本文介绍了C51语言中变量在内存中的分配情况，以及建议的使用方法。

swin transformer 预训练模型swin_large_patch4_window12_384_22kto1k.pth

Large Scale Machine Learning with Python [PDF + EPUB + CODE] Packt Publishing | August 4, 2016 | English | 439 pages Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.

wav2vec2-finetune 尼泊尔文： ： 旁遮普语： ：

提出了一种提高实际复杂场景中光流估计的鲁棒性和准确性的方法。 该方法克服了使用亮度恒定性和梯度恒定性的组合引起的照明变化引起的限制。 此外，该方法通过同时应用双边滤波器和惩罚函数，提高了光流估计的可靠性。 此外，它采用对偶算法和从粗到精方案提高了估计光流的计算能力和适用性。 我们使用来自Middlebury光流数据库的场景和真实的复杂场景来验证所提出的方法。 结果表明，所提出的方法对光照变化具有鲁棒性，并提高了光流估计的准确性和提取目标边缘的能力。

为了在大图中找到两点之间的最短路径，我们先通过宽度优先搜索为每个点建立距离标签索引。关键是在宽度优先搜索是进行剪枝。

Discrete Berth Allocation，Iterative Variable Grouping Genetic Algorithm

基于Mapreduce的大规模图强连通分量算法，吕璐，谢磊，有向图强连通分量是图论中的基本问题。强连通分量算法一般都是基于深度优先搜索，但难于在大规模图上并行实现。本文提出了一种基

本构模型matlab代码弹塑性大变形 考虑到大变形并采用弹塑性本构模型的有限元程序的Matlab代码。 依存关系 “有限元方法的计算机辅助学习”是一组用于编写FE代码的例程。

文的其余部分结构如下：在第3节中，我们描述了我们的高效立体匹配方法。第4节报告了真实世界数据集的实验结果以及与Middlebury基准图像上的各种其他方法的比较