Scientific datasets present unique challenges for machine learning-driven
compression methods, including more stringent requirements on accuracy and
mitigation of potential invalidating artifacts. Drawing on results from
compressed sensing and rate-distortion theory, we introduce effective data
compression methods by developing autoencoders using high dimensional latent
spaces that are $L^1$-regularized to obtain sparse low dimensional
representations. We show how these information-rich latent spaces can be used
to mitigate blurring and other artifacts to obtain highly effective data
compression methods for scientific data. We demonstrate our methods for short
angle scattering (SAS) datasets showing they can achieve compression ratios
around two orders of magnitude and in some cases better. Our compression
methods show promise for use in addressing current bottlenecks in transmission,
storage, and analysis in high-performance distributed computing environments.
This is central to processing the large volume of SAS data being generated at
shared experimental facilities around the world to support scientific
investigations. Our approaches provide general ways for obtaining specialized
compression methods for targeted scientific datasets.

使用高维稀疏特征表达式通过 L1 正则化的自编码器产生的信息丰富潜在空间，可以有效地用于科学数据压缩，解决高性能分布式计算环境下传输、存储和分析的瓶颈问题。

科学数据压缩的稀疏 L1 自编码器

Sparse $L^1$-Autoencoders for Scientific Data Compression

Large language models are a form of artificial intelligence systems whose
primary knowledge consists of the statistical patterns, semantic relationships,
and syntactical structures of language1. Despite their limited forms of
"knowledge", these systems are adept at numerous complex tasks including
creative writing, storytelling, translation, question-answering, summarization,
and computer code generation. However, they have yet to demonstrate advanced
applications in natural science. Here we show how large language models can
perform scientific synthesis, inference, and explanation. We present a method
for using general-purpose large language models to make inferences from
scientific datasets of the form usually associated with special-purpose machine
learning algorithms. We show that the large language model can augment this
"knowledge" by synthesizing from the scientific literature. When a conventional
machine learning system is augmented with this synthesized and inferred
knowledge it can outperform the current state of the art across a range of
benchmark tasks for predicting molecular properties. This approach has the
further advantage that the large language model can explain the machine
learning system's predictions. We anticipate that our framework will open new
avenues for AI to accelerate the pace of scientific discovery.

使用大型语言模型进行科学综合、推理和解释，通过从科学文献综合知识，将其应用于预测分子属性等任务，提高了当前机器学习系统的性能，并能解释其预测结果，将加速科学发现的进程。

用于科学综合、推理和解释的大型语言模型

Large Language Models for Scientific Synthesis, Inference and  Explanation

Numeracy is the ability to understand and work with numbers. It is a
necessary skill for composing and understanding documents in clinical,
scientific, and other technical domains. In this paper, we explore different
strategies for modelling numerals with language models, such as memorisation
and digit-by-digit composition, and propose a novel neural architecture that
uses a continuous probability density function to model numerals from an open
vocabulary. Our evaluation on clinical and scientific datasets shows that using
hierarchical models to distinguish numerals from words improves a perplexity
metric on the subset of numerals by 2 and 4 orders of magnitude, respectively,
over non-hierarchical models. A combination of strategies can further improve
perplexity. Our continuous probability density function model reduces mean
absolute percentage errors by 18% and 54% in comparison to the second best
strategy for each dataset, respectively.

探索用语言模型建模数字的不同策略，提出使用连续概率密度函数来建模开放词汇中数字的新型神经架构，并在临床和科学数据集上进行评估表明使用分层模型可显著提高困惑度指标，连续概率密度函数模型相较于其他策略可分别将平均绝对百分比误差降低 18％和 54％。