Transformer-based language models (TLMs) have widely been recognized to be a
cutting-edge technology for the successful development of deep-learning-based
solutions to problems and applications that require natural language processing
and understanding. Like for other textual domains, TLMs have indeed pushed the
state-of-the-art of AI approaches for many tasks of interest in the legal
domain. Despite the first Transformer model being proposed about six years ago,
there has been a rapid progress of this technology at an unprecedented rate,
whereby BERT and related models represent a major reference, also in the legal
domain. This article provides the first systematic overview of TLM-based
methods for AI-driven problems and tasks in the legal sphere. A major goal is
to highlight research advances in this field so as to understand, on the one
hand, how the Transformers have contributed to the success of AI in supporting
legal processes, and on the other hand, what are the current limitations and
opportunities for further research development.

这篇文章给出了 TLM 在法律领域中基于 AI 的问题和任务的系统概述，旨在突出此领域的研究进展，从而理解 Transformer 模型在支持法律流程中的作用、当前的限制和进一步的研究发展机会。

为人工智能和法律领域引入基于 Transformer 的语言模型的秩序

Bringing order into the realm of Transformer-based language models for  artificial intelligence and law

Unsupervised deformable image registration is one of the challenging tasks in
medical imaging. Obtaining a high-quality deformation field while preserving
deformation topology remains demanding amid a series of deep-learning-based
solutions. Meanwhile, the diffusion model's latent feature space shows
potential in modeling the deformation semantics. To fully exploit the diffusion
model's ability to guide the registration task, we present two modules:
Feature-wise Diffusion-Guided Module (FDG) and Score-wise Diffusion-Guided
Module (SDG). Specifically, FDG uses the diffusion model's multi-scale semantic
features to guide the generation of the deformation field. SDG uses the
diffusion score to guide the optimization process for preserving deformation
topology with barely any additional computation. Experiment results on the 3D
medical cardiac image registration task validate our model's ability to provide
refined deformation fields with preserved topology effectively. Code is
available at: this https URL

通过使用扩散模型的多尺度语义特征来引导变形场的生成，以及使用扩散得分来引导优化过程，本研究证实了模型在 3D 医学心脏图像注册任务中有效提供保留拓扑的改进型变形场的能力。