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The crayola-fication of the world: How we gave colors names, and it messed with our brains (part I)

The crayola-fication of the world: How we gave colors names, and it messed with our brains (part I) | Social Foraging | Scoop.it

In Japan, people often refer to traffic lights as being blue in color. And this is a bit odd, because the traffic signal indicating ‘go’ in Japan is just as green as it is anywhere else in the world. So why is the color getting lost in translation? This visual conundrum has its roots in the history of language.

 

Blue and green are similar in hue. They sit next to each other in a rainbow, which means that, to our eyes, light can blend smoothly from blue to green or vice-versa, without going past any other color in between. Before the modern period, Japanese had just one word, Ao, for both blue and green. The wall that divides these colors hadn’t been erected as yet. As the language evolved, in the Heian period around the year 1000, something interesting happened. A new word popped into being – midori – and it described a sort of greenish end of blue. Midori was a shade of ao, it wasn’t really a new color in its own right.

 

One of the first fences in this color continuum came from an unlikely place – crayons. In 1917, the first crayons were imported into Japan, and they brought with them a way of dividing a seamless visual spread into neat, discrete chunks. There were different crayons for green (midori) and blue (ao), and children started to adopt these names. But the real change came during the Allied occupation of Japan after World War II, when new educational material started to circulate. In 1951, teaching guidelines for first grade teachers distinguished blue from green, and the word midori was shoehorned to fit this new purpose.

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Coding Together at Scale: GitHub as a Collaborative Social Network

GitHub is the most popular repository for open source code. It has more than 3.5 million users, as the company declared in April 2013, and more than 10 million repositories, as of December 2013. It has a publicly accessible API and, since March 2012, it also publishes a stream of all the events occurring on public projects. Interactions among GitHub users are of a complex nature and take place in different forms. Developers create and fork repositories, push code, approve code pushed by others, bookmark their favorite projects and follow other developers to keep track of their activities.
In this paper we present a characterization of GitHub, as both a social network and a collaborative platform. To the best of our knowledge, this is the first quantitative study about the interactions happening on GitHub. We analyze the logs from the service over 18 months (between March 11, 2012 and September 11, 2013), describing 183.54 million events and we obtain information about 2.19 million users and 5.68 million repositories, both growing linearly in time. We show that the distributions of the number of contributors per project, watchers per project and followers per user show a power-law-like shape. We analyze social ties and repository-mediated collaboration patterns, and we observe a remarkably low level of reciprocity of the social connections. We also measure the activity of each user in terms of authored events and we observe that very active users do not necessarily have a large number of followers. Finally, we provide a geographic characterization of the centers of activity and we investigate how distance influences collaboration.

 

Coding Together at Scale: GitHub as a Collaborative Social Network
Antonio Lima, Luca Rossi, Mirco Musolesi

http://arxiv.org/abs/1407.2535


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Mathematica 10 launched with 700+ New Functions and an Amazing Amount of R&D

Mathematica 10 launched with 700+ New Functions and an Amazing Amount of R&D | Social Foraging | Scoop.it

Mathematica 10 has more new features than any previous version. It is satisfying to see such a long curve of accelerating development—and to realize that there are more new functions being added with Mathematica 10 than there were functions altogether in Mathematica 1. So what is the new functionality in Mathematica 10? It’s a mixture of completely new areas and directions (like geometric computation, machine learning and geographic computation)—together with extensive strengthening, polishing and expanding of existing areas. It’s also a mixture of things I’ve long planned for us to do—but which had to wait for us to develop the necessary technology—together with things I’ve only fairly recently realized we’re in a position to tackle.


When you first launch Mathematica 10 there are some things you’ll notice right away. One is that Mathematica 10 is set up to connect immediately to the Wolfram Cloud. Unlike Wolfram Programming Cloud—or the upcoming Mathematica Online—Mathematica 10 doesn’t run its interface or computations in the cloud. Instead, it maintains all the advantages of running these natively on your local computer—but connects to the Wolfram Cloud so it can have cloud-based files and other forms of cloud-mediated sharing, as well as the ability to access cloud-based parts of the Wolfram Knowledgebase.

 

If you’re an existing Mathematica user, you’ll notice some changes when you start using notebooks in Mathematica 10. Like there’s now autocompletion everywhere—for option values, strings, wherever. And there’s also a hovering help box that lets you immediately get function templates or documentation. And there’s also—as much requested by the user community—computation-aware multiple undo. It’s horribly difficult to know how and when you can validly undo Mathematica computations—but in Mathematica 10 we’ve finally managed to solve this to the point of having a practical multiple undo.


