Markovian Gaussian Process Variational Autoencoders

Deep generative models are widely used for modelling high-dimensional time series, such as video animations, audio and climate data. Sequential variational autoencoders have been successfully considered for many applications, with many variant models relying on discrete-time methods and recurrent neural networks (RNNs). On the other hand, continuous-time methods have recently gained attraction, especially in the context of irregularly-sampled time series, where they can better handle the data than discrete-time methods. One such class are Gaussian process variational autoencoders (GPVAEs), where the VAE prior is set as a Gaussian process (GPs), allowing inductive biases to be explicitly encoded via the kernel function and interpretability of the latent space. However, a major limitation of GPVAEs is that it inherits the same cubic computational cost as GPs. In this work, we leverage the equivalent discrete state space representation of Markovian GPs to enable a linear-time GP solver via Kalman filtering and smoothing. We show via corrupt and missing frames tasks that our method performs favourably, especially on the latter where it outperforms RNN-based models.Comment: Non-archival paper presented at Workshop on Continuous Time Methods for Machine Learning. The 39th International Conference on Machine Learning, Baltimor

Municipal Government Use of Social Media: An Analysis of Three Chinese Cities

To investigate the use of information dissemination and public communication by Chinese municipal governments, we analyzed the social media use of three large cities with relatively mature social media development: Shanghai, Nanjing and Chengdu. We collected 4,429 government posts and users’ likes, shares and comments from Weibo accounts of each city’s information office. Government posts were coded into 7 types and 16 topics. We used cross-tabulation, correlation analysis and multivariate linear regression to analyze government posts, user responses and their inter-relationships. Chengdu has issued the most posts, while Nanjing has received the highest average user response to posts and exhibited the best signs of success in communication between the government and citizens

Methods for Surveying Stable Fly Populations

Stable flies are among the most important pests of livestock throughout much of the world. Their painful bites induce costly behavioral and physiological stress responses and reduce productivity. Stable flies are anthropogenic and their population dynamics vary depending on agricultural and animal husbandry practices. Standardized sampling methods are needed to better identify the factors controlling stable fly populations, test novel control technologies, and determine optimal management strategies. The current study reviewed methods used for a long-term study of stable fly population dynamics in the central Great Plains. An additional study compared the relative size of flies sampled from the general population with that of flies sampled emerging from substrates associated with livestock production. Flies developing in livestock associated substrates are significantly larger than those in the general population indicating that other types of developmental sites are contributing significant numbers of flies to the general population. Because efforts to identify those sites have yet to be successful, we speculate that they may be sites with low densities of developing stable flies, but covering large areas such as croplands and grasslands. The stable fly surveillance methods discussed can be used and further improved for monitoring stable fly populations for research and management programs

Large strain compressive response of 2-D periodic representative volume element for random foam microstructures

A numerical investigation has been conducted to determine the influence of Representative Volume Element (RVE) size and degree of irregularity of polymer foam microstructure on its compressive mechanical properties, including stiffness, plateau stress and onset strain of densification. Periodic two-dimensional RVEs have been generated using a Voronoi-based numerical algorithm and compressed. Importantly, self-contact of the foam’s internal microstructure has been incorporated through the use of shell elements, allowing simulation of the foam well into the densification stage of compression; strains of up to 80 percent are applied. Results suggest that the stiffness of the foam RVE is relatively insensitive to RVE size but tends to soften as the degree of irregularity increases. Both the shape of the plateau stress and the onset strain of densification are sensitive to both the RVE size and degree of irregularity. Increasing the RVE size and decreasing the degree of irregularity both tend to result in a decrease of the gradient of the plateau region, while increasing the RVE size and degree of irregularity both tend to decrease the onset strain of densification. Finally, a method of predicting the onset strain of densification to an accuracy of about 10 per cent, while reducing the computational cost by two orders of magnitude is suggested

Respiratory Rate Estimation from Face Videos

Vital signs, such as heart rate (HR), heart rate variability (HRV), respiratory rate (RR), are important indicators for a person's health. Vital signs are traditionally measured with contact sensors, and may be inconvenient and cause discomfort during continuous monitoring. Commercial cameras are promising contact-free sensors, and remote photoplethysmography (rPPG) have been studied to remotely monitor heart rate from face videos. For remote RR measurement, most prior art was based on small periodical motions of chest regions caused by breathing cycles, which are vulnerable to subjects' voluntary movements. This paper explores remote RR measurement based on rPPG obtained from face videos. The paper employs motion compensation, two-phase temporal filtering, and signal pruning to capture signals with high quality. The experimental results demonstrate that the proposed framework can obtain accurate RR results and can provide HR, HRV and RR measurement synergistically in one framework