Graph similarity measurement, which computes the distance/similarity between
two graphs, arises in various graph-related tasks. Recent learning-based
methods lack interpretability, as they directly transform interaction
information between two graphs into one hidden vector and then map it to
similarity. To cope with this problem, this study proposes a more interpretable
end-to-end paradigm for graph similarity learning, named Similarity Computation
via Maximum Common Subgraph Inference (INFMCS). Our critical insight into
INFMCS is the strong correlation between similarity score and Maximum Common
Subgraph (MCS). We implicitly infer MCS to obtain the normalized MCS size, with
the supervision information being only the similarity score during training. To
capture more global information, we also stack some vanilla transformer encoder
layers with graph convolution layers and propose a novel permutation-invariant
node Positional Encoding. The entire model is quite simple yet effective.
Comprehensive experiments demonstrate that INFMCS consistently outperforms
state-of-the-art baselines for graph-graph classification and regression tasks.
Ablation experiments verify the effectiveness of the proposed computation
paradigm and other components. Also, visualization and statistics of results
reveal the interpretability of INFMCS.

该研究提出了一种更易解释的基于最大公共子图推断的图相似性学习的端到端学习范式，并使用变压器编码器层和图卷积层堆叠一些基本组件，以捕获更全局的信息，并且通过可视化和统计结果验证了 INFMCS 的可解释性和对基于图的分类和回归任务的性能优于现有模型的能力。

最大公共子图推断，增强图相似性计算的可解释性

More Interpretable Graph Similarity Computation via Maximum Common Subgraph Inference

Our paper focuses on automating the generation of medical reports from chest
X-ray image inputs, a critical yet time-consuming task for radiologists. Unlike
existing medical re-port generation efforts that tend to produce human-readable
reports, we aim to generate medical reports that are both fluent and clinically
accurate. This is achieved by our fully differentiable and end-to-end paradigm
containing three complementary modules: taking the chest X-ray images and
clinical his-tory document of patients as inputs, our classification module
produces an internal check-list of disease-related topics, referred to as
enriched disease embedding; the embedding representation is then passed to our
transformer-based generator, giving rise to the medical reports; meanwhile, our
generator also pro-duces the weighted embedding representation, which is fed to
our interpreter to ensure consistency with respect to disease-related
topics.Our approach achieved promising results on commonly-used metrics
concerning language fluency and clinical accuracy. Moreover, noticeable
performance gains are consistently ob-served when additional input information
is available, such as the clinical document and extra scans of different views.

本研究旨在通过将病人的胸部 X 射线图像和临床病史文档作为输入，利用一种完全可微的端到端框架来生成既流畅又具有临床准确性的医学报告，并通过分类、生成和解释三个互补模块来实现。其表现出良好的语言流畅性和临床准确性，当可提供附加信息时，例如临床文档和不同视图的额外扫描，仍然可以始终显著提高性能。

自动生成精准流畅的医学 X 光报告

Automated Generation of Accurate \& Fluent Medical X-ray Reports

End-to-end neural network systems for automatic speech recognition (ASR) are
trained from acoustic features to text transcriptions. In contrast to modular
ASR systems, which contain separately-trained components for acoustic modeling,
pronunciation lexicon, and language modeling, the end-to-end paradigm is both
conceptually simpler and has the potential benefit of training the entire
system on the end task. However, such neural network models are more opaque: it
is not clear how to interpret the role of different parts of the network and
what information it learns during training. In this paper, we analyze the
learned internal representations in an end-to-end ASR model. We evaluate the
representation quality in terms of several classification tasks, comparing
phonemes and graphemes, as well as different articulatory features. We study
two languages (English and Arabic) and three datasets, finding remarkable
consistency in how different properties are represented in different layers of
the deep neural network.

本文分析了自动语音识别中使用的端到端神经网络模型的内部表示学习，对音素和字母、不同发音特征进行了比较，并发现不同特征在深度神经网络的不同层中的表示具有明显的一致性。