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Scientists Propose a New Architecture for Sustainable Development

Scientists Propose a New Architecture for Sustainable Development | Social Foraging | Scoop.it

As a United Nations working group negotiates a set of “sustainable development goals,” 10 scientists and development analysts, in a commentary published today in Nature, have proposed a fundamentally different way to frame this concept. (Click here for relevant Dot Earth posts.)

 

Over the last several decades, sustainable human development has been conceived largely as the outcome of balanced work onthree “pillars” — economic and social development and environmental protection. The authors, building on arguments that have been brewing for awhile, say that these concepts are instead nested one inside the next, not separate free-standing realms.

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The #Neuroscience of Social #Influence | Beautiful Minds

The #Neuroscience of Social #Influence | Beautiful Minds | Social Foraging | Scoop.it
Before I wrote this article, I went through two stages. In the first stage, I cruised the academic journals for interesting papers. Once I found a ...

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luiy's curator insight, December 18, 9:32 AM

Can the pattern of neurons firing in my brain predict how much this article will be retweeted on twitter?

 

A recent study conducted by Emily Falk, Matthew Lieberman, and colleagues gets us closer to answering these important questions. The researchers recruited undergraduate participants and randomly assigned them to two groups: the “interns” and the “producers.” The 20 interns were asked to view ideas for television pilots and provide recommendations to the 79 producers about which shows should be considered for further development and production. All of the interns had their brains scanned by fMRI while they viewed the videos, and they were then videotaped while they discussed the merits of each pilot show idea. The producers rated which ideas they would like to further recommend. How was neural activity related to the spread of ideas?

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Computer-based "deep neural network" as good as primates at visual object recognition

Computer-based "deep neural network" as good as primates at visual object recognition | Social Foraging | Scoop.it

Computers aren't best suited to visual object recognition. Our brains are hardwired to quickly see and match patterns in everything, with great leaps of intuition, while the processing center of a computer is more akin to a very powerful calculator. But that hasn't stopped neuroscientists and computer scientists from trying over the past 40 years to design computer networks that mimic our visual skills. Recent advances in computing power and deep learning algorithms have accelerated that process to the point where a group of MIT neuroscientists has found a network design that compares favorably to the brain of our primate cousins.


This is important beyond the needs of automated digital information processing like Google's image search. Computer-based neural networks that work like the human brain will further our understanding of how the brain works, and any attempts to create them will test that understanding. Essentially, the fact that these networks work to a level comparable to primates suggests that neuroscientists now have a solid grasp of how object recognition works in the brain.


To see how current networks hold up, the MIT scientists started by testing primates. They implanted arrays of electrodes in the inferior temporal (IT) cortex and area V4 (a part of the visual system that feeds into the IT cortex) of the primates' brains. This allowed them to see how neurons related to object recognition responded when the animals looked at various objects in 1,960 images. The viewing time per image was a mere 100 milliseconds, which is long enough for humans to recognize an object.


They then compared these results with those of the latest deep neural networks. These networks produce arrays of numbers when fed an image – different numbers for different images. If it groups similar objects into similar clusters in this number matrix representation, it's deemed accurate. "Through each of these computational transformations, through each of these layers of networks, certain objects or images get closer together, while others get further apart," explains lead author Charles Cadieu.


The best network, developed by researchers at New York University, classified objects as well as the macaque - a medium-sized Old World monkey - brain. That's the good news. The bad is that they don't know why. Neural networks are learning from massive datasets containing millions or billions of images, churning through the information with help from the high-performance graphical processing units that power the latest video games. But nobody knows quite what is going on in there as the networks refine their own algorithms.

 



Via Dr. Stefan Gruenwald
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Deep-learning AI algorithm shines new light on mutations in once obscure areas of the genome

Deep-learning AI algorithm shines new light on mutations in once obscure areas of the genome | Social Foraging | Scoop.it

The so-called “streetlight effect” has often fettered scientists who study complex hereditary diseases. The term refers to an old joke about a drunk searching for his lost keys under a streetlight. A cop asks, "Are you sure this is where you lost them?" The drunk says, "No, I lost them in the park, but the light is better here."