And in Mathematica 10 one important area where this is happening is machine learning. Inside the system there are all kinds of core algorithms familiar to experts—logistic regression, random forests, SVMs, etc. And all kinds of preprocessing and scoring schemes. But to the user there are just two highly automated functions: Classify and Predict. And with these functions, it’s now easy to call on machine learning whenever one wants.


There are huge new algorithmic capabilities in Mathematica 10 in graph theory, image processing, control theory and lots of other areas. Sometimes one’s not surprised that it’s at least possible to have such-and-such a function—even though it’s really nice to have it be as clean as it is in Mathematica 10. But in other cases it at first seems somehow impossible that the function could work.

 

There are all kinds of issues. Maybe the general problem is undecidable, or theoretically intractable. Or it’s ill conditioned. Or it involves too many cases. Or it needs too much data. What’s remarkable is how often—by being algorithmically sophisticated, and by leveraging what we’ve built in Mathematica and the Wolfram Language—it’s possible to work around these issues, and to build a function that covers the vast majority of important practical cases.

 

Another important issue is just how much we can represent and do computation on. Expanding this is a big emphasis in the Wolfram Language—and Mathematica 10 has access to everything that’s been developed there. And so, for example, in Mathematica 10 there’s an immediate symbolic representation for dates, times and time series—as well as for geolocations and geographic data.


 


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Connecting Core Percolation and Controllability of Complex Networks

Connecting Core Percolation and Controllability of Complex Networks | Social Foraging | Scoop.it
Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks.
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Learning ability in math and reading are tightly linked and highly genetic, scientists say

Learning ability in math and reading are tightly linked and highly genetic, scientists say | Social Foraging | Scoop.it

Around half of the genes that influence how well a child can read also play a role in their mathematics ability, say scientists from UCL, the University of Oxford and King’s College London who led a study into the genetic basis of cognitive traits.


While mathematics and reading ability are known to run in families, the complex system of genes affecting these traits is largely unknown. The finding deepens scientists’ understanding of how nature and nurture interact, highlighting the important role that a child’s learning environment may have on the development of reading and mathematics skills, and the complex, shared genetic basis of these cognitive traits.

 

The collaborative study, published today in Nature Communications as part of the Wellcome Trust Case-Control Consortium, used data from the Twins Early Development Study (TEDS) to analyse the influence of genetics on the reading and mathematics performance of 12-year-old children from nearly 2,800 British families.

 

Twins and unrelated children were tested for reading comprehension and fluency, and answered mathematics questions based on the UK national curriculum. The information collected from these tests was combined with DNA data, showing a substantial overlap in the genetic variants that influence mathematics and reading. 


Dr Chris Spencer (Oxford University), lead author said: “We’re moving into a world where analysing millions of DNA changes, in thousands of individuals, is a routine tool in helping scientists to understand aspects of human biology. This study used the technique to help investigate the overlap in the genetic component of reading and maths ability in children. Interestingly, the same method can be applied to pretty much any human trait, for example to identify new links between diseases and disorders, or the way in which people respond to treatments.”


Via Dr. Stefan Gruenwald
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On the Use of Human Mobility Proxies for Modeling Epidemics

On the Use of Human Mobility Proxies for Modeling Epidemics | Social Foraging | Scoop.it

Human mobility is a key component of large-scale spatial-transmission models of infectious diseases. Correctly modeling and quantifying human mobility is critical for improving epidemic control, but may be hindered by data incompleteness or unavailability. Here we explore the opportunity of using proxies for individual mobility to describe commuting flows and predict the diffusion of an influenza-like-illness epidemic. We consider three European countries and the corresponding commuting networks at different resolution scales, obtained from (i) official census surveys, (ii) proxy mobility data extracted from mobile phone call records, and (iii) the radiation model calibrated with census data. Metapopulation models defined on these countries and integrating the different mobility layers are compared in terms of epidemic observables. We show that commuting networks from mobile phone data capture the empirical commuting patterns well, accounting for more than 87% of the total fluxes. The distributions of commuting fluxes per link from mobile phones and census sources are similar and highly correlated, however a systematic overestimation of commuting traffic in the mobile phone data is observed. This leads to epidemics that spread faster than on census commuting networks, once the mobile phone commuting network is considered in the epidemic model, however preserving to a high degree the order of infection of newly affected locations. Proxies' calibration affects the arrival times' agreement across different models, and the observed topological and traffic discrepancies among mobility sources alter the resulting epidemic invasion patterns. Results also suggest that proxies perform differently in approximating commuting patterns for disease spread at different resolution scales, with the radiation model showing higher accuracy than mobile phone data when the seed is central in the network, the opposite being observed for peripheral locations. Proxies should therefore be chosen in light of the desired accuracy for the epidemic situation under study.

