The objective of Information Extraction (IE) is to derive structured
representations from unstructured or semi-structured documents. However,
developing IE models is complex due to the need of integrating several
subtasks. Additionally, representation of data among varied tasks and
transforming datasets into task-specific model inputs presents further
challenges. To streamline this undertaking for researchers, we introduce
PyTorch-IE, a deep-learning-based framework uniquely designed to enable swift,
reproducible, and reusable implementations of IE models. PyTorch-IE offers a
flexible data model capable of creating complex data structures by integrating
interdependent layers of annotations derived from various data types, like
plain text or semi-structured text, and even images. We propose task modules to
decouple the concerns of data representation and model-specific
representations, thereby fostering greater flexibility and reusability of code.
PyTorch-IE also extends support for widely used libraries such as
PyTorch-Lightning for training, HuggingFace datasets for dataset reading, and
Hydra for experiment configuration. Supplementary libraries and GitHub
templates for the easy setup of new projects are also provided. By ensuring
functionality and versatility, PyTorch-IE provides vital support to the
research community engaged in Information Extraction.

信息抽取（IE）旨在从非结构化或半结构化文档中导出结构化表示。本研究提出了 PyTorch-IE，一种基于深度学习的框架，旨在实现 IE 模型的快速、可重复和可重用的实现。PyTorch-IE 提供了灵活的数据模型，能够通过集成来自各种数据类型（如纯文本或半结构化文本以及图像）的相互依赖的注释层来创建复杂的数据结构。通过提供对 PyTorch-Lightning 用于训练、HuggingFace 数据集用于数据集读取和 Hydra 用于实验配置等广泛使用的库的支持，PyTorch-IE 为信息抽取领域的研究社区提供了重要的支持。

PyTorch-IE：信息提取的快速可重现原型设计

PyTorch-IE: Fast and Reproducible Prototyping for Information Extraction

This work aims efficiently estimating the posterior distribution of kinetic
parameters for dynamic positron emission tomography (PET) imaging given a
measurement of time of activity curve. Considering the inherent information
loss from parametric imaging to measurement space with the forward kinetic
model, the inverse mapping is ambiguous. The conventional (but expensive)
solution can be the Markov Chain Monte Carlo (MCMC) sampling, which is known to
produce unbiased asymptotical estimation. We propose a deep-learning-based
framework for efficient posterior estimation. Specifically, we counteract the
information loss in the forward process by introducing latent variables. Then,
we use a conditional variational autoencoder (CVAE) and optimize its evidence
lower bound. The well-trained decoder is able to infer the posterior with a
given measurement and the sampled latent variables following a simple
multivariate Gaussian distribution. We validate our CVAE-based method using
unbiased MCMC as the reference for low-dimensional data (a single brain region)
with the simplified reference tissue model.

使用基于深度学习的条件变分自编码器 (CVAE) 的方法，给定活性曲线的测量结果，有效估计动态正电子发射断层成像 (PET) 的运动动力学参数的后验分布。验证结果使用无偏 MCMC 作为参考，处理低维数据 (单个脑区) 和简化的参考组织模型。

基于条件变分推断的动态 PET 图像的后验估计

Posterior Estimation for Dynamic PET imaging using Conditional  Variational Inference

Most existing large-scale academic search engines are built to retrieve
text-based information. However, there are no large-scale retrieval services
for scientific figures and tables. One challenge for such services is
understanding scientific figures' semantics, such as their types and purposes.
A key obstacle is the need for datasets containing annotated scientific figures
and tables, which can then be used for classification, question-answering, and
auto-captioning. Here, we develop a pipeline that extracts figures and tables
from the scientific literature and a deep-learning-based framework that
classifies scientific figures using visual features. Using this pipeline, we
built the first large-scale automatically annotated corpus, ACL-Fig, consisting
of 112,052 scientific figures extracted from ~56K research papers in the ACL
Anthology. The ACL-Fig-Pilot dataset contains 1,671 manually labeled scientific
figures belonging to 19 categories. The dataset is accessible at
this https URL under a CC BY-NC license.

该研究提出了利用深度学习框架分类科学图表的流程，通过构建一个包含注释的大规模科学图表库，开创了科学图表自动注释的先河。