 

For researchers who study the genetic roots of human diseases, most of the light has shone down on the 2 percent of the human genome that includes protein-coding DNA sequences. “That’s fine. Lots of diseases are caused by mutations there, but those mutations are low-hanging fruit,” says University of Toronto (U.T.) professor Brendan Frey who studies genetic networks. “They’re easy to find because the mutation actually changes one amino acid to another one, and that very much changes the protein.”

 

The trouble is, many disease-related mutations also happen in noncoding regions of the genome—the parts that do not directly make proteins but that still regulate how genes behave. Scientists have long been aware of how valuable it would be to analyze the other 98 percent but there has not been a practical way to do it.

 

Now Frey has developed a “deep-learning” machine algorithm that effectively shines a light on the entire genome. A paper appearing December 18 in Science describes how this algorithm can identify patterns of mutation across coding and noncoding DNA alike. The algorithm can also predict how likely each variant is to contribute to a given disease. “Our method works very differently from existing methods,” says Frey, the study’s lead author. “GWAS-, QTL- and ENCODE-type approaches can't figure out causal relationships. They can only correlate. Our system can predict whether or not a mutation will cause a change in RNA splicing that could lead to a disease phenotype.”

 

RNA splicing is one of the major steps in turning genetic blueprints into living organisms. Splicing determines which bits of DNA code get included in the messenger-RNA strings that build proteins. Different configurations yield different proteins. Misregulated splicing contributes to an estimated 15 to 60 percent of human genetic diseases.

 

The combination of whole-genome analysis and predictive models for RNA splicing makes Frey’s method a major contribution to the field, according to Stephan Sanders, an assistant professor at the University of California, San Francisco, School of Medicine. “I’m looking forward to using this tool in larger data sets and really getting sense of how important splicing is,” he says. Sanders, who researches the genetic causes of diseases, notes Frey’s approach complements, rather than replaces, other methods of genetic analysis. “I think any genomist [sic] would agree that noncoding [areas of the genome] are hugely important. This method is a really novel way of getting at that,” he says.


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The Dominant Life Form in the Cosmos Is Probably Superintelligent Robots

The Dominant Life Form in the Cosmos Is Probably Superintelligent Robots | Social Foraging | Scoop.it
If and when we finally encounter aliens, they probably won’t look like little green men, or spiny insectoids. It’s likely they won’t be biological creatures at all, but rather, advanced robots that outstrip our intelligence in every conceivable way. While scores of philosophers, scientists and futurists have prophesied the rise of artificial intelligence and the impending singularity, most have restricted their predictions to Earth. Fewer thinkers—outside the realm of science fiction, that is—have considered the notion that artificial intelligence is already out there, and has been for eons.

Susan Schneider, a professor of philosophy at the University of Connecticut, is one who has. She joins a handful of astronomers, including Seth Shostak, director of NASA’s Search for Extraterrestrial Intelligence, or SETI, program, NASA Astrobiologist Paul Davies, and Library of Congress Chair in Astrobiology Stephen Dick in espousing the view that the dominant intelligence in the cosmos is probably artificial. In her paper “Alien Minds," written for a forthcoming NASA publication, Schneider describes why alien life forms are likely to be synthetic, and how such creatures might think.
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Cost Conscious? The Neural and Behavioral Impact of Price Primacy on Decision-Making