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The measurement of consciousness: a framework for the scientific study of consciousness

Scientists studying consciousness are attempting to identify correlations between measurements of consciousness and the physical world. Consciousness can only be measured through first-person reports, which raises problems about the accuracy of first-person reports, the possibility of non-reportable consciousness and the causal closure of the physical world. Many of these issues could be resolved by assuming that consciousness is entirely physical or functional. However, this would sacrifice the theory-neutrality that is a key attraction of a correlates-based approach to the study of consciousness. This paper puts forward a different solution that uses a framework of definitions and assumptions to explain how consciousness can be measured. This addresses the problems associated with first-person reports and avoids the issues with the causal closure of the physical world. This framework is compatible with most of the current theories of consciousness and it leads to a distinction between two types of correlates of consciousness.
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Earth's magnetic field is weakening 10 times faster than originally predicted, swarm satellites show

Earth's magnetic field is weakening 10 times faster than originally predicted, swarm satellites show | Social Foraging | Scoop.it

Earth's magnetic field, which protects the planet from huge blasts of deadly solar radiation, has been weakening over the past six months, according to data collected by a European Space Agency (ESA) satellite array called Swarm.

 

The biggest weak spots in the magnetic field — which extends 370,000 miles (600,000 kilometers) above the planet's surface — have sprung up over the Western Hemisphere, while the field has strengthened over areas like the southern Indian Ocean, according to the magnetometers onboard the Swarm satellites — three separate satellites floating in tandem.

 

The scientists who conducted the study are still unsure why the magnetic field is weakening, but one likely reason is that Earth's magnetic poles are getting ready to flip, said Rune Floberghagen, the ESA's Swarm mission manager. In fact, the data suggest magnetic north is moving toward Siberia.

 

In fact over the past 20 million years, our planet has settled into a pattern of a pole reversal about every 200,000 to 300,000 years; as of 2012, however, it has been more than twice that long since the last reversal. These reversals aren't split-second flips, and instead occur over hundreds or thousands of years. During this lengthy stint, the magnetic poles start to wander away from the region around the spin poles (the axis around which our planet spins), and eventually end up switched around, according to Cornell University astronomers.


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Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems

Artificial Life 14: Proceedings of the Fourteenth International Conference on the Synthesis and Simulation of Living Systems | Social Foraging | Scoop.it

ALIFE 14, the Fourteenth International Conference on the Synthesis and Simulation of Living Systems, presents the current state of the art of Artificial Life—the highly interdisciplinary research area on artificially constructed living systems, including mathematical, computational, robotic, and biochemical ones. The understanding and application of such generalized forms of life, or “life as it could be,” have been producing significant contributions to various fields of science and engineering.

 

This volume contains papers that were accepted through rigorous peer reviews for presentations at the ALIFE 14 conference. The topics covered in this volume include: Evolutionary Dynamics; Artificial Evolutionary Ecosystems; Robot and Agent Behavior; Soft Robotics and Morphologies; Collective Robotics; Collective Behaviors; Social Dynamics and Evolution; Boolean Networks, Neural Networks and Machine Learning; Artificial Chemistries, Cellular Automata and Self-Organizing Systems; In-Vitro and In-Vivo Systems; Evolutionary Art, Philosophy and Entertainment; and Methodologies.

