Pile-up signals are frequently produced in experimental physics. They create
inaccurate physics data with high uncertainty and cause various problems.
Therefore, the correction to pile-up signals is crucially required. In this
study, we implemented a deep learning method to restore the original signals
from the pile-up signals. We showed that a deep learning model could accurately
reconstruct the original signal waveforms from the pile-up waveforms. By
substituting the pile-up signals with the original signals predicted by the
model, the energy and timing resolutions of the data are notably enhanced. The
model implementation significantly improved the quality of the particle
identification plot and particle tracks. This method is applicable to similar
problems, such as separating multiple signals or correcting pile-up signals
with other types of noises and backgrounds.

本研究利用深度学习方法对堆积信号进行校正，成功地提高了数据的能量和时间分辨率，并显著改善了粒子鉴别图和粒子轨迹。该方法可用于类似的问题，如多信号分离或与其他类型的噪声和背景校正堆积信号。

使用深度学习从堆积信号中恢复原始信号

Restoring Original Signal From Pile-up Signal using Deep Learning

Biased associations have been a challenge in the development of classifiers
for detecting toxic language, hindering both fairness and accuracy. As
potential solutions, we investigate recently introduced debiasing methods for
text classification datasets and models, as applied to toxic language
detection. Our focus is on lexical (e.g., swear words, slurs, identity
mentions) and dialectal markers (specifically African American English). Our
comprehensive experiments establish that existing methods are limited in their
ability to prevent biased behavior in current toxicity detectors. We then
propose an automatic, dialect-aware data correction method, as a
proof-of-concept. Despite the use of synthetic labels, this method reduces
dialectal associations with toxicity. Overall, our findings show that debiasing
a model trained on biased toxic language data is not as effective as simply
relabeling the data to remove existing biases.

由于文本分类器开发中的偏见关联限制了公平性和准确性，因此我们调查了最近介绍的去偏置方法，作用于检测有毒语言的文本分类数据集和模型，重点关注词汇（例如骂人话、侮辱性言论、身份称谓）和方言标记（特别是非裔美国英语）。我们的全面实验表明，现有的方法在防止当前毒性检测器中出现有偏见的行为方面存在局限性。然后，我们提出了一种自动的方言感知数据校正方法作为概念验证。尽管采用了合成标签，但该方法减少了方言与毒性之间的关联。总的来说，我们的发现表明，在训练有毒性偏见性数据的模型时去偏置并不如简单重标记数据以消除现有偏见有效。