Cost Conscious? The Neural and Behavioral Impact of Price Primacy on Decision-Making | Social Foraging | Scoop.it
Price is a key factor in most purchases, but can be presented at different stages of decision-making prior to a purchase. We examine the sequence-dependent effects of price and product information on the decision-making process at both neural and behavioral levels. During functional magnetic resonance imaging, the price of a product was shown to participants either before or after the product itself was presented. Early exposure to price, or price primacy altered the process of valuation, as seen via altered patterns of activity in medial prefrontal cortex immediately prior to purchase decisions. Specifically, while viewing products first resulted in evaluations strongly related to products' attractiveness or desirability, viewing prices first appeared to promote overall evaluations related to products' monetary worth. Consistent with this framework, we show that price primacy can increase purchase of bargain priced products when their worth is easily recognized. Together, these results suggest that price primacy highlights considerations of product worth, and can thereby influence purchasing.
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What time is this colour...or what colour is this time? Lets find out?

What time is this colour...or what colour is this time? Lets find out? | Social Foraging | Scoop.it
the time....... now in colour.
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Fish'n'microchips: MIT's soft robotic fish swims 'like the real thing'

Fish'n'microchips: MIT's soft robotic fish swims 'like the real thing' | Social Foraging | Scoop.it
The Massachusetts Institute of Technology (MIT) has unveiled a robot fish that it claims can change direction almost as fast as the real thing.

The fish – or “autonomous soft robot” as it’s described by MIT – can perform escape manoeuvres through rapid convulsions of its body, powered by carbon dioxide released from a canister in its abdomen.

Graduate student Andrew Marchese built the fish using 3D printing technology to create a mould, which was then used to cast the fish’s body from silicone rubber. The fish can execute between 20 or 30 manoeuvres before running out of gas.

“The fish was designed to explore performance capabilities, not long-term operation,” said Marchese in MIT’s announcement of the research. “Next steps for future research are taking that system and building something that’s compromised on performance a little bit but increases longevity.”

That will involve switching carbon dioxide for a pumped-water system that could keep the fish swimming for around 30 minutes of a time. Such a device could eventually be used to swim alongside schools of real fish to study their behaviour in the wild.
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Links that speak: The global language network and its association with global fame

Links that speak: The global language network and its association with global fame | Social Foraging | Scoop.it
Languages vary enormously in global importance because of historical, demographic, political, and technological forces. However, beyond simple measures of population and economic power, there has been no rigorous quantitative way to define the global influence of languages. Here we use the structure of the networks connecting multilingual speakers and translated texts, as expressed in book translations, multiple language editions of Wikipedia, and Twitter, to provide a concept of language importance that goes beyond simple economic or demographic measures. We find that the structure of these three global language networks (GLNs) is centered on English as a global hub and around a handful of intermediate hub languages, which include Spanish, German, French, Russian, Portuguese, and Chinese. We validate the measure of a language’s centrality in the three GLNs by showing that it exhibits a strong correlation with two independent measures of the number of famous people born in the countries associated with that language. These results suggest that the position of a language in the GLN contributes to the visibility of its speakers and the global popularity of the cultural content they produce.
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Worm 'Brain' Uploaded Into Lego Robot

Worm 'Brain' Uploaded Into Lego Robot | Social Foraging | Scoop.it
Can a digitally simulated brain on a computer perform tasks just like the real thing?

For simple commands, the answer, it would seem, is yes it can. Researchers at the OpenWorm project recently hooked a simulated worm brain to a wheeled robot. Without being explicitly programmed to do so, the robot moved back and forth and avoided objects—driven only by the interplay of external stimuli and digital neurons.
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The retail market as a complex system

