Consistency-based diagnosis is an established approach to diagnose technical
applications, but suffers from significant modeling efforts, especially for
dynamic multi-modal time series. Machine learning seems to be an obvious
solution, which becomes less obvious when looking at details: Which notion of
consistency can be used? If logical calculi are still to be used, how can
dynamic time series be transferred into the discrete world?
This paper presents the methodology Discret2Di for automated learning of
logical expressions for consistency-based diagnosis. While these logical
calculi have advantages by providing a clear notion of consistency, they have
the key problem of relying on a discretization of the dynamic system. The
solution presented combines machine learning from both the time series and the
symbolic domain to automate the learning of logical rules for consistency-based
diagnosis.

本论文提出了一种名为 Discret2Di 的方法，用于自动学习基于一致性的诊断的逻辑表达式。该方法通过结合时间序列和符号域的机器学习，解决了逻辑计算在动态系统离散化方面的关键问题。

基于深度学习的离散化模型诊断方法

Discret2Di -- Deep Learning based Discretization for Model-based  Diagnosis

Event temporal relation extraction~(ETRE) is usually formulated as a
multi-label classification task, where each type of relation is simply treated
as a one-hot label. This formulation ignores the meaning of relations and wipes
out their intrinsic dependency. After examining the relation definitions in
various ETRE tasks, we observe that all relations can be interpreted using the
start and end time points of events. For example, relation \textit{Includes}
could be interpreted as event 1 starting no later than event 2 and ending no
earlier than event 2. In this paper, we propose a unified event temporal
relation extraction framework, which transforms temporal relations into logical
expressions of time points and completes the ETRE by predicting the relations
between certain time point pairs. Experiments on TB-Dense and MATRES show
significant improvements over a strong baseline and outperform the
state-of-the-art model by 0.3\% on both datasets. By representing all relations
in a unified framework, we can leverage the relations with sufficient data to
assist the learning of other relations, thus achieving stable improvement in
low-data scenarios. When the relation definitions are changed, our method can
quickly adapt to the new ones by simply modifying the logic expressions that
map time points to new event relations. The code is released at
https://github.com/AndrewZhe/A-Unified-Framework-for-ETRE.

本文提出了一个统一的事件时间关系提取框架，并将时间关系转化成时间点的逻辑表达式，通过预测某些时间点的关系完成事件时间关系提取。该方法不仅在 TB-Dense 和 MATRES 上取得了显著提升，也可以在低数据情况下实现稳定的改进。

超越分类：一种用于事件时间关系提取的统一框架

More than Classification: A Unified Framework for Event Temporal  Relation Extraction

Combining abstract, symbolic reasoning with continuous neural reasoning is a
grand challenge of representation learning. As a step in this direction, we
propose a new architecture, called neural equivalence networks, for the problem
of learning continuous semantic representations of algebraic and logical
expressions. These networks are trained to represent semantic equivalence, even
of expressions that are syntactically very different. The challenge is that
semantic representations must be computed in a syntax-directed manner, because
semantics is compositional, but at the same time, small changes in syntax can
lead to very large changes in semantics, which can be difficult for continuous
neural architectures. We perform an exhaustive evaluation on the task of
checking equivalence on a highly diverse class of symbolic algebraic and
boolean expression types, showing that our model significantly outperforms
existing architectures.

本文介绍了神经等价网络的新架构，用于学习代数和逻辑表达式的连续语义表示，实现了语义等价的可靠推导，对符号代数和布尔表达式进行了全面评估