Our work presents two fundamental contributions. On the application side, we
tackle the challenging problem of predicting day-ahead crypto-currency prices.
On the methodological side, a new dynamical modeling approach is proposed. Our
approach keeps the probabilistic formulation of the state-space model, which
provides uncertainty quantification on the estimates, and the function
approximation ability of deep neural networks. We call the proposed approach
the deep state-space model. The experiments are carried out on established
cryptocurrencies (obtained from Yahoo Finance). The goal of the work has been
to predict the price for the next day. Benchmarking has been done with both
state-of-the-art and classical dynamical modeling techniques. Results show that
the proposed approach yields the best overall results in terms of accuracy.

我们的工作提出了两个基本贡献：在应用方面，我们解决了预测次日加密货币价格的难题；在方法上，我们提出了一种新的动态建模方法，深度状态空间模型，它保持了状态空间模型的概率形式，提供了估计的不确定性量化，并具备深度神经网络的函数逼近能力。实验使用了从 Yahoo Finance 获取的已建立的加密货币进行，旨在预测下一天的价格。与现有技术和传统动态建模技术进行了基准测试，结果显示所提出的方法在准确性方面取得了最佳综合结果。

深度状态空间模型预测加密货币价格

Deep State-Space Model for Predicting Cryptocurrency Price

An electrocardiogram (ECG or EKG) is a medical test that measures the heart's
electrical activity. ECGs are often used to diagnose and monitor a wide range
of heart conditions, including arrhythmias, heart attacks, and heart failure.
On the one hand, the conventional ECG requires clinical measurement, which
restricts its deployment to medical facilities. On the other hand, single-lead
ECG has become popular on wearable devices using administered procedures. An
alternative to ECG is Photoplethysmography (PPG), which uses non-invasive,
low-cost optical methods to measure cardiac physiology, making it a suitable
option for capturing vital heart signs in daily life. As a result, it has
become increasingly popular in health monitoring and is used in various
clinical and commercial wearable devices. While ECG and PPG correlate strongly,
the latter does not offer significant clinical diagnostic value. Here, we
propose a subject-independent attention-based deep state-space model to
translate PPG signals to corresponding ECG waveforms. The model is highly
data-efficient by incorporating prior knowledge in terms of probabilistic
graphical models. Notably, the model enables the detection of atrial
fibrillation (AFib), the most common heart rhythm disorder in adults, by
complementing ECG's accuracy with continuous PPG monitoring. We evaluated the
model on 55 subjects from the MIMIC III database. Quantitative and qualitative
experimental results demonstrate the effectiveness and efficiency of our
approach.

我们提出了一种基于注意力机制的深度状态空间模型，将光电脉搏图信号转化为相应的心电图波形，从而通过连续的光电脉搏图监测来补充心电图的准确性以检测成人中最常见的心律失常，房颤。在 MIMIC III 数据库的 55 个受试者上对该模型进行了评估，定量和定性的实验结果证明了我们方法的有效性和效率。

基于注意力机制的深度状态空间建模的连续房颤检测中 PPG 到 ECG 信号转换

PPG to ECG Signal Translation for Continuous Atrial Fibrillation  Detection via Attention-based Deep State-Space Modeling

We solve the problem of 6-DoF localisation and 3D dense reconstruction in
spatial environments as approximate Bayesian inference in a deep state-space
model. Our approach leverages both learning and domain knowledge from
multiple-view geometry and rigid-body dynamics. This results in an expressive
predictive model of the world, often missing in current state-of-the-art visual
SLAM solutions. The combination of variational inference, neural networks and a
differentiable raycaster ensures that our model is amenable to end-to-end
gradient-based optimisation. We evaluate our approach on realistic unmanned
aerial vehicle flight data, nearing the performance of state-of-the-art
visual-inertial odometry systems. We demonstrate the applicability of the model
to generative prediction and planning.

使用深度状态空间模型中的近似贝叶斯推理方法，结合多视图几何和刚体动力学的领域知识，进行六自由度本地化和三维密集重建。我们的方法在真实的无人机飞行数据上进行评估，接近最先进的视觉惯性测距系统的表现，并展示了模型在生成预测和规划方面的应用性。