Monitoring and recognizing patterns in continuous sensing data is crucial for
many practical applications. These real-world time-series data are often
nonstationary, characterized by varying statistical and spectral properties
over time. This poses a significant challenge in developing learning models
that can effectively generalize across different distributions. In this work,
based on our observation that nonstationary statistics are intrinsically linked
to the phase information, we propose a time-series learning framework, PhASER.
It consists of three novel elements: 1) phase augmentation that diversifies
non-stationarity while preserving discriminatory semantics, 2) separate feature
encoding by viewing time-varying magnitude and phase as independent modalities,
and 3) feature broadcasting by incorporating phase with a novel residual
connection for inherent regularization to enhance distribution invariant
learning. Upon extensive evaluation on 5 datasets from human activity
recognition, sleep-stage classification, and gesture recognition against 10
state-of-the-art baseline methods, we demonstrate that PhASER consistently
outperforms the best baselines by an average of 5% and up to 13% in some cases.
Moreover, PhASER's principles can be applied broadly to boost the
generalization ability of existing time series classification models.

基于我们观察到的非稳态统计与相位信息的内在联系，我们提出了一种时间序列学习框架 PhASER，包括相位增强、分离特征编码、特征广播等元素，以增强学习模型对不同分布的泛化能力，通过在人类活动识别、睡眠阶段分类和手势识别等 5 个数据集上的广泛评估，我们证明 PhASER 相对于现有基线方法平均提高了 5% 的性能，在某些情况下提高了 13%，此外，PhASER 的原则可以广泛应用于提升现有时间序列分类模型的泛化能力。

非平稳时间序列的相位驱动领域通用学习

Phase-driven Domain Generalizable Learning for Nonstationary Time Series

Purpose: In this work, we present a workflow to construct generic and robust
generative image priors from magnitude-only images. The priors can then be used
for regularization in reconstruction to improve image quality. Methods: The
workflow begins with the preparation of training datasets from magnitude-only
MR images. This dataset is then augmented with phase information and used to
train generative priors of complex images. Finally, trained priors are
evaluated using both linear and nonlinear reconstruction for compressed sensing
parallel imaging with various undersampling schemes. Results: The results of
our experiments demonstrate that priors trained on complex images outperform
priors trained only on magnitude images. Additionally, a prior trained on a
larger dataset exhibits higher robustness. Finally, we show that the generative
priors are superior to L1 -wavelet regularization for compressed sensing
parallel imaging with high undersampling. Conclusion: These findings stress the
importance of incorporating phase information and leveraging large datasets to
raise the performance and reliability of the generative priors for MRI
reconstruction. Phase augmentation makes it possible to use existing image
databases for training.

本研究提出了一种从仅包含振幅信息的图像中构建通用且稳健的生成图像先验的工作流程，并将其用于改善图像质量的重建中进行正则化。结果表明，使用复杂图像训练的先验表现优于仅使用振幅图像训练的先验，且基于更大数据集训练的先验具有更高的鲁棒性。此外，相较于 L1 小波正则化，生成图像先验在高欠采样条件下的压缩感知并行成像中表现卓越。因此，本研究强调了整合相位信息和利用大型数据集提升 MRI 重建生成图像先验的性能和可靠性的重要性。相位增强使得可以利用现有图像数据库进行训练。