  
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Free Download Link: http://mitpress.mit.edu/sites/default/files/titles/free_download/9780262326216_Artificial_Life_2014.pdf

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Unique in the Crowd: The privacy bounds of human mobility

Unique in the Crowd: The privacy bounds of human mobility | Social Foraging | Scoop.it
We study fifteen months of human mobility data for one and a half million individuals and find that human mobility traces are highly unique. In fact, in a dataset where the location of an individual is specified hourly, and with a spatial resolution equal to that given by the carrier's antennas, four spatio-temporal points are enough to uniquely identify 95% of the individuals. We coarsen the data spatially and temporally to find a formula for the uniqueness of human mobility traces given their resolution and the available outside information. This formula shows that the uniqueness of mobility traces decays approximately as the 1/10 power of their resolution. Hence, even coarse datasets provide little anonymity. These findings represent fundamental constraints to an individual's privacy and have important implications for the design of frameworks and institutions dedicated to protect the privacy of individuals.
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Studying Collective Human Decision Making and Creativity with Evolutionary Computation

We report a summary of our interdisciplinary research project "Evolutionary Perspective on Collective Decision Making" that was conducted through close collaboration between computational, organizational and social scientists at Binghamton University. We redefined collective human decision making and creativity as evolution of ecologies of ideas, where populations of ideas evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of ideas, each conducted by participating humans. Based on this evolutionary perspective, we generated hypotheses about collective human decision making using agent-based computer simulations. The hypotheses were then tested through several experiments with real human subjects. Throughout this project, we utilized evolutionary computation (EC) in non-traditional ways---(1) as a theoretical framework for reinterpreting the dynamics of idea generation and selection, (2) as a computational simulation model of collective human decision making processes, and (3) as a research tool for collecting high-resolution experimental data of actual collaborative design and decision making from human subjects. We believe our work demonstrates untapped potential of EC for interdisciplinary research involving human and social dynamics.
  
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Forget the Shortest Route Across a City; New Algorithm Finds the Most Beautiful

Forget the Shortest Route Across a City; New Algorithm Finds the Most Beautiful | Social Foraging | Scoop.it

The way we navigate in cities has been revolutionized in the last few years by the advent of GPS mapping programs. Enter your start and end location and these will give you the shortest route from A to B.


That’s usually the best bet when driving, but walking is a different matter. Often, pedestrians want the quietest route or the most beautiful but if they turn to a mapping application, they’ll get little help.


That could change now thanks to the work of Daniele Quercia at Yahoo Labs in Barcelona, Spain, and a couple of pals. These guys have worked out how to measure the “beauty” of specific locations within cities and then designed an algorithm that automatically chooses a route between two locations in a way that maximizes the beauty along it. “The goal of this work is to automatically suggest routes that are not only short but also emotionally pleasant,” they say.


Quercia and co begin by creating a database of images of various parts of the center of London taken from Google Street View and Geograph, both of which have reasonably consistent standards of images. They then crowdsourced opinions about the beauty of each location using a website called UrbanGems.org.


Each visitor to UrbanGems sees two photographs and chooses the one which shows the more beautiful location. That gives the team a crowdsourced opinion about the beauty of each location. They then plot each of these locations and their beauty score on a map which they use to provide directions.

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Twenty Years of Machine Learning at Microsoft

People may not realize it: Microsoft has more than twenty years of experience in creating machine learning systems and applying them to real problems. This experience is much longer than the recent buzz around Big Data and Deep Learning. It certainly gives us a good perspective on a variety of technologies and what it takes to actually deploy ML in production.

 

The story of ML at Microsoft started in 1992. We started working with Bayesian Networks, language modeling, and speech recognition. By 1993, Eric Horvitz, David Heckerman, and Jack Breese started the Decision Theory Group in Research and XD Huang started the Speech Recognition Group. In the 90s, we found that many problems, such as text categorization and email prioritization, were solvable through a combination of linear classification and Bayes networks. That work produced the first content-based spam detector and a number of other prototypes and products.

 

As we were working on solving specific problems for Microsoft products, we also wanted to get our tools directly into the hands of our customers. Making usable tools requires more than just clever algorithms: we need to consider the end-to-end user experience. We added predictive analytics to the Commerce Server product in order to provide recommendation service to our customers. We shipped the SQL Server Data Mining product in 2005, which allowed customers to build analytics on top of our SQL Server product.

 

As our algorithms became more sophisticated, we started solving tougher problems in fields related to ML, such as information retrieval, computer vision, and speech recognition. We blended the best ideas from ML and from these fields to make substantial forward progress. As I mentioned in my previous post, there are a number of such examples. Jamie Shotton, Antonio Criminisi, and others used decision forests to perform pixel-wise classification, both for human pose estimation and for medical imaging. Li Deng, Frank Seide, Dong Yu, and colleagues applied deep learning to speech recognition.

