PULSAR (personalized, ultra-fractionated stereotactic adaptive radiotherapy)
is the adaptation of stereotactic ablative radiotherapy towards personalized
cancer management. For the first time, we applied a transformer-based attention
mechanism to investigate the underlying interactions between combined PULSAR
and PD-L1 blockade immunotherapy based on a murine cancer model (Lewis Lung
Carcinoma, LLC). The proposed approach is able to predict the trend of tumor
volume change semi-quantitatively, and excels in identifying the potential
causal relationships through both self-attention and cross-attention scores.

PULSAR 是一种个性化的、超分式立体定向自适应放射疗法，该研究应用基于 Transformer 的注意力机制，通过小鼠癌症模型（LLC）探究了 PULSAR 与 PD-L1 阻断免疫治疗相结合的基本相互作用，该方法能够对肿瘤体积变化趋势进行半定量预测，并且通过自注注意和交叉注意力评分有效地识别潜在的因果关系。

利用 Transformer 模型及注意力机制理解放疗与免疫治疗中的 PULSAR 效应

Understanding the PULSAR Effect in Combined Radiotherapy and  Immunotherapy through Attention Mechanisms with a Transformer Model

This paper presents a novel Sequence-to-Sequence (Seq2Seq) model based on a
transformer-based attention mechanism and temporal pooling for Non-Intrusive
Load Monitoring (NILM) of smart buildings. The paper aims to improve the
accuracy of NILM by using a deep learning-based method. The proposed method
uses a Seq2Seq model with a transformer-based attention mechanism to capture
the long-term dependencies of NILM data. Additionally, temporal pooling is used
to improve the model's accuracy by capturing both the steady-state and
transient behavior of appliances. The paper evaluates the proposed method on a
publicly available dataset and compares the results with other state-of-the-art
NILM techniques. The results demonstrate that the proposed method outperforms
the existing methods in terms of both accuracy and computational efficiency.

本文介绍了一种基于转换器注意机制和时间池化的新颖序列到序列模型，用于智能建筑的非侵入式负载监测 (NILM)，旨在通过使用深度学习方法提高 NILM 的准确性。该方法使用序列到序列 (Seq2Seq) 模型，通过转换器注意机制捕获 NILM 数据的长期依赖性，并使用时间池化来提高模型的准确性，捕获电器的稳态和瞬态行为。该论文在公开数据集上评估了所提出的方法，并将结果与其他最新的 NILM 技术进行了比较，结果表明，该方法在准确性和计算效率方面优于现有的方法。