Statistics of grain sizes and orientations in metals correlate to the
material's mechanical properties. Reproducing representative volume elements
for further analysis of deformation and failure in metals, like 316L stainless
steel, is particularly important due to their wide use in manufacturing goods
today. Two approaches, initially created for video games, were considered for
the procedural generation of representative grain microstructures. The first is
the Wave Function Collapse (WFC) algorithm, and the second is constraint
propagation and probabilistic inference through Markov Junior, a free and
open-source software. This study aimed to investigate these two algorithms'
effectiveness in using reference electron backscatter diffraction (EBSD) maps
and recreating a statistically similar one that could be used in further
research. It utilized two stainless steel EBSD maps as references to test both
algorithms. First, the WFC algorithm was too constricting and, thus, incapable
of producing images that resembled EBSDs. The second, MarkovJunior, was much
more effective in creating a Voronoi tessellation that could be used to create
an EBSD map in Python. When comparing the results between the reference and the
generated EBSD, we discovered that the orientation and volume fractions were
extremely similar. With the study, it was concluded that MarkovJunior is an
effective machine learning tool that can reproduce representative grain
microstructures.

用参考的电子背散射衍射图 (Electron Backscatter Diffraction, EBSD) 地图测试了 Wave Function Collapse (WFC) 算法和 Markov Junior 算法两种方法在生成金属晶粒结构中的效果，并发现 Markov Junior 是一种有效的机器学习工具。

使用波函数坍缩算法生成晶粒取向的过程

Procedural Generation of Grain Orientations using the Wave Function  Collapse Algorithm

In this paper we consider the problem of multi-view face detection. While
there has been significant research on this problem, current state-of-the-art
approaches for this task require annotation of facial landmarks, e.g. TSM [25],
or annotation of face poses [28, 22]. They also require training dozens of
models to fully capture faces in all orientations, e.g. 22 models in HeadHunter
method [22]. In this paper we propose Deep Dense Face Detector (DDFD), a method
that does not require pose/landmark annotation and is able to detect faces in a
wide range of orientations using a single model based on deep convolutional
neural networks. The proposed method has minimal complexity; unlike other
recent deep learning object detection methods [9], it does not require
additional components such as segmentation, bounding-box regression, or SVM
classifiers. Furthermore, we analyzed scores of the proposed face detector for
faces in different orientations and found that 1) the proposed method is able
to detect faces from different angles and can handle occlusion to some extent,
2) there seems to be a correlation between dis- tribution of positive examples
in the training set and scores of the proposed face detector. The latter
suggests that the proposed methods performance can be further improved by using
better sampling strategies and more sophisticated data augmentation techniques.
Evaluations on popular face detection benchmark datasets show that our
single-model face detector algorithm has similar or better performance compared
to the previous methods, which are more complex and require annotations of
either different poses or facial landmarks.

本文提出了一种基于卷积神经网络的 Deep Dense Face Detector (DDFD) 方法，不仅不需要姿态或者地标注释，而且能够探测多个视角下人脸的位置，并且表现出与其他方法相当或优于其他更为复杂的方法。