Accurately predicting heart activity and other biological signals is crucial
for diagnosis and monitoring. Given that speech is an outcome of multiple
physiological systems, a significant body of work studied the acoustic
correlates of heart activity. Recently, self-supervised models have excelled in
speech-related tasks compared to traditional acoustic methods. However, the
robustness of data-driven representations in predicting heart activity remained
unexplored. In this study, we demonstrate that self-supervised speech models
outperform acoustic features in predicting heart activity parameters. We also
emphasize the impact of individual variability on model generalizability. These
findings underscore the value of data-driven representations in such tasks and
the need for more speech-based physiological data to mitigate speaker-related
challenges.

通过研究语音与心脏活动的声学相关性，本研究证明了自监督语音模型在预测心脏活动参数方面胜过传统声学特征，并强调了个体变异对模型的普适性的影响，这些发现突显了数据驱动模型在这类任务中的价值，并需要更多基于语音的生理数据来解决与说话者相关的挑战。

利用数据驱动和基于知识的特征预测心脏活动的语音

Predicting Heart Activity from Speech using Data-driven and  Knowledge-based features

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 recent proliferation of fake portrait videos poses direct threats on
society, law, and privacy. Believing the fake video of a politician,
distributing fake pornographic content of celebrities, fabricating impersonated
fake videos as evidence in courts are just a few real world consequences of
deep fakes. We present a novel approach to detect synthetic content in portrait
videos, as a preventive solution for the emerging threat of deep fakes. In
other words, we introduce a deep fake detector. We observe that detectors
blindly utilizing deep learning are not effective in catching fake content, as
generative models produce formidably realistic results. Our key assertion
follows that biological signals hidden in portrait videos can be used as an
implicit descriptor of authenticity, because they are neither spatially nor
temporally preserved in fake content. To prove and exploit this assertion, we
first engage several signal transformations for the pairwise separation
problem, achieving 99.39% accuracy. Second, we utilize those findings to
formulate a generalized classifier for fake content, by analyzing proposed
signal transformations and corresponding feature sets. Third, we generate novel
signal maps and employ a CNN to improve our traditional classifier for
detecting synthetic content. Lastly, we release an "in the wild" dataset of
fake portrait videos that we collected as a part of our evaluation process. We
evaluate FakeCatcher on several datasets, resulting with 96%, 94.65%, 91.50%,
and 91.07% accuracies, on Face Forensics, Face Forensics++, CelebDF, and on our
new Deep Fakes Dataset respectively. We also analyze signals from various
facial regions, under image distortions, with varying segment durations, from
different generators, against unseen datasets, and under several dimensionality
reduction techniques.

本文提出了一种新颖的方法来检测肖像视频的合成内容，通过使用隐藏在肖像视频中的生物信号作为真实性的隐含描述符，并通过信号转化和卷积神经网络，最终实现了检测 deep fake 的效果达到了高准确率。