We propose a new descriptor for local atomic environments, to be used in
combination with machine learning models for the construction of interatomic
potentials. The Local Atomic Tensors Trainable Expansion (LATTE) allows for the
efficient construction of a variable number of many-body terms with learnable
parameters, resulting in a descriptor that is efficient, expressive, and can be
scaled to suit different accuracy and computational cost requirements. We
compare this new descriptor to existing ones on several systems, showing it to
be competitive with very fast potentials at one end of the spectrum, and
extensible to an accuracy close to the state of the art.

我们提出了一种用于构建原子间势能的机器学习模型的新型描述符，即可与局部原子环境相结合的局部原子张量可训练扩展（LATTE），能够高效地构建可学习参数的可变数量的多体项，从而实现了一种高效、表达力强的描述符，可以根据不同的精确度和计算成本要求进行扩展，并将这种新型描述符与现有的描述符在多个系统上进行比较，结果表明它在速度非常快的势能端和接近现有最先进精确度的情况下，具备竞争力。

LATTE：基于笛卡尔张量缩并的原子环境描述器

LATTE: an atomic environment descriptor based on Cartesian tensor  contractions

Entity linking is the task of linking mentions of named entities in natural
language text, to entities in a curated knowledge-base. This is of significant
importance in the biomedical domain, where it could be used to semantically
annotate a large volume of clinical records and biomedical literature, to
standardized concepts described in an ontology such as Unified Medical Language
System (UMLS). We observe that with precise type information, entity
disambiguation becomes a straightforward task. However, fine-grained type
information is usually not available in biomedical domain. Thus, we propose
LATTE, a LATent Type Entity Linking model, that improves entity linking by
modeling the latent fine-grained type information about mentions and entities.
Unlike previous methods that perform entity linking directly between the
mentions and the entities, LATTE jointly does entity disambiguation, and latent
fine-grained type learning, without direct supervision. We evaluate our model
on two biomedical datasets: MedMentions, a large scale public dataset annotated
with UMLS concepts, and a de-identified corpus of dictated doctor's notes that
has been annotated with ICD concepts. Extensive experimental evaluation shows
our model achieves significant performance improvements over several
state-of-the-art techniques.

本文介绍了一种用于解决生物医学领域实体链接问题的模型 LATTE，该模型通过建模提及和实体的潜在类型信息来提高实体链接的性能，并且在 MedMentions 数据集和医生笔记语料库上取得了显著的表现提升。