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Persistence of social signatures in human communication

Persistence of social signatures in human communication | Social Foraging | Scoop.it

The social network maintained by a focal individual, or ego, is intrinsically dynamic and typically exhibits some turnover in membership over time as personal circumstances change. However, the consequences of such changes on the distribution of an ego’s network ties are not well understood. Here we use a unique 18-mo dataset that combines mobile phone calls and survey data to track changes in the ego networks and communication patterns of students making the transition from school to university or work. Our analysis reveals that individuals display a distinctive and robust social signature, captured by how interactions are distributed across different alters. Notably, for a given ego, these social signatures tend to persist over time, despite considerable turnover in the identity of alters in the ego network. Thus, as new network members are added, some old network members either are replaced or receive fewer calls, preserving the overall distribution of calls across network members. This is likely to reflect the consequences of finite resources such as the time available for communication, the cognitive and emotional effort required to sustain close relationships, and the ability to make emotional investments.

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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Brain interfaces open up a whole new way to get hacked

Brain interfaces open up a whole new way to get hacked | Social Foraging | Scoop.it
Malicious software could use brain interfaces to help steal passwords and other private data.
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RoboBees: Big Possibilities in Micro-robots, Including Programmable Bees

RoboBees: Big Possibilities in Micro-robots, Including Programmable Bees | Social Foraging | Scoop.it

Robots that fly. Robots you wear. Robots the size of nickels. These new classes of robots all have one thing in common—every aspect of them must be conceived and created from scratch. There are no designs, materials, manufacturing processes, or off-the-shelf components for them.

Electrical engineer Robert Wood's Microrobotics Lab at Harvard University is at the forefront of engineering such robots, which can fly lighter, slither through narrower spaces, and operate at smaller sizes than anything imagined before.

 

"Traditionally robots have been big, powerful, metallic objects that might weld doors onto cars in a factory," Wood says. "The robots we explore are dramatically different, some on a new, micro-sized scale, others made of soft rather than rigid materials."

 

The ways the robots might one day help humans are astonishing, he says, potentially transforming fields like medicine and agriculture.

 

Take RoboBees, colonies of autonomous flying micro-robots that Wood's team has been developing for years. He says that they could one day perform search-and-rescue expeditions, scout hazardous environments, gather scientific field data, even help pollinate crops. (Related "The Drones Come Home.") Like much of Wood's work, the RoboBees' design is "bio-inspired."

 

"If you want to make something a centimeter big that can fly, several hundred thousand solutions already exist in nature," he says. "We don't just copy nature. We try to understand the what, how, and why behind an organism's anatomy, movement, and behavior, and then translate that into engineering terms."

 

He and fellow researchers devised novel techniques to fabricate, assemble, and manufacture the miniature machines, each with a housefly-size thorax, three-centimeter (1.2-inch) wingspan, and weight of just 80 milligrams (.0028 ounces). The latest prototype rises on a thread-thin tether, flaps its wings 120 times a second, hovers, and flies along preprogrammed paths.

 

The manufacturing process is based on folding layered elements, an idea inspired by children's pop-up books. Now Wood's experiments are focused on finding a self-contained energy source that won't be too heavy and that can efficiently power the delicate bees.


Via Dr. Stefan Gruenwald
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Mapillary opens up 25k street-level images to train automotive AI systems

Mapillary opens up 25k street-level images to train automotive AI systems | Social Foraging | Scoop.it
As more companies wade into the business of building artificial intelligence systems to help you drive (or do the driving for you), a startup founded by an..
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AI Pathologist Zeroes in on Correct Cancer Diagnosis | NVIDIA Blog

AI Pathologist Zeroes in on Correct Cancer Diagnosis | NVIDIA Blog | Social Foraging | Scoop.it
An AI pathologist could lead to more accurate diagnoses and more effective treatments for breast cancer and prostate cancer. 
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Gaussian correlation inequality (GCI): A Long-Sought Proof, Found and Almost Lost