Aim of this paper is to introduce the complex system perspective into retail market analysis. Currently, to understand the retail market means to search for local patterns at the micro level, involving the segmentation, separation and profiling of diverse groups of consumers. In other contexts, however, markets are modelled as complex systems. Such strategy is able to uncover emerging regularities and patterns that make markets more predictable, e.g. enabling to predict how much a country’s GDP will grow. Rather than isolate actors in homogeneous groups, this strategy requires to consider the system as a whole, as the emerging pattern can be detected only as a result of the interaction between its self-organizing parts. This assumption holds also in the retail market: each customer can be seen as an independent unit maximizing its own utility function. As a consequence, the global behaviour of the retail market naturally emerges, enabling a novel description of its properties, complementary to the local pattern approach. Such task demands for a data-driven empirical framework. In this paper, we analyse a unique transaction database, recording the micro-purchases of a million customers observed for several years in the stores of a national supermarket chain. We show the emergence of the fundamental pattern of this complex system, connecting the products’ volumes of sales with the customers’ volumes of purchases. This pattern has a number of applications. We provide three of them. By enabling us to evaluate the sophistication of needs that a customer has and a product satisfies, this pattern has been applied to the task of uncovering the hierarchy of needs of the customers, providing a hint about what is the next product a customer could be interested in buying and predicting in which shop she is likely to go to buy it.

 

The retail market as a complex system
Pennacchioli D, Coscia M, Rinzivillo S, Giannotti F, Pedreschi D
EPJ Data Science 2014, 3 :33 (11 December 2014)

http://dx.doi.org/10.1140/epjds/s13688-014-0033-x ;


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Eli Levine's curator insight, December 13, 10:43 PM

With this knowledge and insight into the world that's dawning.  One world must be destroyed in order for the new world to come forth; one segment of society must give way to the new in order to facilitate and ease this transition.

 

The top of society is where and what determines the ease or the possibility of this transition.  That is the point that needs to be altered in order to bring about this new dawn.  

 

Unfortunately, it is the point in society that is least willing, although most capable of change.  They'll cling to illusions and delusions of relative power over people and unsustainable material wealth than allow for everyone, including themselves, to realize something that could truly be wonderful for our lives, our well-being, and our health as living organisms.  This is before we talk about the bottom-up resistance that will be experienced as well, especially if the transition is done badly by the people who are at the top of the given social unit.  A shame that something so relatively simple can be so completely complicated and complex to carry out.

 

Silly world.

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Flavour network and the principles of food pairing : Data Science in Gastronomy

Flavour network and the principles of food pairing : Data Science in Gastronomy | Social Foraging | Scoop.it
The cultural diversity of culinary practice, as illustrated by the variety of regional cuisines, raises the question of whether there are any general patterns that determine the ingredient combinations used in food today or principles that transcend individual tastes and recipes. We introduce a flavor network that captures the flavor compounds shared by culinary ingredients. Western cuisines show a tendency to use ingredient pairs that share many flavor compounds, supporting the so-called food pairing hypothesis. By contrast, East Asian cuisines tend to avoid compound sharing ingredients. Given the increasing availability of information on food preparation, our data-driven investigation opens new avenues towards a systematic understanding of culinary practice.
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Brain–machine interface for eye movements

Brain–machine interface for eye movements | Social Foraging | Scoop.it
A number of studies in tetraplegic humans and healthy nonhuman primates (NHPs) have shown that neuronal activity from reach-related cortical areas can be used to predict reach intentions using brain–machine interfaces (BMIs) and therefore assist tetraplegic patients by controlling external devices (e.g., robotic limbs and computer cursors). However, to our knowledge, there have been no studies that have applied BMIs to eye movement areas to decode intended eye movements. In this study, we recorded the activity from populations of neurons from the lateral intraparietal area (LIP), a cortical node in the NHP saccade system. Eye movement plans were predicted in real time using Bayesian inference from small ensembles of LIP neurons without the animal making an eye movement. Learning, defined as an increase in the prediction accuracy, occurred at the level of neuronal ensembles, particularly for difficult predictions. Population learning had two components: an update of the parameters of the BMI based on its history and a change in the responses of individual neurons. These results provide strong evidence that the responses of neuronal ensembles can be shaped with respect to a cost function, here the prediction accuracy of the BMI. Furthermore, eye movement plans could be decoded without the animals emitting any actual eye movements and could be used to control the position of a cursor on a computer screen. These findings show that BMIs for eye movements are promising aids for assisting paralyzed patients.
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Humans 2.0: Seeing Ourselves Anew in ‘Algorithmic Cascades of Data’

Humans 2.0: Seeing Ourselves Anew in ‘Algorithmic Cascades of Data’ | Social Foraging | Scoop.it
Sensors are cheap and abundant. They’re already in our devices, and soon enough, many of us may elect to carry sensors in and on our bodies, and embed them in our homes, offices, and cities. This terrifies people, Jason Silva says in a new video.

