Lifeboat Foundation: Safeguarding Humanity
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Purdue University scientists led a comprehensive analysis of research concerning the effects of microplastics on aquatic life, with the results showing widely different impacts among different types of animals. Strong negative effects were particularly apparent for small animals, such as larval fish and zooplankton, a source of food for many species, suggesting serious potential consequences that could ripple throughout the food web.

Tomas Höök, an associate professor in Purdue University’s Department of Forestry and Natural Resources and director of the Illinois-Indiana Sea Grant College Program, led a team that designed a meta-analysis of research related to the effects of microplastics on aquatic life. The analysis, published in the journal Science of the Total Environment, used results from 43 other studies that each considered the effects of microplastics on consumption of , growth, reproduction, and/or survival of aquatic . The analysis mathematically calculated one or more effect size(s) for each study, then those effects were combined statistically to understand the big-picture effect on animals. The animals included in this study were all aquatic but ranged from fish to mussels to sea urchins to worms.

The most significant findings included:

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Rough translation: Blue Origin doesn’t give a damn about SpaceX’s media circus. It’s not trying to outdo competitors with each subsequent project — the company is working on just two rockets (New Shepard and New Glenn) with hopes to launch a manned flight before the end of 2018. Blue Origin is worrying about Blue Origin. That’s it.

It’s a bit too early to tell whether Blue Origin’s strategy is any better than SpaceX’s, or vice versa. Competition is a powerful force for innovation. But with the commercial space industry quickly growing (and SpaceX threatening to monopolize it), it’s easy enough to keep innovating in an effort to one-up the competition, losing sight of the main goal in the process.

One way to judge who wins? Whoever sends humans farther than they’ve ever gone. In that sense, the companies are striving for the same goal in the long term, and those that keep their eyes on the prize might fare best. In her interview with Engadget, Blue Origin’s Dietrich said that the company’s vision of millions of people living and working in space meant that they “are applauding all launch operators that are building new and more capable systems.”

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Looking deep into the observable Universe – and hence, back to the earliest periods of time – is an immensely fascinating thing. In so doing, astronomers are able to see the earliest galaxies in the Universe and learn more about how they evolved over time. From this, they are not only able to see how large-scale structures (like galaxies and galaxy clusters) formed, but also the role played by dark matter.

Most recently, an international team of scientists used the Atacama Large Millimeter-submillimeter Array (ALMA) to observe the Universe when it was just 1.4 billion years old. What they observed was a “protocluster”, a series of 14 galaxies located 12.4 billion light-years away that were about to merge. This would result in the formation of a massive galaxy cluster, one of the largest objects in the known Universe.

The study which described their findings, titled “A massive core for a cluster of galaxies at a redshift of 4.3”, recently appeared in the journal Nature. The study was led by Tim Miller – an astronomer from Dalhousie University, Halifax, and Yale University – and included members from NASA’s Jet Propulsion Laboratory, the European Southern Observatory (ESO), Canada’s National Research Council, the Harvard-Smithsonian Center for Astrophysics, the National Radio Astronomy Observatory, and multiple universities and research institutions.

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Deepcric is a deep learning system for cricket. It looks at cricket video and does scene segmentation, scene classification, automatic commentary generation, targeted highlights generation, player identification, and player stats extraction.

Deep learning has been applied everywhere. From imagenet [1] to disease identification [2] to large-scale video classification [3] to text classification [4], there are barely any areas where people have not applied deep learning. But interestingly, there has been very little work in applying data science and deep learning to the game of cricket. This post is a detailed overview of my final year project at the FAST National University. We have developed a deep learning based system that is able to do many tasks in cricket in an automated way. Some of these tasks are:

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