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Two ways of measuring the universe's expansion rate yield two conflicting answers. Many point to the possibility of new physics at work, but a new analysis argues that unseen errors could be to blame. See also this work based on GAIA data that, on the other hand, reinforces the discrepancy: Milky Way Cepheid Standards for Measuring Cosmic Distances and Application to Gaia DR2: Implications for the Hubble Constant https://arxiv.org/abs/1804.10655

Some locations on Mars are known to have water ice just below the surface, but how much has remained unclear. Dundas et al. used data from two orbiting spacecraft to examine eight locations where erosion has occurred. This revealed cliffs composed mostly of water ice, which is slowly sublimating as it is exposed to the atmosphere. The ice sheets extend from just below the surface to a depth of 100 meters or more and appear to contain distinct layers, which could preserve a record of Mars' past climate. They might even be a useful source of water for future human exploration of the red planet.

Old data reveal new evidence of europa spouting plumes into space.

A college course at University of Washington on "Calling Bullshit". We should invite them to give a lunch lecture at ESA... Our aim in this course is to teach you how to think critically about the data and models that constitute evidence in the social and natural sciences. While bullshit may reach its apogee in the political domain, this is not a course on political bullshit. Instead, we will focus on bullshit that comes clad in the trappings of scholarly discourse. Our learning objectives are straightforward. After taking the course, you should be able to: * Remain vigilant for bullshit contaminating your information diet. * Recognize said bullshit whenever and wherever you encounter it. * Figure out for yourself precisely why a particular bit of bullshit is bullshit. * Provide a statistician or fellow scientist with a technical explanation of why a claim is bullshit. * Provide your crystals-and-homeopathy aunt or casually racist uncle with an accessible and persuasive explanation of why a claim is bullshit. We will be astonished if these skills do not turn out to be among the most useful and most broadly applicable of those that you acquire during the course of your college education.

love it: "Politicians are unconstrained by facts. Science is conducted by press release. Higher education rewards bullshit over analytic thought. Startup culture elevates bullshit to high art. Advertisers wink conspiratorially and invite us to join them in seeing through all the bullshit - and take advantage of our lowered guard to bombard us with bullshit of the second order. The majority of administrative activity, whether in private business or the public sphere, seems to be little more than a sophisticated exercise in the combinatorial reassembly of bullshit. We're sick of it. It's time to do something, and as educators, one constructive thing we know how to do is to teach people. So, the aim of this course is to help students navigate the bullshit-rich modern environment by identifying bullshit, seeing through it, and combating it with effective analysis and argument."

"To create Sketch-RNN, Google Brain researchers David Ha and Douglas Eck collected more than five million user-drawn sketches from the Google tool Quick, Draw! Each time a user drew something on the app, it recorded not only the final image, but also the order and direction of every pen stroke used to make it. The resulting data gives a more complete picture (ho, ho, ho) of how we really draw." It's funny because this David Ha used to be a quant banker ha ha

The source is anonymous

On similar subject, but older http://www.businessinsider.com/nick-szabo-bitcoin-2014-4

This has been around for over a year. The current trend in deep learning is "deeper is better". But a consequence of this is that for a given network depth, we can only feasibly evaluate a tiny fraction of the "search space" of NN architectures. The current approach to choosing a network architecture is to iteratively add more layers/units and keeping the architecture which gives an increase in the accuracy on some held-out data set i.e. we have the following information: {NN, accuracy}. Clearly, this process can be automated by using the accuracy as a 'signal' to a learning algorithm. The novelty in this work is they use reinforcement learning with a recurrent neural network controller which is trained by a policy gradient - a gradient-based method. Previously, evolutionary algorithms would typically be used. In summary, yes, the results are impressive - BUT this was only possible because they had access to Google's resources. An evolutionary approach would probably end up with the same architecture - it would just take longer. This is part of a broader research area in deep learning called 'meta-learning' which seeks to automate all aspects of neural network training.

Btw that techxplore article was cringing to read - if interested read this article instead: https://research.googleblog.com/2017/05/using-machine-learning-to-explore.html

Thats a bit of a stretch .. uing parkng occupancy and internet requests to conclude the virus could have been active before the current belief!

This is the current page for ESA datalabs. Its an ongoing activity that we should monitor and join with Kelvins update if possible.

I like the "computational narrative" term!

A somewhat eclectic paper about events collected with an event camera onboard the ISS, courtesy of Western Sydney University and...the US AirForce Academy.