Who hasn’t heard of Big Brother or feared the rise of the surveillance state? But Silva says there’s an upside.

As the world is reduced to “algorithmic cascades of data” he thinks we’ll get what Steven Johnson calls the “long view,” like a microscope or telescope for previously invisible information and datasets.

Billions of sensors measuring location, motion, orientation, pressure, temperature, vital signs and more—each of these will be like a pixel. Seen up close, a modestly flashing primary color. But at a distance, individual pixels dissolve. Discrete points will smooth out into a contiguous image no one could have guessed by looking at looking at each pixel alone.

Exactly what image will our sensors reveal?

Silva thinks it will be like looking in a mirror and, seeing ourselves individually and collectively for the first time, will spark a feedback loop—one in which information leads to new behaviors, new behaviors to new information.

“Who knows what we might learn about ourselves?” Silva asks. “How might we be able to take that data and use those insights to feed them back into us?
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Swarms of Bees Inspired this Energy-saving Innovation

Swarms of Bees Inspired this Energy-saving Innovation | Social Foraging | Scoop.it

"When it comes to managing a building’s cooling and heating costs, just look up. It turns out there’s a lot to be learned from the birds and the bees, according to Toronto-based REGEN Energy. The clean tech firm co-ordinates HVAC (heating, ventilating and air-conditioning) systems by tapping into the ability of insect colonies and flocks of birds to display a greater collective intelligence."


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Eben Lenderking's curator insight, Today, 4:44 AM
En Ingles...Another reason to love bees and what the natural world can teach us...
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Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence

Socilab - LinkedIn Social Network Visualization, Analysis, and Education | #SNA #influence | Social Foraging | Scoop.it

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luiy's curator insight, December 23, 4:40 AM

Socilab is a free tool that allows users to visualize, analyze, and download data on their LinkedIn network. It works with the LinkedIn API to a) calculate structural hole metrics such as network density, hierarchy and constraint - and displays your percentile compared to other users of the tool, b) display a dynamic/interactive visualization of your ego network with node coloring by industry and an option to enable/disable connections to self using D3.js, and c) produce a CSV adjacency matrix or Pajek edgelist for download and import into your favorite SNA package. Users might find it useful for class tutorials and/or quickly and cheaply fielding crude network surveys. Former users of the now deprecated LinkedIn inMaps may find this to be a useful alternative.

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In the Face of Stress, Flies Unite

In the Face of Stress, Flies Unite | Social Foraging | Scoop.it
Fruit flies respond more effectively to danger when in a group. A research team from EPFL and UNIL discovered this behavior as well as the neural circuits which relay this information, opening a new field of research. An article on the findings is being published today in Nature.
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One Small Experiment in Learning and Memory, One Giant Leap for Mankind

One Small Experiment in Learning and Memory, One Giant Leap for Mankind | Social Foraging | Scoop.it
Benjamin Storm, a psychologist at the University of California, Santa Cruz , recently ran an interesting experiment in memory and technology.

Storm took twenty college students and gave them a pair of computer files, call them A and B, each containing a list of simple nouns. The students were given twenty seconds to review the nouns in File A. Afterward, half were instructed to save and close the file, while with the others, the saving step was omitted. They were simply told to close the file.

When this first task was complete, the students got another twenty seconds to study the nouns on File B and, immediately afterward, were given a memory test for those words. But that part was distraction—literally—as the real experiment is what happened next.

