Efforts to leverage deep learning models in low-resource regimes have led to
numerous augmentation studies. However, the direct application of methods such
as mixup and cutout to text data, is limited due to their discrete
characteristics. While methods using pretrained language models have exhibited
efficiency, they require additional considerations for robustness. Inspired by
recent studies on decision boundaries, this paper proposes a
decision-boundary-aware data augmentation strategy to enhance robustness using
pretrained language models. The proposed technique first focuses on shifting
the latent features closer to the decision boundary, followed by reconstruction
to generate an ambiguous version with a soft label. Additionally, mid-K
sampling is suggested to enhance the diversity of the generated sentences. This
paper demonstrates the performance of the proposed augmentation strategy
compared to other methods through extensive experiments. Furthermore, the
ablation study reveals the effect of soft labels and mid-K sampling and the
extensibility of the method with curriculum data augmentation.

该研究提出了一种基于决策边界的数据增强策略，利用预训练语言模型来提高鲁棒性；该技术首先将潜在特征接近决策边界，然后通过重构生成一个带有软标签的模糊版本，并建议使用中点 K 采样来增强生成句子的多样性。通过大量实验证明了该增强策略与其他方法相比的性能，同时深入研究了软标签和中点 K 采样的效果以及该方法在课程数据增强中的可扩展性。

通过决策边界感知的数据增强在低资源环境中提高效果和鲁棒性

Enhancing Effectiveness and Robustness in a Low-Resource Regime via  Decision-Boundary-aware Data Augmentation

Ubiquitous sensing from wearable devices in the wild holds promise for
enhancing human well-being, from diagnosing clinical conditions and measuring
stress to building adaptive health promoting scaffolds. But the large volumes
of data therein across heterogeneous contexts pose challenges for conventional
supervised learning approaches. Representation Learning from biological signals
is an emerging realm catalyzed by the recent advances in computational modeling
and the abundance of publicly shared databases. The electrocardiogram (ECG) is
the primary researched modality in this context, with applications in health
monitoring, stress and affect estimation. Yet, most studies are limited by
small-scale controlled data collection and over-parameterized architecture
choices. We introduce \textbf{WildECG}, a pre-trained state-space model for
representation learning from ECG signals. We train this model in a
self-supervised manner with 275,000 10s ECG recordings collected in the wild
and evaluate it on a range of downstream tasks. The proposed model is a robust
backbone for ECG analysis, providing competitive performance on most of the
tasks considered, while demonstrating efficacy in low-resource regimes. The
code and pre-trained weights are shared publicly at
this https URL

利用佩戴设备进行普遍感知在提升人类福祉方面具有潜力，从诊断临床状况和测量压力到构建适应性的促进健康的框架。然而，异质背景下的海量数据对传统的监督学习方法提出了挑战。生物信号的表示学习是一个新兴的领域，受到计算建模的最新进展和公开共享数据库的丰富影响。心电图（ECG）是这一领域的主要研究模态，具有健康监测、压力和情感估计等应用。我们引入了一种名为 WildECG 的预训练状态空间模型，用于从 ECG 信号进行表示学习。我们以自我监督的方式训练了这个模型，使用了从野外收集的 275,000 个 10 秒的 ECG 记录，并对各种下游任务进行评估。该模型对于 ECG 分析是一个稳健的基础架构，在大多数考虑到的任务中表现出竞争力，并且在资源有限的情况下显示出有效性。代码和预训练权重已公开共享在指定的 URL 网址。

使用状态空间模型从普遍的心电图中扩展表示学习

Scaling Representation Learning from Ubiquitous ECG with State-Space  Models

The truth is significantly hampered by massive rumors that spread along with
breaking news or popular topics. Since there is sufficient corpus gathered from
the same domain for model training, existing rumor detection algorithms show
promising performance on yesterday's news. However, due to a lack of training
data and prior expert knowledge, they are poor at spotting rumors concerning
unforeseen events, especially those propagated in different languages (i.e.,
low-resource regimes). In this paper, we propose a unified contrastive transfer
framework to detect rumors by adapting the features learned from well-resourced
rumor data to that of the low-resourced. More specifically, we first represent
rumor circulated on social media as an undirected topology, and then train a
Multi-scale Graph Convolutional Network via a unified contrastive paradigm. Our
model explicitly breaks the barriers of the domain and/or language issues, via
language alignment and a novel domain-adaptive contrastive learning mechanism.
To enhance the representation learning from a small set of target events, we
reveal that rumor-indicative signal is closely correlated with the uniformity
of the distribution of these events. We design a target-wise contrastive
training mechanism with three data augmentation strategies, capable of unifying
the representations by distinguishing target events. Extensive experiments
conducted on four low-resource datasets collected from real-world microblog
platforms demonstrate that our framework achieves much better performance than
state-of-the-art methods and exhibits a superior capacity for detecting rumors
at early stages.

本文提出了一种基于统一对比转移框架的谣言检测方法，通过语言对齐和领域自适应对比学习机制，训练多尺度图卷积网络，实现在不同语言资源匮乏的情况下检测谣言，实验证明这种方法比其他方法更具优越性。