Matrix completion and robust principal component analysis have been widely used for the recovery of data suffering from missing entries or outliers. In many real-world applications however, the data is also time-varying, and the naive approach of per-snapshot recovery is both expensive and sub-optimal. This paper develops generative Bayesian models that fit sequential multivariate measurements arising from a low-dimensional time-varying subspace. A variational Bayesian subspace filtering approach is proposed that learns the underlying subspace and its state-transition matrix. Different from the plethora of deterministic counterparts, the proposed approach utilizes automatic relevance determination priors that obviate the need to tune key parameters such as rank and noise power. We also propose a forward-backward algorithm that allows the updates to be carried out at low complexity. Extensive tests over traffic and electricity data demonstrate the superior imputation, outlier rejection, and temporal prediction prowess of the proposed algorithm over the state-of-the-art matrix/tensor completion algorithms.
The Covid-19 pandemic has impacted and infiltrated every aspect of our lives. Successive lockdowns, social distancing measures, and reduction in economic activity have developed a new way of living and, in many cases, tend to lead to depression. The initial strict lockdown for about 3 months and eventually for a few more months has imposed greater challenges on children and adolescents in terms of psychological problems and psychiatric disorders. Regardless of their viral infection status, many people have been affected by the psychosocial changes associated with the Covid-19 pandemic. In the present review, we have attempted to evaluate the impact of COVID on the mental health of people from different age groups and occupations. The present review has highlighted the need for taking effective measures by the stakeholder to cope with depression among human population groups worldwide.
Accurate expected time of arrival (ETA) information is crucial in maintaining the quality of service of public transit. Recent advances in artificial intelligence (AI) has led to more effective models for ETA estimation that rely heavily on a large GPS datasets. More importantly, these are mainly cabs based datasets which may not be fit for bus based public transport. Consequently, the latest methods may not be applicable for ETA estimation in cities with the absence of large training data data set. On the other hand, the ETA estimation problem in many cities needs to be solved in the absence of big datasets that also contains outliers, anomalies and may be incomplete. This work presents a simple but robust model for ETA estimation for a bus route that only relies on the historical data of the particular route. We propose a system that generates ETA information for a trip and updates it as the trip progresses based on the real-time information. We train a deep learning based generative model that learns the probability distribution of ETA data across trips and conditional on the current trip information updates the ETA information on the go. Our plug and play model not only captures the non-linearity of the task well but that any transit agency can use without needing any other external data source. The experiments run over three routes data collected in the city of Delhi illustrates the promise of our approach.
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