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Choices Can Become Overwhelming, So Make It Easier for Customers

Choices Can Become Overwhelming, So Make It Easier for Customers | Social Foraging | Scoop.it
Use these strategies to increase click-through rates and purchases when presenting choices.

 

Imagine the following scenario: All your friends have long been involved in relationships, and you are tired of being the third wheel at every social gathering. After a few failed matchmaking attempts, you decide to try a dating website. Soon you discover a new and exciting world that had previously been unknown to you. Suddenly you have many suitors and your dating possibilities become endless.

 

This experience makes you feel truly attractive and desired and, without even noticing, you become addicted. But make no mistake, you are not addicted to love or dating -- you are addicted to the idea of having many possibilities available to you.

 

The above scenario exemplifies a basic human trait: People love to have many options, even if they only exist in theory. When asked, who wouldn’t prefer to choose from a list of five different items over a list of only two?

 

Intuitively, people feel that the more options they have, the greater their chances are of finding the choice that will perfectly satisfy their needs. But this intuitive assumption turns out to be an illusion -- the more options we have, the less likely we are to make a decision at all.

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SupraPed Robots Will Use Trekking Poles to Hike Across Rough Terrain

SupraPed Robots Will Use Trekking Poles to Hike Across Rough Terrain | Social Foraging | Scoop.it

Last year at the Stanford-Berkeley Robotics Symposium, we saw some tantalizing slides from Oussama Khatib about a humanoid robot that used trekking poles to balance itself. We were promised more details later, and the Stanford researchers delivered at the IEEE International Conference on Robotics and Automation (ICRA) this year, where they presented the concept of SupraPed robots.

 

The idea is equipping robots with a pair of special trekking poles packed with sensors that, according to the researchers, "transforms biped humanoids into tripeds or quadrupeds or more generally, SupraPeds." By using these smart poles to steady themselves, the robots would be able to navigate through "cluttered and unstructured environments such as disaster sites."

 

Humans have had a lot of practice walking around on two legs. Robots have not, which isn't their fault, but at the moment, even the best robots are working up to the level of a toddler. Some of them aren't bad at flat terrain, but as we saw in the DARPA Robotics Challenge Trials, varied terrain is very, very difficult. It doesn't just require the physical ability to move and balance, but also the awareness to know what path to take and where feet should be placed.

 

As good at this as humans are, even we get into situations where our balance and movements with our legs and feet simply aren't enough. And when this happens, we scramble. If we're fancy, we might use a walking stick or hiking poles for balance assistance, and if we're not fancy, sometimes an outstretched arm is enough.

 

Similar to the research we looked at yesterday, this is an entirely different philosophy about obstacles: instead of things to be avoided, they're things that can potentially be used to complete tasks that would otherwise be unsafe or impossible.

 

However, this is all simulation, and the programming behind it is fairly complex. The robot (when they throw a real robot into this mix) will have sophisticated 3D vision, tactile sensing, and a special set of actuated ski poles. The SupraPed platform includes a pair smart walking staffs, a whole-body multi-contact control and planning software system, and real-time reactive controllers that integrate both tactile and visual information. Moreover, to bypass the difficulty of programming fully autonomous robot controllers, the SupraPed platform contains a remote haptic teleoperation system which allows the operator remotely give high level command.


Via Dr. Stefan Gruenwald
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Your Brain Is On the Brink of Chaos

Your Brain Is On the Brink of Chaos | Social Foraging | Scoop.it

In one important way, the recipient of a heart transplant ignores its new organ: Its nervous system usually doesn’t rewire to communicate with it. The 40,000 neurons controlling a heart operate so perfectly, and are so self-contained, that a heart can be cut out of one body, placed into another, and continue to function perfectly, even in the absence of external control, for a decade or more. This seems necessary: The parts of our nervous system managing our most essential functions behave like a Swiss watch, precisely timed and impervious to perturbations. Chaotic behavior has been throttled out.

Or has it? Two simple pendulums that swing with perfect regularity can, when yoked together, move in a chaotic trajectory. Given that the billions of neurons in our brain are each like a pendulum, oscillating back and forth between resting and firing, and connected to 10,000 other neurons, isn’t chaos in our nervous system unavoidable?

 

http://nautil.us/issue/15/turbulence/your-brain-is-on-the-brink-of-chaos


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Complexity at the social science interface

This article introduces a special issue of Complexity dedicated to the increasingly important element of complexity science that engages with social policy. We introduce and frame an emerging research agenda that seeks to enhance social policy by working at the interface between the social sciences and the physical sciences (including mathematics and computer science), and term this research area the “social science interface” by analogy with research at the life sciences interface. We locate and exemplify the contribution of complexity science at this new interface before summarizing the contributions collected in this special issue and identifying some common themes that run through them.