Gaussian correlation inequality (GCI): A Long-Sought Proof, Found and Almost Lost | Social Foraging | Scoop.it
Gaussian correlation inequality (GCI)
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How AI researchers built a neural network that learns to speak in just a few hours

How AI researchers built a neural network that learns to speak in just a few hours | Social Foraging | Scoop.it
The Chinese search giant’s Deep Voice system learns to talk in just a few hours with little or no human interference.
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Nestedness across biological scales

Nestedness across biological scales | Social Foraging | Scoop.it
Biological networks pervade nature. They describe systems throughout all levels of biological organization, from molecules regulating metabolism to species interactions that shape ecosystem dynamics. The network thinking revealed recurrent organizational patterns in complex biological systems, such as the formation of semi-independent groups of connected elements (modularity) and non-random distributions of interactions among elements. Other structural patterns, such as nestedness, have been primarily assessed in ecological networks formed by two non-overlapping sets of elements; information on its occurrence on other levels of organization is lacking. Nestedness occurs when interactions of less connected elements form proper subsets of the interactions of more connected elements. Only recently these properties began to be appreciated in one-mode networks (where all elements can interact) which describe a much wider variety of biological phenomena. Here, we compute nestedness in a diverse collection of one-mode networked systems from six different levels of biological organization depicting gene and protein interactions, complex phenotypes, animal societies, metapopulations, food webs and vertebrate metacommunities. Our findings suggest that nestedness emerge independently of interaction type or biological scale and reveal that disparate systems can share nested organization features characterized by inclusive subsets of interacting elements with decreasing connectedness. We primarily explore the implications of a nested structure for each of these studied systems, then theorize on how nested networks are assembled. We hypothesize that nestedness emerges across scales due to processes that, although system-dependent, may share a general compromise between two features: specificity (the number of interactions the elements of the system can have) and affinity (how these elements can be connected to each other). Our findings suggesting occurrence of nestedness throughout biological scales can stimulate the debate on how pervasive nestedness may be in nature, while the theoretical emergent principles can aid further research on commonalities of biological networks.

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Uber launches artificial intelligence lab - acquires Geometric Intelligence

Uber launches artificial intelligence lab - acquires Geometric Intelligence | Social Foraging | Scoop.it
Geometric Intelligence
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Amazon’s Traveling Salesman Problem

Amazon’s Traveling Salesman Problem | Social Foraging | Scoop.it
The Traveling Salesman Problem (TSP) is a classic mathematical problem in which one tries to find the shortest route that passes through a set of points. The TSP was first defined in the 1800s, it is regarded as difficult to solve and has intrigued mathematicians ever since.

With the advent of mega distribution centers, we may conclude that finding an optimal collection route positively impacts the speed of execution, as well as the aggregated cost of the merchandise.

To solve the TSP we must consider two facts:

a) The modern traveling salesman is IoT connected.

b) There is a never-ending stream of orders to fulfill.

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Amazon Has Chosen This Framework to Guide Deep Learning Strategy

Amazon Has Chosen This Framework to Guide Deep Learning Strategy | Social Foraging | Scoop.it
As artificial intelligence advances, the goal for modern tech companies is to build AI software that thinks for itself without human intervention.

Towards that end, Amazon Web Services just picked MXNet, as its favored deep-learning framework to facilitate that work, according to a blog post Tuesday by Amazon chief technology officer Werner Vogels.
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Collective strategy for obstacle navigation during cooperative transport by ants

