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Deep Science: Robot perception, acoustic monitoring, using ML to detect arthritis

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Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect the most relevant recent discoveries and papers — particularly in but not limited to artificial intelligence — and explain why they matter.

The topics in this week’s Deep Science column are a real grab bag that range from planetary science to whale tracking. There are also some interesting insights from tracking how social media is used and some work that attempts to shift computer vision systems closer to human perception (good luck with that).

ML model detects arthritis early

Image Credits: UC San Diego

One of machine learning’s most reliable use cases is training a model on a target pattern, say a particular shape or radio signal, and setting it loose on a huge body of noisy data to find possible hits that humans might struggle to perceive. This has proven useful in the medical field, where early indications of serious conditions can be spotted with enough confidence to recommend further testing.

This arthritis detection model looks at X-rays, same as doctors who do that kind of work. But by the time it’s visible to human perception, the damage is already done. A long-running project tracking thousands of people for seven years made for a great training set, making the nearly imperceptible early signs of osteoarthritis visible to the AI model, which predicted it with 78% accuracy three years out.

The bad news is that knowing early doesn’t necessarily mean it can be avoided, as there’s no effective treatment. But that knowledge can be put to other uses — for example, much more effective testing of potential treatments. “Instead of recruiting 10,000 people and following them for 10 years, we can just enroll 50 people who we know are going to be getting osteoarthritis … Then we can give them the experimental drug and see whether it stops the disease from developing,” said co-author Kenneth Urish. The study appeared in PNAS.

Using acoustic monitoring to preemptively save the whales

It’s amazing to think that ships still collide with and kill large whales on a regular basis, but it’s true. Voluntary speed reductions haven’t been much help, but a smart, multisource system called Whale Safe is being put in play in the Santa Barbara channel that could hopefully give everyone a better idea of where the creatures are in real-time.

Image Credits: UW/UC Santa Barbara

The system uses underwater acoustic monitoring, near-real-time forecasting of likely feeding areas, actual sightings and a dash of machine learning (to identify whale calls quickly) to produce a prediction for whale presence along a given course. Large container ships can then make small adjustments well-ahead of time instead of trying to avoid a pod at the last minute.

“Predictive models like this give us a clue for what lies ahead, much like a daily weather forecast,” said Briana Abrahms, who led the effort from the University of Washington. “We’re harnessing the best and most current data to understand what habitats whales use in the ocean, and therefore where whales are most likely to be as their habitats shift on a daily basis.”

Incidentally, Salesforce founder Marc Benioff and his wife Lynne helped establish the UC Santa Barbara center that made this possible.

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Science

Too bright to breed

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Night light from coastal cities overpowers natural signals for coral spawning from neighboring reefs.

PHOTO: NOKURO/ALAMY STOCK PHOTO

Most coral species reproduce through broadcast spawning. For such a strategy to be successful, coordination has had to evolve such that gametes across clones are released simultaneously. Over millennia, lunar cycles have facilitated this coordination, but the recent development of bright artificial light has led to an overpowering of these natural signals. Ayalon et al. tested for the direct impact of different kinds of artificial light on different species of corals. The authors found that multiple lighting types, including cold and warm light-emitting diode (LED) lamps, led to loss of synchrony and spawning failure. Further, coastal maps of artificial lighting globally suggest that it threatens to interfere with coral reproduction worldwide and that the deployment of LED lights, the blue light of which penetrates deeper into the water column, is likely to make the situation even worse.

Curr. Biol. 10.1016/j.cub.2020.10.039 (2020).

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SpaceX launches Starlink app and provides pricing and service info to early beta testers

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SpaceX has debuted an official app for its Starlink satellite broadband internet service, for both iOS and Android devices. The Starlink app allows users to manage their connection – but to take part you’ll have to be part of the official beta program, and the initial public rollout of that is only just about to begin, according to emails SpaceX sent to potential beta testers this week.

The Starlink app provides guidance on how to install the Starlink receiver dish, as well as connection status (including signal quality), a device overview for seeing what’s connected to your network, and a speed test tool. It’s similar to other mobile apps for managing home wifi connections and routers. Meanwhile, the emails to potential testers that CNBC obtained detail what users can expect in terms of pricing, speeds and latency.

The initial Starlink public beta test is called the “Better than Nothing Beta Program,” SpaceX confirms in their app description, and will be rolled out across the U.S. and Canada before the end of the year – which matches up with earlier stated timelines. As per the name, SpaceX is hoping to set expectations for early customers, with speeds users can expect ranging from between 50Mb/s to 150Mb/s, and latency of 20ms to 40ms according to the customer emails, with some periods including no connectivity at all. Even with expectations set low, if those values prove accurate, it should be a big improvement for users in some hard-to-reach areas where service is currently costly, unreliable and operating at roughly dial-up equivalent speeds.

Image Credits: SpaceX

In terms of pricing, SpaceX says in the emails that the cost for participants in this beta program will be $99 per moth, plus a one-time cost of $499 initially to pay for the hardware, which includes the mounting kit and receiver dish, as well as a router with wifi networking capabilities.

The goal eventually is offer reliably, low-latency broadband that provides consistent connection by handing off connectivity between a large constellation of small satellites circling the globe in low Earth orbit. Already, SpaceX has nearly 1,000 of those launched, but it hopes to launch many thousands more before it reaches global coverage and offers general availability of its services.

SpaceX has already announced some initial commercial partnerships and pilot programs for Starlink, too, including a team-up with Microsoft to connect that company’s mobile Azure data centers, and a project with an East Texas school board to connect the local community.

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Erratum for the Report “Meta-analysis reveals declines in terrestrial but increases in freshwater insect abundances” by R. Van Klink, D. E. Bowler, K. B. Gongalsky, A. B. Swengel, A. Gentile, J. M. Chase

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S. Rennie, J. Adamson, R. Anderson, C. Andrews, J. Bater, N. Bayfield, K. Beaton, D. Beaumont, S. Benham, V. Bowmaker, C. Britt, R. Brooker, D. Brooks, J. Brunt, G. Common, R. Cooper, S. Corbett, N. Critchley, P. Dennis, J. Dick, B. Dodd, N. Dodd, N. Donovan, J. Easter, M. Flexen, A. Gardiner, D. Hamilton, P. Hargreaves, M. Hatton-Ellis, M. Howe, J. Kahl, M. Lane, S. Langan, D. Lloyd, B. McCarney, Y. McElarney, C. McKenna, S. McMillan, F. Milne, L. Milne, M. Morecroft, M. Murphy, A. Nelson, H. Nicholson, D. Pallett, D. Parry, I. Pearce, G. Pozsgai, A. Riley, R. Rose, S. Schafer, T. Scott, L. Sherrin, C. Shortall, R. Smith, P. Smith, R. Tait, C. Taylor, M. Taylor, M. Thurlow, A. Turner, K. Tyson, H. Watson, M. Whittaker, I. Woiwod, C. Wood, UK Environmental Change Network (ECN) Moth Data: 1992-2015, NERC Environmental Information Data Centre (2018); .

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