 

Complexity at the social science interface
Nigel Gilbert and Seth Bullock

Complexity
Volume 19, Issue 6, pages 1–4, July/August 2014

http://dx.doi.org/10.1002/cplx.21550

 

Special Issue on Complexity Science and Social Policy

http://onlinelibrary.wiley.com/doi/10.1002/cplx.v19.6/issuetoc


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The five most impressive innovations of the World Cup in Brazil

The five most impressive innovations of the World Cup in Brazil | Social Foraging | Scoop.it

1 – A pioneering system to predict the risk of epidemics in large events

 

2 – The exoskeleton that is controlled by the mind

 

3 – Medicine 2.0

 

4 – Ultra High Definition

 

5 – The end of ghost/phantom goals

 

 

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Hypnosis: The day my mind was 'possessed'

Hypnosis: The day my mind was 'possessed' | Social Foraging | Scoop.it
Scientists are using hypnosis to make healthy people delusional. To find out why, David Robson discovered what it’s like to lose control of his mind

 

 am lying on my back and trapped in a gleaming white tunnel, the surface barely six inches from my nose. There is a strange mechanical rumbling in the background, and I hear footsteps padding around the room beyond. In my mounting claustrophobia, I ask myself why I am here – but there is no way out now. A few moments later, the light dims, and as the man speaks, my thoughts begin to fade.

 

“The engineer has developed a way of taking control of your thoughts from the inside. He does this because he is fascinated by mind control, and wants to apply the most direct method of controlling your thoughts. He is doing this to advance his research into mind control. You will soon be aware of the engineer inserting his thoughts.”

A strange serenity descends as I realise that soon, my will won’t be my own. Then the experiment begins. I am about to be possessed.

 

The man who will soon take control of my thoughts is Eamonn Walsh, a psychologist who uses hypnosis to investigate psychoses at the Institute of Psychiatry in London. The idea is to turn healthy subjects into ‘virtual patients’ suffering full-on delusions, such as being possessed by a paranormal entity, allowing the scientists to understand the underlying illness in a new way, and potentially find treatments

 

The scientists are understandably keen to distance themselves from stage hypnotists. “It’s not flaky, it’s not for entertainment – we’ve got carefully specific research goals,” says Mitul Mehta, who collaborates with Walsh on these studies. It’s a bold idea, but can it possibly work? And what does it feel like to lose total control of your mind?

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LEVAN: Learning Everything about Anything

LEVAN: Learning Everything about Anything | Social Foraging | Scoop.it

Ever wondered what that chair you saw at your friend’s place was called? Ever wanted to know all about dance, and looked for its compact visual summary?


A team of scientists from the University of Washington and the Allen Institute for Artificial Intelligence is developing a program that teaches itself all there is to know about a concept and presents the findings in pictures and phrases. The program is called Learning Everything About Anything, or LEVAN."

Try LEVAN! It is a fully-automated system that learns everything visual about any concept, by processing lots of books and images on the web. It acts as a visual encyclopedia for you, helping you explore and understand any topic that you are curious about, in great detail.

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US military studied how to influence Twitter users in Darpa-funded research

US military studied how to influence Twitter users in Darpa-funded research | Social Foraging | Scoop.it

The activities of users of Twitter and other social media services were recorded and analysed as part of a major project funded by the US military, in a program that covers ground similar to Facebook’s controversial experiment into how to control emotions by manipulating news feeds.

 

Research funded directly or indirectly by the US Department of Defense’s military research department, known as Darpa, has involved users of some of the internet’s largest destinations, including Facebook, Twitter, Pinterest and Kickstarter, for studies of social connections and how messages spread.

 

While some elements of the multi-million dollar project might raise a wry smile – research has included analysis of the tweets of celebrities such as Lady Gaga and Justin Bieber, in an attempt to understand influence on Twitter – others have resulted in the buildup of massive datasets of tweets and additional types social media posts.

Several of the DoD-funded studies went further than merely monitoring what users were communicating on their own, instead messaging unwitting participants in order to track and study how they responded.

