for each cause) to show two causes of this problem.

"段错误"是 C 程序常见的运行时错误。下面给出两种导致这个问题的原因的代码示例：

1. 访问未初始化的指针：

#include <stdio.h>

int main() {
  int *ptr;
  *ptr = 5;
  return 0;
}

2. 访问超出数组范围的内存：

#include <stdio.h>

int main() {
  int arr[5];
  arr[10] = 5;
  return 0;
}

这两种情况都会导致段错误，因为它们都在访问不属于程序的内存。

相关问题

The human visual cortex is biased towards shape components while CNNs produce texture biased features. This fact may explain why the performance of CNN significantly degrades with low-labeled input data scenarios. In this paper, we propose a frequency re-calibration U-Net (FRCU-Net) for medical image segmentation. Representing an object in terms of frequency may reduce the effect of texture bias, resulting in better generalization for a low data regime. To do so, we apply the Laplacian pyramid in the bottleneck layer of the U-shaped structure. The Laplacian pyramid represents the object proposal in different frequency domains, where the high frequencies are responsible for the texture information and lower frequencies might be related to the shape. Adaptively re-calibrating these frequency representations can produce a more discriminative representation for describing the object of interest. To this end, we first propose to use a channel-wise attention mechanism to capture the relationship between the channels of a set of feature maps in one layer of the frequency pyramid. Second, the extracted features of each level of the pyramid are then combined through a non-linear function based on their impact on the final segmentation output. The proposed FRCU-Net is evaluated on five datasets ISIC 2017, ISIC 2018, the PH2, lung segmentation, and SegPC 2021 challenge datasets and compared to existing alternatives, achieving state-of-the-art results.请详细介绍这段话中的技术点和实现方式

这段话主要介绍了一种针对医学图像分割的新型网络结构——频率重新校准U-Net（FRCU-Net）。该网络结构的核心思想是将物体表示为频率，在低数据情况下降低纹理偏差的影响，从而获得更好的泛化性能。具体实现方式包括以下技术点：

1. 使用Laplacian金字塔将物体表示在不同的频率域中，其中高频率负责纹理信息，而较低的频率可能与物体的形状相关。

2. 通过自适应重新校准这些频率表示，以产生更具有区分性的物体表示。为此，首先提出使用通道注意力机制来捕捉一个特征图集合中各通道之间的关系，然后通过一个非线性函数将金字塔各层的特征提取结果组合起来，以得到最终的分割输出。

3. 在ISIC 2017、ISIC 2018、PH2、肺分割和SegPC 2021数据集上对FRCU-Net进行了评估，并与现有的替代方案进行了比较，取得了最先进的结果。

综上所述，FRCU-Net通过将物体表示为频率，以及使用自适应的频率重新校准和通道注意力机制，实现了对低数据情况下医学图像分割的有效处理。

In the proposed DLR, a convolutional neural network (CNN) is used. CNN is a representative method used for deep learning, and it has been successfully applied to the field of image segmentation8. Recently, many groups have used CNN for the segmentation of medical images, and it has provided better results than traditional meth- ods9. In glioma segmentation based on MR images, most of the CNN methods were proposed for high-grade gliomas10, 11. Compared with high-grade gliomas, low-grade gliomas are smaller and have lower contrast with the surrounding tissues12. Existing CNN structures would not work well for segmentation of low-grade gliomas. A major architecture adjustment of CNN is therefore essential for both image segmentation and feature extraction. To address the challenging characteristics of low-grade gliomas, we used a modified CNN architecture with 6 convolutional layers and a fully connected layer with 4096 neurons for segmentation. 解释

这段文字讲述了一种名为DLR的分割方法，其中使用了卷积神经网络（CNN），这是一种应用于深度学习的代表性方法，并且已经成功地应用于图像分割领域。最近，许多团队已经将CNN用于医学图像分割，并且相比传统方法，CNN方法提供了更好的结果。然而，对于低级别胶质瘤的分割，由于其大小较小且与周围组织对比度较低，现有的CNN结构无法很好地工作。因此，需要进行主要的架构调整来进行图像分割和特征提取。为了解决低级别胶质瘤的挑战性特征，作者使用了修改后的CNN架构，其中包括6个卷积层和一个有4096个神经元的全连接层用于分割。