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interesting story with many juicy details on how they proceed ... (similarly interesting nickname for the "operation" chosen by our british friends) "The spies used the IP addresses they had associated with the engineers as search terms to sift through their surveillance troves, and were quickly able to find what they needed to confirm the employees' identities and target them individually with malware. The confirmation came in the form of Google, Yahoo, and LinkedIn "cookies," tiny unique files that are automatically placed on computers to identify and sometimes track people browsing the Internet, often for advertising purposes. GCHQ maintains a huge repository named MUTANT BROTH that stores billions of these intercepted cookies, which it uses to correlate with IP addresses to determine the identity of a person. GCHQ refers to cookies internally as "target detection identifiers." Top-secret GCHQ documents name three male Belgacom engineers who were identified as targets to attack. The Intercept has confirmed the identities of the men, and contacted each of them prior to the publication of this story; all three declined comment and requested that their identities not be disclosed. GCHQ monitored the browsing habits of the engineers, and geared up to enter the most important and sensitive phase of the secret operation. The agency planned to perform a so-called "Quantum Insert" attack, which involves redirecting people targeted for surveillance to a malicious website that infects their computers with malware at a lightning pace. In this case, the documents indicate that GCHQ set up a malicious page that looked like LinkedIn to trick the Belgacom engineers. (The NSA also uses Quantum Inserts to target people, as The Intercept has previously reported.) A GCHQ document reviewing operations conducted between January and March 2011 noted that the hack on Belgacom was successful, and stated that the agency had obtained access to the company's

I knew I wasn't using TOR often enough...

Cool! It seems that after all it is best to restrict employees' internet access only to work-critical areas... @Paul TOR works on network level, so it would not help here much as cookies (application level) were exploited.

I like this work... it mixes physics, networks and biology ! Anyone heard about her ? Here's an interesting paper found on this website: http://nuweb.neu.edu/gbianconi/condensation.pdf

We worked on this with Luzi a few years back ... while the analogy is original and interesting it fails to capture the dynamics of a network, e.g. if a network has hubs that grow and shrink .... Luzi worked on an extended model to solve this issue, but, if I remember correctly, he got stuck in a computationally very hard problem .... We intended to develop and use the extended model to define relevant characteristic of the ESA network formed by mail exchanges.....

...but then the CMS YGT didn't really like the project

that would be an interesting "Systems of Systems" study for once ...

Computer scientists prove that networks of cells can compute as efficiently as networks of computers linked via the internet

Sounds too super to be true, no?

> Sounds too super to be true, no? Yeah, how else may it sound, being a combination of hi-quality (I assume) research targeted at attracting funding, raised to the power of Science Daily's pop-pseudo-scientific journalists' bu****it? Original article starts with a cool sentence too: > The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. ...a good starting point for a never-ending philosophers' debate... Now seriously, because of a big name behind the study, I'm very curious to read the original article. Although I expect the conclusion to be that in practical cases (i.e. the cases of "networks" you *would like to* "control"), you need to control all nodes or something equally impractical...

then I am looking forward to reading your conclusions here after you will have actually read the paper

Network scientists have discovered how social networks can create the illusion that something is common when it is actually rare. One of the curious things about social networks is the way that some messages, pictures, or ideas can spread like wildfire while others that seem just as catchy or interesting barely register at all.

"The effect is largest in the political blogs network, where as many as 60%-70% of nodes will have a majority active neighbours, even when only 20% of the nodes are active." How convenient :-)

"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"

Deep neural networks "dreaming" psychedelic images

Google released the code behind this, and applications of it are now popping up: - http://googleresearch.blogspot.nl/2015/07/deepdream-code-example-for-visualizing.html - https://imgur.com/a/nVlLe - https://twitter.com/hashtag/deepdream

Funny how representation errors in NNs suddenly become art. God.... neo-post-modernism.

The "Biological Economics" thing is a hyping (or misunderstanding) of the BBC article. The work it refers to seems to be an application of Complex Networks theory to financial Networks. I found what appear to be some of the related publications: Andrew G. Haldane (April 2009) Rethinking the financial network (further references in the footnote to page 10) Erlend Nier, Jing Yang, Tanju Yorulmazer and Amadeo Alentorn (April 2008) Network models and financial stability Funny how these issues have been repeatedly popping up at the ACT in recent weeks. This connects both with the discussions on information spreading in Networks, and with roadmaps' robustness.

Recently, many competitions in the computer vision domain have been won by huge convolutional networks. In the image net competition, the convolutional network approach halves the error from ~30% to ~15%! Key changes that make this happen: weight-sharing to reduce the search space, and training with a massive GPU approach. (See also the work at IDSIA: http://www.idsia.ch/~juergen/vision.html) This should please Francisco :)

...mmmmm... they use 60 million parameters and 650,000 neurons on a task that one can somehow consider easier than (say) predicting a financial crisis ... still they get 15% of errors .... reminds me of a comic we saw once ... cat http://www.sarjis.info/stripit/abstruse-goose/496/the_singularity_is_way_over_there.png

I think the ultimate solution is still to put a human brain in a jar and use it for pattern recognition. Maybe we should get a stagiaire for this..?

The algorithm that regulates the flow of ants is evolving toward minimizing operating costs rather than immediate accumulation.

Turns out even ants can profit from a siesta on a hot day and they use network security and repair mechanisms. Maybe there is still something undiscovered that we can apply for our own networks.

it seems that there are indications that we are differently wired .... Sex differences in human behavior show adaptive complementarity: Males have better motor and spatial abilities, whereas females have superior memory and social cognition skills. Studies also show sex differences in human brains but do not explain this complementarity. In this work, we modeled the structural connectome using diffusion tensor imaging in a sample of 949 youths (aged 8-22 y, 428 males and 521 females) and discovered unique sex differences in brain connectivity during the course of development. Connection-wise statistical analysis, as well as analysis of regional and global network measures, presented a comprehensive description of network characteristics. In all supratentorial regions, males had greater within-hemispheric connectivity, as well as enhanced modularity and transitivity, whereas between-hemispheric connectivity and cross-module participation predominated in females. However, this effect was reversed in the cerebellar connections. Analysis of these changes developmentally demonstrated differences in trajectory between males and females mainly in adolescence and in adulthood. Overall, the results suggest that male brains are structured to facilitate connectivity between perception and coordinated action, whereas female brains are designed to facilitate communication between analytical and intuitive processing modes.

I like this abstract: sex, sex, sex, sex, SEX, SEX, SEX, SEX...!!! I wonder if the "sex differences" are related to gender-specific differences...

Plant stuff and network design.

"The Complexity of Nash Equilibria,", It also suggests that the Nash equilibrium may not be an accurate prediction of behavior in all situations. Daskalakis's research emphasizes the need for new, computationally meaningful methods for modeling strategic behavior in complex systems such as those encountered in financial markets, online systems, and social networks.

This is an article that examines information network both enigneered and evolved ones. The find striking similarities and examine the differences.

for Tobi! nice analogy between the threat and the fast responding in human network

Yet another example of "because scientifically accurate title would sound sooo boring".

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

For the Barabasi fans ...