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Coling 2014 - 25th International Conference on Computational Linguistics

Coling 2014 - 25th International Conference on Computational Linguistics | Social Foraging | Scoop.it

The COLING conference, organised under the auspices of the International Committee on Computational Linguistics (ICCL), has a history that dates back to the 1960s. The conference is held every two years and regularly attracts more than 700 delegates. The 1st conference was held in New York, 1965. Since then, the conference has developed into one of the premier Natural Language Processing conferences world-wide held every two years. The last five conferences were held in Geneva (COLING 2004), Sydney (COLING - ACL 2006), Manchester (COLING 2008), Beijing (COLING 2010) and Mumbai (COLING 2012).

 

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The Role of the Internet of Things in Network Resilience

Disasters lead to devastating structural damage not only to buildings and transport infrastructure, but also to other critical infrastructure, such as the power grid and communication backbones. Following such an event, the availability of minimal communication services is however crucial to allow efficient and coordinated disaster response, to enable timely public information, or to provide individuals in need with a default mechanism to post emergency messages. The Internet of Things consists in the massive deployment of heterogeneous devices, most of which battery-powered, and interconnected via wireless network interfaces. Typical IoT communication architectures enables such IoT devices to not only connect to the communication backbone (i.e. the Internet) using an infrastructure-based wireless network paradigm, but also to communicate with one another autonomously, without the help of any infrastructure, using a spontaneous wireless network paradigm. In this paper, we argue that the vast deployment of IoT-enabled devices could bring benefits in terms of data network resilience in face of disaster. Leveraging their spontaneous wireless networking capabilities, IoT devices could enable minimal communication services (e.g. emergency micro-message delivery) while the conventional communication infrastructure is out of service. We identify the main challenges that must be addressed in order to realize this potential in practice. These challenges concern various technical aspects, including physical connectivity requirements, network protocol stack enhancements, data traffic prioritization schemes, as well as social and political aspects.
  
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Using an Artificial Financial Market for studying a Cryptocurrency Market

This paper presents an agent-based artificial cryptocurrency market in which heterogeneous agents buy or sell cryptocurrencies, in particular Bitcoins. In this market, there are two typologies of agents, Random Traders and Chartists, which interact with each other by trading Bitcoins. Each agent is initially endowed with a finite amount of crypto and/or fiat cash and issues buy and sell orders, according to her strategy and resources. The number of Bitcoins increases over time with a rate proportional to the real one, even if the mining process is not explicitly modelled. 


The model proposed is able to reproduce some of the real statistical properties of the price absolute returns observed in the Bitcoin real market. In particular, it is able to reproduce the autocorrelation of the absolute returns, and their cumulative distribution function. The simulator has been implemented using object-oriented technology, and could be considered a valid starting point to study and analyse the cryptocurrency market and its future evolutions.

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Steven Pinker Uses Theories from Evolutionary Biology to Explain Why Academic Writing is So Bad

Steven Pinker Uses Theories from Evolutionary Biology to Explain Why Academic Writing is So Bad | Social Foraging | Scoop.it

I don’t know about other disciplines, but academic writing in the humanities has become notorious for its jargon-laden wordiness, tangled constructions, and seemingly deliberate vagary and obscurity. A popular demonstration of this comes via the University of Chicago’s academic sentence generator, which allows one to plug in a number of stock phrases, verbs, and “-tion” words to produce corkers like “The reification of post-capitalist hegemony is always already participating in the engendering of print culture” or “The discourse of the gaze gestures toward the linguistic construction of the gendered body”—the point, of course, being that the language of academia has become so meaningless that randomly generated sentences closely resemble and make as much sense as those pulled from the average journal article (a point well made by the so-called “Sokal hoax”).

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Google Tests Personal Data Market To Find Out How Much Your Personal Information Is Worth

Google Tests Personal Data Market To Find Out How Much Your Personal Information Is Worth | Social Foraging | Scoop.it

The personal information that your smart phone can collect about you is increasingly detailed. Apps can record your location, your level of exercise, the phone calls that you make and receive, the photographs that you take and who you share them with and so on. Various studies have shown that this data provides a detailed and comprehensive insight into an individual’s habits and lifestyle, information that advertisers and marketers dearly love to have.

 

Indeed, this information can surprisingly useful. The Google Now smartphone app uses information such as your location to provide details it thinks you might find useful, such as directions home or nearby restaurants.

 

But this service isn’t entirely altruistic. Google knows perfectly well that it can use this information to sell adverts and other services.

 

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