Sequential Variational Autoencoder with Adversarial Classifier for Video Disentanglement.

In this paper, we propose a sequential variational autoencoder for video disentanglement, which is a representation learning method that can be used to separately extract static and dynamic features from videos. Building sequential variational autoencoders with a two-stream architecture induces inductive bias for video disentanglement. However, our preliminary experiment demonstrated that the two-stream architecture is insufficient for video disentanglement because static features frequently contain dynamic features.

Spatiotemporal forecasting of vertical track alignment with exogenous factors.

To ensure the safety of railroad operations, it is important to monitor and forecast track geometry irregularities. A higher safety requires forecasting with higher spatiotemporal frequencies, which in turn requires capturing spatial correlations. Additionally, track geometry irregularities are influenced by multiple exogenous factors.

Framework for identifying and visualising emotional atmosphere in online learning environments in the COVID-19 Era.

In addition to the almost five million lives lost and millions more than that in hospitalisations, efforts to mitigate the spread of the COVID-19 pandemic, which that has disrupted every aspect of human life deserves the contributions of all and sundry. Education is one of the areas most affected by the COVID-imposed abhorrence to physical (i.e.

Zero-Shot Action Recognition with Three-Stream Graph Convolutional Networks.

Large datasets are often used to improve the accuracy of action recognition. However, very large datasets are problematic as, for example, the annotation of large datasets is labor-intensive. This has encouraged research in zero-shot action recognition (ZSAR).

2D Convolutional Neural Markov Models for Spatiotemporal Sequence Forecasting.

Recent approaches to time series forecasting, especially forecasting spatiotemporal sequences, have leveraged the approximation power of deep neural networks to model the complexity of such sequences, specifically approaches that are based on recurrent neural networks. Still, as spatiotemporal sequences that arise in the real world are noisy and chaotic, modeling approaches that utilize probabilistic temporal models, such as deep Markov models (DMMs), are favorable because of their ability to model uncertainty, increasing their robustness to noise. However, approaches based on DMMs do not maintain the spatial characteristics of spatiotemporal sequences, with most of the approaches converting the observed input into 1D data halfway through the model.

[Anesthetic management for a patient with platypnea-orthodeoxia syndrome].

We experienced anesthetic management for a patient with platypnea-orthodeoxia syndrome. This syndrome is relatively uncommon and accompanies dyspnea and hypoxemia on changing to a sitting or standing from recumbent position. A 75-year-old man with the syndrome underwent atrial septal defect closure on cardiopulmonary bypass.