Emerging Global Health Crisis of Our Times- Climate Change

The progress of the human race over the last 200 years is unprecedented in recent history. Rapid industrialization, urbanization, and consumerism have made lives easier for humankind. Still, these changes come at a very high price. We never anticipated that we will have to pay the price in the form of climate change and global warming. Our planet, the earth is getting warmer by 0.85 ̊centigrde annually for the last one hundred and seventy years. Hence, glaciers are melting faster than ever, water levels are rising, and cities are sinking, while greenhouse gas emission numbers are at their highest points in human history. Unfortunately we humans are living in anthropogenic epoch and are also speeding up the destruction of the earth's ecosystem by being the dominant cause of the warming observed since the 20th century. Deforestation coupled with increased greenhouse gas emissions has led to a surge of heat-waves globally. These environmental disasters not only affect the environment, plants, and land but also have a profound direct and indirect impact on the health of people. In-fact the health impact has already debuted in the form of worsening key health indicators. In Pakistan alone, the 2015 heat-wave claimed the lives of twelve hundred people in Sindh province. Due to variable rainfall patterns that affect the availability of fresh water, it also affects food production & delivery and brings on the drought. Quality of air, clean drinking water, and availability of food are the top three indicators most influenced by these disasters. Coupled with these, the more than the frequent occurrence of natural calamities; tsunamis, wildfires, snowstorms, and extremes of temperatures has put an extra financial burden on already, stretched to limits budgets of health

Conditional Random Field Autoencoders for Unsupervised Structured Prediction

We introduce a framework for unsupervised learning of structured predictors with overlapping, global features. Each input's latent representation is predicted conditional on the observable data using a feature-rich conditional random field. Then a reconstruction of the input is (re)generated, conditional on the latent structure, using models for which maximum likelihood estimation has a closed-form. Our autoencoder formulation enables efficient learning without making unrealistic independence assumptions or restricting the kinds of features that can be used. We illustrate insightful connections to traditional autoencoders, posterior regularization and multi-view learning. We show competitive results with instantiations of the model for two canonical NLP tasks: part-of-speech induction and bitext word alignment, and show that training our model can be substantially more efficient than comparable feature-rich baselines