This paper presents a novel framework for Bayesian structural model updating
and proposes a method that utilizes the surrogate unimodal encoders of a
multimodal variational autoencoder. This method facilitates an efficient
nonparametric estimation of the likelihood describing the observed data. It is
particularly suitable for high-dimensional correlated simultaneous observations
applicable to various dynamic analysis models. The proposed approach is
benchmarked using a numerical model of a single-story frame building with
acceleration and dynamic strain measurements.

本论文提出了一种用于贝叶斯结构模型更新的新颖框架，并提出了一种利用多模态变分自动编码器的单峰编码器代替方法。该方法通过高效的非参数估计来描述观测数据的似然性，特别适用于适用于各种动态分析模型的高维相关同时观测。该方法使用加速度和动态应变测量对单层框架建筑的数值模型进行了基准测试。

贝叶斯结构模型更新与多模态变分自动编码器

Bayesian Structural Model Updating with Multimodal Variational  Autoencoder

Recent advancements in non-invasive detection of cardiac hemodynamic
instability (CHDI) primarily focus on applying machine learning techniques to a
single data modality, e.g. cardiac magnetic resonance imaging (MRI). Despite
their potential, these approaches often fall short especially when the size of
labeled patient data is limited, a common challenge in the medical domain.
Furthermore, only a few studies have explored multimodal methods to study CHDI,
which mostly rely on costly modalities such as cardiac MRI and echocardiogram.
In response to these limitations, we propose a novel multimodal variational
autoencoder ($\text{CardioVAE}_\text{X,G}$) to integrate low-cost chest X-ray
(CXR) and electrocardiogram (ECG) modalities with pre-training on a large
unlabeled dataset. Specifically, $\text{CardioVAE}_\text{X,G}$ introduces a
novel tri-stream pre-training strategy to learn both shared and
modality-specific features, thus enabling fine-tuning with both unimodal and
multimodal datasets. We pre-train $\text{CardioVAE}_\text{X,G}$ on a large,
unlabeled dataset of $50,982$ subjects from a subset of MIMIC database and then
fine-tune the pre-trained model on a labeled dataset of $795$ subjects from the
ASPIRE registry. Comprehensive evaluations against existing methods show that
$\text{CardioVAE}_\text{X,G}$ offers promising performance (AUROC $=0.79$ and
Accuracy $=0.77$), representing a significant step forward in non-invasive
prediction of CHDI. Our model also excels in producing fine interpretations of
predictions directly associated with clinical features, thereby supporting
clinical decision-making.

利用低成本的胸部 X 光和心电图模态，结合大规模未标记数据的预训练方法，我们提出了一种新颖的多模态变分自编码器（CardioVAE_X,G），以实现对心脏血流动力学不稳定性（CHDI）的无创预测，在预测 CHDI 方面显示出有希望的性能，对相关的临床特征解释上也表现出色，从而支持临床决策。

低成本心脏血液动力学不稳定性检测的多模态变分自编码器

Multimodal Variational Autoencoder for Low-cost Cardiac Hemodynamics  Instability Detection

We propose a new multimodal variational autoencoder that enables to generate
from the joint distribution and conditionally to any number of complex
modalities. The unimodal posteriors are conditioned on the Deep Canonical
Correlation Analysis embeddings which preserve the shared information across
modalities leading to more coherent cross-modal generations. Furthermore, we
use Normalizing Flows to enrich the unimodal posteriors and achieve more
diverse data generation. Finally, we propose to use a Product of Experts for
inferring one modality from several others which makes the model scalable to
any number of modalities. We demonstrate that our method improves likelihood
estimates, diversity of the generations and in particular coherence metrics in
the conditional generations on several datasets.

本文介绍了一种新的多模态变分自编码器，它可以从联合分布中生成或有条件地生成任意数量的复杂模态。该方法使用深度典型相关性分析嵌入，条件化单模态后验分布，从而可以跨模态生成产生更一致性的结果。此外，本文使用正则化流准确表达单模态后验分布，实现更多样化的数据生成。最后，文章提出了一种基于 Expert 乘积的推断模式，使得模型可适用于任意数量的模态。在多个数据集上的实验表明，本文的方法可以提高生成数据的合理性和多样性，以及特定条件生成的连贯度量。