A fundamental problem associated with the task of network reconstruction from
dynamical or behavioral data consists in determining the most appropriate model
complexity in a manner that prevents overfitting, and produces an inferred
network with a statistically justifiable number of edges. The status quo in
this context is based on $L_{1}$ regularization combined with cross-validation.
As we demonstrate, besides its high computational cost, this commonplace
approach unnecessarily ties the promotion of sparsity with weight "shrinkage".
This combination forces a trade-off between the bias introduced by shrinkage
and the network sparsity, which often results in substantial overfitting even
after cross-validation. In this work, we propose an alternative nonparametric
regularization scheme based on hierarchical Bayesian inference and weight
quantization, which does not rely on weight shrinkage to promote sparsity. Our
approach follows the minimum description length (MDL) principle, and uncovers
the weight distribution that allows for the most compression of the data, thus
avoiding overfitting without requiring cross-validation. The latter property
renders our approach substantially faster to employ, as it requires a single
fit to the complete data. As a result, we have a principled and efficient
inference scheme that can be used with a large variety of generative models,
without requiring the number of edges to be known in advance. We also
demonstrate that our scheme yields systematically increased accuracy in the
reconstruction of both artificial and empirical networks. We highlight the use
of our method with the reconstruction of interaction networks between microbial
communities from large-scale abundance samples involving in the order of
$10^{4}$ to $10^{5}$ species, and demonstrate how the inferred model can be
used to predict the outcome of interventions in the system.

从动态或行为数据中重建网络的一个基本问题在于以能够防止过拟合的方式确定最适合的模型复杂度，并生成具有统计合理边数的推断网络。与常见做法 L1 正则化结合交叉验证相比，本文提出了一种基于层级贝叶斯推断和权重量化的非参数正则化方法，该方法能够提高网络重建的准确性，并且不需要事先知道边的数量。特别是在关于大规模物种数量的微生物群落间相互作用网络重建方面，我们展示了该方法的应用，并演示了利用推断模型预测系统干预结果的能力。

基于最小描述长度原理的网络重建

Network reconstruction via the minimum description length principle

Microbial communities play a key role in biological wastewater treatment
processes. Activated sludge settling characteristics, for example, are affected
by microbial community composition, varying by changes in operating conditions
and influent characteristics of wastewater treatment plants (WWTPs). Timely
assessment and prediction of changes in microbial composition leading to
settling problems, such as filamentous bulking (FB), can prevent operational
challenges, reductions in treatment efficiency, and adverse environmental
impacts. This study presents an innovative computer vision-based approach to
assess activated sludge-settling characteristics based on the morphological
properties of flocs and filaments in microscopy images. Implementing the
transfer learning of deep convolutional neural network (CNN) models, this
approach aims to overcome the limitations of existing quantitative image
analysis techniques. The offline microscopy image dataset was collected over
two years, with weekly sampling at a full-scale industrial WWTP in Belgium.
Multiple data augmentation techniques were employed to enhance the
generalizability of the CNN models. Various CNN architectures, including
Inception v3, ResNet18, ResNet152, ConvNeXt-nano, and ConvNeXt-S, were tested
to evaluate their performance in predicting sludge settling characteristics.
The sludge volume index was used as the final prediction variable, but the
method can easily be adjusted to predict any other settling metric of choice.
The results showed that the suggested CNN-based approach provides less
labour-intensive, objective, and consistent assessments, while transfer
learning notably minimises the training phase, resulting in a generalizable
system that can be employed in real-time applications.

本研究提出了基于计算机视觉的方法来评估活性污泥沉降特性，该方法使用了深度卷积神经网络模型进行图像分析，并取得了较少劳动力、客观和一致的评估结果，可用于实时应用。

应用深度卷积神经网络和迁移学习预测活性污泥沉降特性

Prediction of Activated Sludge Settling Characteristics from Microscopy  Images with Deep Convolutional Neural Networks and Transfer Learning

Using modern technology, it is now common to survey microbial communities by
sequencing DNA or RNA extracted in bulk from a given environment. Comparative
methods are needed that indicate the extent to which two communities differ
given data sets of this type. UniFrac, a method built around a somewhat ad hoc
phylogenetics-based distance between two communities, is one of the most
commonly used tools for these analyses. We provide a foundation for such
methods by establishing that if one equates a metagenomic sample with its
empirical distribution on a reference phylogenetic tree, then the weighted
UniFrac distance between two samples is just the classical
Kantorovich-Rubinstein (KR) distance between the corresponding empirical
distributions. We demonstrate that this KR distance and extensions of it that
arise from incorporating uncertainty in the location of sample points can be
written as a readily computable integral over the tree, we develop $L^p$
Zolotarev-type generalizations of the metric, and we show how the p-value of
the resulting natural permutation test of the null hypothesis "no difference
between the two communities" can be approximated using a functional of a
Gaussian process indexed by the tree. We relate the $L^2$ case to an ANOVA-type
decomposition and find that the distribution of its associated Gaussian
functional is that of a computable linear combination of independent $\chi_1^2$
random variables.

本论文基于引用的参考系统树的实验证据分布为基础，建立了一种基于 Kantorovich-Rubinstein 距离的 UniFrac 方法，并且将其扩展为 $L^p$ Zolotarev 度量，实现了与两个社区之间没有差异的自然置换测试的 $p$ 值。