Collective strategy for obstacle navigation during cooperative transport by ants | Social Foraging | Scoop.it
Group cohesion and consensus have primarily been studied in the context of discrete decisions, but some group tasks require making serial decisions that build on one another. We examine such collective problem solving by studying obstacle navigation during cooperative transport in ants. In cooperative transport, ants work together to move a large object back to their nest. We blocked cooperative transport groups of Paratrechina longicornis with obstacles of varying complexity, analyzing groups' trajectories to infer what kind of strategy the ants employed. Simple strategies require little information, but more challenging, robust strategies succeed with a wider range of obstacles. We found that transport groups use a stochastic strategy that leads to efficient navigation around simple obstacles, and still succeeds at difficult obstacles. While groups navigating obstacles preferentially move directly toward the nest, they change their behavior over time; the longer the ants are obstructed, the more likely they are to move away from the nest. This increases the chance of finding a path around the obstacle. Groups rapidly changed directions and rarely stalled during navigation, indicating that these ants maintain consensus even when the nest direction is blocked. Although some decisions were aided by the arrival of new ants, at many key points, direction changes were initiated within the group, with no apparent external cause. This ant species is highly effective at navigating complex environments, and implements a flexible strategy that works for both simple and more complex obstacles.

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A neural code for emotion: tracking unconscious emotional influences – with fMRI

A neural code for emotion: tracking unconscious emotional influences – with fMRI | Social Foraging | Scoop.it
Our daily experience rides along the backdrop of a dynamic stream of mental states, characterized by spontaneous changes is mood or emotion. For instance, as
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The AP wants to use machine learning to automate turning print stories into broadcast ones

The AP wants to use machine learning to automate turning print stories into broadcast ones | Social Foraging | Scoop.it
The experiment is part of a larger effort by the news agency to incorporate automation into its journalism.
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Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena

Diffusion Geometry Unravels the Emergence of Functional Clusters in Collective Phenomena | Social Foraging | Scoop.it
Collective phenomena emerge from the interaction of natural or artificial units with a complex organization. The interplay between structural patterns and dynamics might induce functional clusters that, in general, are different from topological ones. In biological systems, like the human brain, the overall functionality is often favored by the interplay between connectivity and synchronization dynamics, with functional clusters that do not coincide with anatomical modules in most cases. In social, sociotechnical, and engineering systems, the quest for consensus favors the emergence of clusters. Despite the unquestionable evidence for mesoscale organization of many complex systems and the heterogeneity of their interconnectivity, a way to predict and identify the emergence of functional modules in collective phenomena continues to elude us. Here, we propose an approach based on random walk dynamics to define the diffusion distance between any pair of units in a networked system. Such a metric allows us to exploit the underlying diffusion geometry to provide a unifying framework for the intimate relationship between metastable synchronization, consensus, and random search dynamics in complex networks, pinpointing the functional mesoscale organization of synthetic and biological systems.
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Rescooped by Ashish Umre from Center for Collective Dynamics of Complex Systems (CoCo)
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Why Is 'Systems Thinking' So Rare?

Center for Collective Dynamics of Complex Systems (CoCo) Seminar Series April 27, 2017 Mark Sellers (Systems Science, Binghamton University / Northrop Grumman…

Via Hiroki Sayama
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Robots sorting system helps Chinese company finish at least 200,000 packages a day in the warehouse

Self-charging robots sorting system helps Chinese delivery company finish at least 200,000 packages a day in the warehouse Chinese delivery firm is moving to embrace automation. Orange robots at the company's sorting stations are able to identify the destination of a package through a code-scan, virtually eliminating sorting mistakes. The army of robots can sort up to 200,000 packages a day, and are self-charging, meaning they are operational 24/7. The company estimates its robotic sorting system is saving around 70-percent of the costs a human-based sorting line would require.

