Generative models can produce synthetic patient records for analytical tasks
when real data is unavailable or limited. However, current methods struggle
with adhering to domain-specific knowledge and removing invalid data. We
present ConSequence, an effective approach to integrating domain knowledge into
sequential generative neural network outputs. Our rule-based formulation
includes temporal aggregation and antecedent evaluation modules, ensured by an
efficient matrix multiplication formulation, to satisfy hard and soft logical
constraints across time steps. Existing constraint methods often fail to
guarantee constraint satisfaction, lack the ability to handle temporal
constraints, and hinder the learning and computational efficiency of the model.
In contrast, our approach efficiently handles all types of constraints with
guaranteed logical coherence. We demonstrate ConSequence's effectiveness in
generating electronic health records, outperforming competitors in achieving
complete temporal and spatial constraint satisfaction without compromising
runtime performance or generative quality. Specifically, ConSequence
successfully prevents all rule violations while improving the model quality in
reducing its test perplexity by 5% and incurring less than a 13% slowdown in
generation speed compared to an unconstrained model.

通过运用领域知识和约束条件，ConSequence 模型能够高效地生成电子病历，满足完整的时间和空间约束条件，同时不影响运行性能和生成质量。

ConSequence: 为电子健康档案生成合乎逻辑约束的序列进行综合

ConSequence: Synthesizing Logically Constrained Sequences for Electronic  Health Record Generation

ISCAS participated in two subtasks of SemEval 2020 Task 5: detecting
counterfactual statements and detecting antecedent and consequence. This paper
describes our system which is based on pre-trained transformers. For the first
subtask, we train several transformer-based classifiers for detecting
counterfactual statements. For the second subtask, we formulate antecedent and
consequence extraction as a query-based question answering problem. The two
subsystems both achieved third place in the evaluation. Our system is openly
released at this https URL

该论文介绍了 ISCAS 的系统在 SemEval 2020 任务 5 的两个子任务中的表现，基于预先训练的 transformers，第一个子任务使用基于 transformers 的分类器来检测反事实陈述，第二个子任务将先决条件和后果提取为一个基于查询的问答问题，两个子系统在评估中都获得了第三名。我们的系统是公开发布的。

ISCAS 在 SemEval-2020 任务 5 中：针对反事实语句建模的预训练 Transformer

ISCAS at SemEval-2020 Task 5: Pre-trained Transformers for  Counterfactual Statement Modeling

The Riemannian center of mass was constructed in [GrKa] (1973). In [GKR1,
GKR2, Gr, Ka, BuKa] (1974-1981) it was successfully applied with more refined
estimates. Probably in 1990 someone renamed it without justification into
karcher mean and references to the older papers were omitted by those using the
new name. As a consequence newcomers started to reprove results from the above
papers. - Here I explain the older history.

解释了 Riemannian center of mass 的发展历史及其之后被未经证明就改名为 Karcher mean 的影响。