In materials science, the challenge of rapid prototyping materials with
desired properties often involves extensive experimentation to find suitable
microstructures. Additionally, finding microstructures for given properties is
typically an ill-posed problem where multiple solutions may exist. Using
generative machine learning models can be a viable solution which also reduces
the computational cost. This comes with new challenges because, e.g., a
continuous property variable as conditioning input to the model is required. We
investigate the shortcomings of an existing method and compare this to a novel
embedding strategy for generative models that is based on the binary
representation of floating point numbers. This eliminates the need for
normalization, preserves information, and creates a versatile embedding space
for conditioning the generative model. This technique can be applied to
condition a network on any number, to provide fine control over generated
microstructure images, thereby contributing to accelerated materials design.

利用二进制表示的浮点数进行嵌入的新型策略，消除了归一化过程，保留信息，并为生成模型提供了多功能的嵌入空间，用于快速材料设计。

使用新颖连续变量嵌入的加速逆向建模的生成模型

A Generative Model for Accelerated Inverse Modelling Using a Novel  Embedding for Continuous Variables

Recent success of pre-trained language models (PLMs) has stimulated interest
in their ability to understand and work with numbers. Yet, the numerical
reasoning over measurements has not been formally studied despite their
importance. In this study, we show that PLMs lack the capability required for
reasoning over measurements. Furthermore, we find that a language model trained
on a measurement-rich corpus shows better performance on understanding
measurements. We propose a simple embedding strategy to better distinguish
between numbers and units, which leads to a significant improvement in the
probing tasks.

本文研究了预训练语言模型在理解数字和测量值上的能力，并提出了一种嵌入策略来更好地区分数字和单位，以提高模型性能。

语言模型是否理解计量单位？

Do Language Models Understand Measurements?

Deep metric learning seeks to define an embedding where semantically similar
images are embedded to nearby locations, and semantically dissimilar images are
embedded to distant locations. Substantial work has focused on loss functions
and strategies to learn these embeddings by pushing images from the same class
as close together in the embedding space as possible. In this paper, we propose
an alternative, loosened embedding strategy that requires the embedding
function only map each training image to the most similar examples from the
same class, an approach we call "Easy Positive" mining. We provide a collection
of experiments and visualizations that highlight that this Easy Positive mining
leads to embeddings that are more flexible and generalize better to new unseen
data. This simple mining strategy yields recall performance that exceeds state
of the art approaches (including those with complicated loss functions and
ensemble methods) on image retrieval datasets including CUB, Stanford Online
Products, In-Shop Clothes and Hotels-50K.

该文章提出一种称之为 “Easy Positive” mining 的松散的嵌入策略，并利用实验和可视化方法证明了这种方法可以获得更灵活、泛化性更好的嵌入，可能为图像检索领域带来新的进展。