Next, the students were tested on their recall for the nouns in File A—meaning this wasn’t a test of short term memory, it was a test of longer term learning. The results were conclusive: the students who saved and closed the file—as opposed to those who just closed it—had far better recall than the others.

A little bizarre, right—that simply the act of clicking a mouse (i.e. “saving a file”) boosted our ability to recall the information later.
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Neuromarketing: Brain Scans Shed Light On How To Sell Bargains

Neuromarketing: Brain Scans Shed Light On How To Sell Bargains | Social Foraging | Scoop.it
Think of the last time you went shopping.

By the time you decided to buy a product, you knew both what you were buying and how much it cost. But was your decision affected by whether you saw the price or the product first? That’s the question at the heart of new experimental research that uses neuroscience tools to shed light on how our brains make purchasing decisions.

“We were interested in whether considering the price first changed how people thought about the decision process, and whether it changed the way the brain coded the value of a product,” says Uma R. Karmarkar, a neuroscientist and assistant professor in the Marketing unit at Harvard Business School, who conducted the research with Baba Shiv, a marketing professor and neuroeconomics expert at Stanford University’s Graduate School of Business, and Brian Knutson, an associate professor of psychology and neuroscience at Stanford. “Because we had neuroscience tools at our disposal, we had the benefit of exploring both those questions,” Karmarkar says.

The researchers found that price primacy (viewing the price first) makes consumers more likely to focus on whether a product is worth its price, and consequently can help induce the purchase of specific kinds of bargain-priced items. Their study, Cost Conscious? The Neural and Behavioral Impact of Price Primacy on Decision-Making, will appear in a forthcoming issue of the Journal of Marketing Research.
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Data Driven vs Data Informed: Sydney siege brings Uber criticism (Algorithms going wrong)

Data Driven vs Data Informed: Sydney siege brings Uber criticism (Algorithms going wrong) | Social Foraging | Scoop.it

Cab-ordering firm Uber has been criticised for increasing fares by up to four times normal rates during the hostage crisis in Sydney, Australia.

 

As the police cordoned off a wide area around the Lindt cafe where a gunman was holding staff and customers hostage, Uber's pricing algorithm raised prices as demand spiked.

 

Uber responded to the criticism by offering free journeys out of the city's central business district (CBD).

It also refunded some passengers.

 

But the rapidly expanding cab firm also tweeted that higher rates were still in place "to encourage drivers to get into the CBD".

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Want to influence the world? Map reveals the best languages to speak

Want to influence the world? Map reveals the best languages to speak | Social Foraging | Scoop.it

Speak or write in English, and the world will hear you. Speak or write in Tamil or Portuguese, and you may have a harder time getting your message out. Now, a new method for mapping how information flows around the globe identifies the best languages to spread your ideas far and wide. One hint: If you’re considering a second language, try Spanish instead of Chinese.

 

The study was spurred by a conversation about an untranslated book, says Shahar Ronen, a Microsoft program manager whose Massachusetts Institute of Technology (MIT) master’s thesis formed the basis of the new work. A bilingual Hebrew-English speaker from Israel, he told his MIT adviser, César Hidalgo (himself a Spanish-English speaker), about a book written in Hebrew whose translation into English he wasn’t yet aware of. “I was able to bridge a certain culture gap because I was multilingual,” Ronen says. He began thinking about how to create worldwide maps of how multilingual people transmit information and ideas.

 

Ronen and co-authors from MIT, Harvard University, Northeastern University, and Aix-Marseille University tackled the problem by describing three global language networks based on bilingual tweeters, book translations, and multilingual Wikipedia edits. The book translation network maps how many books are translated into other languages. For example, the Hebrew book, translated from Hebrew into English and German, would be represented in lines pointing from a node of Hebrew to nodes of English and German. That network is based on 2.2 million translations of printed books published in more than 1000 languages. As in all of the networks, the thickness of the lines represents the number of connections between nodes. For tweets, the researchers used 550 million tweets by 17 million users in 73 languages. In that network, if a user tweets in, say, Hindi as well as in English, the two languages are connected. To build the Wikipedia network, the researchers tracked edits in up to five languages done by editors, carefully excluding bots.

