This paper presents a comprehensive analysis of both conventional and deep
learning methods for eliminating electromagnetic interference (EMI) in MRI
systems. We explore the underlying principles and implementation of traditional
analytical and adaptive EMI elimination techniques, as well as cutting-edge
deep learning approaches. Through a detailed comparison, the strengths and
limitations of each method are highlighted. Recent advancements in active EMI
elimination utilizing multiple external EMI receiver coils and analytical
techniques are discussed alongside the superior performance of deep learning
methods, which leverage neural networks trained on extensive MRI data. While
deep learning methods demonstrate significant improvements in EMI suppression,
enhancing diagnostic capabilities and accessibility of MRI technology, they
also introduce potential security and safety concerns, especially in production
and commercial applications. This study underscores the need to address these
challenges to fully realize the benefits of deep learning in EMI elimination.
The findings suggest a balanced approach, combining the reliability of
conventional methods with the advanced capabilities of deep learning, to
develop more robust and effective EMI suppression strategies in MRI systems.

综述了传统方法和深度学习方法在消除磁共振成像系统中的电磁干扰方面的综合分析和实施，并通过详细比较强调了每种方法的优势和局限性。研究结果表明，结合传统方法的可靠性和深度学习的先进能力，开发更健壮、有效的电磁干扰抑制策略在 MRI 系统中是有必要的。

低场便携式 MRI 扫描仪电磁消除方法综述

A Review of Electromagnetic Elimination Methods for low-field portable  MRI scanner

Scene text removal (STR) is the image transformation task to remove text
regions in scene images. The conventional STR methods remove all scene text.
This means that the existing methods cannot select text to be removed. In this
paper, we propose a novel task setting named selective scene text removal
(SSTR) that removes only target words specified by the user. Although SSTR is a
more complex task than STR, the proposed multi-module structure enables
efficient training for SSTR. Experimental results show that the proposed method
can remove target words as expected.

该论文提出了一种名为选择性场景文本去除（SSTR）的新任务设置，该任务允许用户只去除指定的目标词，通过提出的多模块结构，能够高效训练 SSTR，并展示实验结果证明该方法能如预期地去除目标词。

选择性场景文本去除

Selective Scene Text Removal

Multilingual transfer techniques often improve low-resource machine
translation (MT). Many of these techniques are applied without considering data
characteristics. We show in the context of Haitian-to-English translation that
transfer effectiveness is correlated with amount of training data and
relationships between knowledge-sharing languages. Our experiments suggest that
for some languages beyond a threshold of authentic data, back-translation
augmentation methods are counterproductive, while cross-lingual transfer from a
sufficiently related language is preferred. We complement this finding by
contributing a rule-based French-Haitian orthographic and syntactic engine and
a novel method for phonological embedding. When used with multilingual
techniques, orthographic transformation makes statistically significant
improvements over conventional methods. And in very low-resource Jamaican MT,
code-switching with a transfer language for orthographic resemblance yields a
6.63 BLEU point advantage.

研究表明，对于低资源机器翻译，多语言转移技术的有效性与训练数据量以及知识共享语言之间的关系有关，当某些语言超过真实数据的阈值时，回译增强方法会适得其反，而跨语言转移则更合适。同时，该研究提出了一种基于规则的法语 - 海地克里奥尔语正字和句法引擎和一种新型的音韵嵌入方法，这些方法使得正字转换在多语言技术中相对于传统方法显著提高效果。在极低资源的牙买加机器翻译中，通过与一种具有正字相似度的转移语言进行代码交换，可以获得 6.63 BLEU 点的优势。