Bayesian approaches to reconstructing the evolutionary history of languages
rely on the tree model, which assumes that these languages descended from a
common ancestor and underwent modifications over time. However, this assumption
can be violated to different extents due to contact and other factors.
Understanding the degree to which this assumption is violated is crucial for
validating the accuracy of phylolinguistic inference. In this paper, we propose
a simple sanity check: projecting a reconstructed tree onto a space generated
by principal component analysis. By using both synthetic and real data, we
demonstrate that our method effectively visualizes anomalies, particularly in
the form of jogging.

提出了一种简单的方法，通过将重建的语言家族树投影到主成分分析的空间中，有效地可视化了异常，特别是在游走的形式中，以理解这种假设被破坏的程度对于验证语言起源演化推断的准确性至关重要。

主成分分析作为贝叶斯语言地球生物进化复原的健全性检查

Principal Component Analysis as a Sanity Check for Bayesian  Phylolinguistic Reconstruction

In this paper, we propose DeepTree, a novel method for modeling trees based
on learning developmental rules for branching structures instead of manually
defining them. We call our deep neural model situated latent because its
behavior is determined by the intrinsic state -encoded as a latent space of a
deep neural model- and by the extrinsic (environmental) data that is situated
as the location in the 3D space and on the tree structure. We use a neural
network pipeline to train a situated latent space that allows us to locally
predict branch growth only based on a single node in the branch graph of a tree
model. We use this representation to progressively develop new branch nodes,
thereby mimicking the growth process of trees. Starting from a root node, a
tree is generated by iteratively querying the neural network on the newly added
nodes resulting in the branching structure of the whole tree. Our method
enables generating a wide variety of tree shapes without the need to define
intricate parameters that control their growth and behavior. Furthermore, we
show that the situated latents can also be used to encode the environmental
response of tree models, e.g., when trees grow next to obstacles. We validate
the effectiveness of our method by measuring the similarity of our tree models
and by procedurally generated ones based on a number of established metrics for
tree form.

本文引入了 DeepTree 方法，该方法利用深度神经网络对树木的生长规则进行建模，从而生成各种形态的树木，同时展示了这种方法的有效性。