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OpenWorm: Building the first digital life form. Open source

OpenWorm is an open source project dedicated to creating a virtual C. elegans nematode in a computer.
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Is Consciousness Computable? Quantifying Integrated Information Using Algorithmic Information Theory

In this article we review Tononi's (2008) theory of consciousness as integrated information. We argue that previous formalizations of integrated information (e.g. Griffith, 2014) depend on information loss. Since lossy integration would necessitate continuous damage to existing memories, we propose it is more natural to frame consciousness as a lossless integrative process and provide a formalization of this idea using algorithmic information theory. We prove that complete lossless integration requires noncomputable functions. This result implies that if unitary consciousness exists, it cannot be modelled computationally.
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Mathematical models for emerging disease

It has been nearly 25 years since the publication of Infectious Disease of Humans, the “vade mecum” of mathematical modeling of infectious disease; the proliferation of epidemiological careers that it initiated is now in its fourth generation. Epidemiological models have proved very powerful in shaping health policy discussions. The complex interactions that lead to pathogen (and pest) outbreaks make it necessary to use models to provide quantitative insights into the counterintuitive outcomes that are the rule of most nonlinear systems. Thus, epidemic models are most interesting when they suggest unexpected outcomes; they are most powerful when they describe the conditions that delineate the worst-case unexpected scenario, and provide a framework in which to compare alternative control strategies. But what are the limits of mathematical models and what kinds provide insight into emerging disease?

 

Mathematical models for emerging disease
Andy Dobson

Science 12 December 2014:
Vol. 346 no. 6215 pp. 1294-1295
http://dx.doi.org/10.1126/science.aaa3441


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Collapse of cooperation in evolving games

Collapse of cooperation in evolving games | Social Foraging | Scoop.it
Game theory provides a quantitative framework for analyzing the behavior of rational agents. The Iterated Prisoner’s Dilemma in particular has become a standard model for studying cooperation and cheating, with cooperation often emerging as a robust outcome in evolving populations. Here we extend evolutionary game theory by allowing players’ payoffs as well as their strategies to evolve in response to selection on heritable mutations. In nature, many organisms engage in mutually beneficial interactions and individuals may seek to change the ratio of risk to reward for cooperation by altering the resources they commit to cooperative interactions. To study this, we construct a general framework for the coevolution of strategies and payoffs in arbitrary iterated games. We show that, when there is a tradeoff between the benefits and costs of cooperation, coevolution often leads to a dramatic loss of cooperation in the Iterated Prisoner’s Dilemma. The collapse of cooperation is so extreme that the average payoff in a population can decline even as the potential reward for mutual cooperation increases. Depending upon the form of tradeoffs, evolution may even move away from the Iterated Prisoner’s Dilemma game altogether. Our work offers a new perspective on the Prisoner’s Dilemma and its predictions for cooperation in natural populations; and it provides a general framework to understand the coevolution of strategies and payoffs in iterated interactions.
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Neuron Networks in Healthy and Diseased Brains

Neuron Networks in Healthy and Diseased Brains | Social Foraging | Scoop.it
The dream of mapping the brain rests on the notion that the trillions of connections between 80 billion neurons form networks that are correlated with mental states. Please see the post, Limits of Current Neuroscience, for a discussion of the many complications in this approach, including the importance of brain waves, glia networks and poor resolution of our imaging devices. Most important is that there is no evidence for a mechanism in the brain that coordinates brain activity to form mental states. There is no center controlling the wide ranging circuits producing the subjective experience of mind. 
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