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very nice paper on adaptation of cells to microgravity

You need to avoid posting these types of links in the title as it is not managed well by plugins connected to our diigo account. Try to go to the source next time, and get rid of useless url codes.

How do humans construct their mental representations of the passage of time? The universalist account claims that abstract concepts like time are universal across humans. In contrast, the linguistic relativity hypothesis holds that speakers of different languages represent duration differently. The precise impact of language on duration representation is, however, unknown. Here, we show that language can have a powerful role in transforming humans' psychophysical experience of time. Contrary to the universalist account, we found language-specific interference in a duration reproduction task, where stimulus duration conflicted with its physical growth. When reproducing duration, Swedish speakers were misled by stimulus length, and Spanish speakers were misled by stimulus size/quantity. These patterns conform to preferred expressions of duration magnitude in these languages (Swedish: long/short time; Spanish: much/small time). Critically, Spanish-Swedish bilinguals performing the task in both languages showed different interference depending on language context. Such shifting behavior within the same individual reveals hitherto undocumented levels of flexibility in time representation. Finally, contrary to the linguistic relativity hypothesis, language interference was confined to difficult discriminations (i.e., when stimuli varied only subtly in duration and growth), and was eliminated when linguistic cues were removed from the task. These results reveal the malleable nature of human time representation as part of a highly adaptive information processing system.

The rules for a stellar death seem pretty simple. If the star isn't that massive, it burns out into a carbon-rich remnant called a white dwarf. If it's big enough, the star ends in a bang, exploding in a supernova that can leave behind a neutron star or a black hole.

"Hinton's new approach, known as capsule networks, is a twist on neural networks intended to make machines better able to understand the world through images or video. In one of the papers posted last week, Hinton's capsule networks matched the accuracy of the best previous techniques on a standard test of how well software can learn to recognize handwritten digits." Links to papers: https://arxiv.org/abs/1710.09829 https://openreview.net/forum?id=HJWLfGWRb&noteId=HJWLfGWRb

impressive!

seems a very impressive guy :"Hinton formed his intuition that vision systems need such an inbuilt sense of geometry in 1979, when he was trying to figure out how humans use mental imagery. He first laid out a preliminary design for capsule networks in 2011. The fuller picture released last week was long anticipated by researchers in the field. "Everyone has been waiting for it and looking for the next great leap from Geoff," says Kyunghyun Cho, a professor"

One overview about a recent publication (10/06/2014 http://journals.aps.org/prl/abstract/10.1103/PhysRevLett.112.236802) showing room temperature operation of a new type of laser 'discovered' in 2013, the polariton laser. This new type of laser shows extremely low threshold current density - 2 orders of magnitude below Vertical Cavity Surface Emitting Lasers (VCSEL) - and it is believed to become one important component of nanophotonic integrated components.

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

:) always thought so!

The direct link without the Washington post paywall https://www.researchgate.net/publication/329131866_Going_With_Your_Gut_The_Inaccuracy_of_Forecast_Revisions_in_a_Football_Score_Prediction_Game

Actually read the paper now and think that this is very doubtful that this could be generalised. They used predictions for football games by those betting on outcomes ...

I wonder if this could be used for irregular adaptive grids? From the description: "One application of the diagram is the idea of Voronoi entropy - a mathematical tool for quantitative characterisation of the orderliness of points distributed on a surface - i.e. how visually 'ordered' the tessellation is. I found this idea particularly fascinating especially when thinking about the aperiodicity and the infinite structure of the Penrose tiling. In these visuals, the Voronoi diagram is created using the vertices of the Penrose as its seed points. This creates a new type of Penrose Tiling, clearly different from the classical Penrose, however still exhibiting the fivefold structure of the original, while 'defects' begin to appear at the peripheries."

A nice look back into how JWST came to be and what it's all about. :)

Brings back some memories... Enjoy!

indeed, I still have the bottle! don#t dare drinking it