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Highlights d Applies cutting-edge natural language processing methods to movie subtitles d Considers 700 popular Bollywood movies spanning 70 years to analyze evolving trends d Tracks evolving biases toward gender, color, geographic region, and religion d Compares movies from Bollywood, Hollywood, and critically acclaimed world movies

Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional machine learning models have been applied for this problem. These models are fast and simple, but they often fail to capture the spatio-temporal correlation that soil moisture exhibits over a region. In this work, we propose a novel graph neural network based solution that learns temporal graph structures and forecast soil moisture in an end-to-end framework. Our solution is able to handle the problem of missing ground truth soil moisture which is common in practice. We show the merit of our algorithm on real-world soil moisture data.

Bollywood, aka the Mumbai film industry, is one of the biggest movie industries in the world with a current movie market share of worth 2.1 billion dollars and a target audience base of 1.2 billion people. While the entertainment impact in terms of lives that Bollywood can potentially touch is mammoth, no NLP study on social biases in Bollywood content exists. We thus seek to understand social biases in a developing country through the lens of popular movies. Our argument is simple -- popular movie content reflects social norms and beliefs in some form or shape. We present our preliminary findings on a longitudinal corpus of English subtitles of popular Bollywood movies focusing on (1) social bias toward a fair skin color (2) gender biases, and (3) gender representation. We contrast our findings with a similar corpus of Hollywood movies. Surprisingly, we observe that much of the biases we report in our preliminary experiments on the Bollywood corpus, also gets reflected in the Hollywood corpus.

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