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Serendipity and strategy in rapid innovation

Innovation is to organizations what evolution is to organisms: it is how organisations adapt to changes in the environment and improve. Governments, institutions and firms that innovate are more likely to prosper and stand the test of time; those that fail to do so fall behind their competitors and succumb to market and environmental change. Yet despite steady advances in our understanding of evolution, what drives innovation remains elusive. On the one hand, organizations invest heavily in systematic strategies to drive innovation. On the other, historical analysis and individual experience suggest that serendipity plays a significant role in the discovery process. To unify these two perspectives, we analyzed the mathematics of innovation as a search process for viable designs across a universe of building blocks. We then tested our insights using historical data from language, gastronomy and technology. By measuring the number of makeable designs as we acquire more components, we observed that the relative usefulness of different components is not fixed, but cross each other over time. When these crossovers are unanticipated, they appear to be the result of serendipity. But when we can predict crossovers ahead of time, they offer an opportunity to strategically increase the growth of our product space. Thus we find that the serendipitous and strategic visions of innovation can be viewed as different manifestations of the same thing: the changing importance of component building blocks over time.
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Toward negotiable reinforcement learning: shifting priorities in Pareto optimal sequential decision-making

Existing multi-objective reinforcement learning (MORL) algorithms do not account for objectives that arise from players with differing beliefs. Concretely, consider two players with different beliefs and utility functions who may cooperate to build a machine that takes actions on their behalf. A representation is needed for how much the machine's policy will prioritize each player's interests over time. Assuming the players have reached common knowledge of their situation, this paper derives a recursion that any Pareto optimal policy must satisfy. Two qualitative observations can be made from the recursion: the machine must (1) use each player's own beliefs in evaluating how well an action will serve that player's utility function, and (2) shift the relative priority it assigns to each player's expected utilities over time, by a factor proportional to how well that player's beliefs predict the machine's inputs. Observation (2) represents a substantial divergence from na\"{i}ve linear utility aggregation (as in Harsanyi's utilitarian theorem, and existing MORL algorithms), which is shown here to be inadequate for Pareto optimal sequential decision-making on behalf of players with different beliefs.
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Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees

Insect Bio-inspired Neural Network Provides New Evidence on How Simple Feature Detectors Can Enable Complex Visual Generalization and Stimulus Location Invariance in the Miniature Brain of Honeybees | Social Foraging | Scoop.it
Honeybees (Apis mellifera) display an impressive visual behavioural repertoire as well as astounding learning capabilities. Foragers rely on visual and olfactory cues identifying rewarding flowers. Being able to recognise informative cues displayed by flowers can be assumed to facilitate fast and efficient decision-making. Indeed, honeybees can be trained to discriminate by an impressive range of visual cues; symmetry [1–3], arrangements of edges [4–6], size [7, 8], pattern disruption [9] and edge orientation [10–12]. These abilities are all the more impressive since trained bees are able to apply these same learnt cues to patterns which may have little or no resemblance to the original training patterns, so long as they fall into the same class of e.g. plane of symmetry, or edge orientation.

This rich visual behaviour despite a relatively tiny brain makes honeybees an ideal model species to explore how visual stimuli are processed and to determine if generalization requires a complex neuronal architecture. Using the published intracellular recordings of large-field optic ganglia neurons to achromatic stimuli [13, 14] and the known anatomical morphologies of mushroom body (learning centres) class II ‘clawed’ Kenyon cells [15] we designed two simple, but biologically inspired models. These models were not created, or indeed in any way ‘tweaked’ to replicate performance at any particular visual task. Instead they attempt to explore how well, or poorly, the known neuronal types within the bee brain could solve real behaviourally relevant problems and how much neuronal complexity would be required to do so. The initial models presented here were therefore kept very basic with limited neuronal pathways and very simple synaptic connections from the optic lobes to the mushroom bodies. In addition, to comprehend how these optic lobe neuron responses alone may explain the bees’ discrimination abilities and behavioural performance, we did not employ any form of learning in these models. Since two of the optic ganglia (medulla and lobula) of bees extend a variety of axonal fibres to both the ipsilateral and the contralateral mushroom bodies and, as opposed to axons from different regions of the optic lobes that are distinctly layered within the mushroom bodes, there is no apparent segregation of the visual inputs from the individual corresponding left and right eye regions [16, 17], we tested the discrimination and generalization performance difference between retaining independent inputs from each eye and combining the neuronal input from both eyes within our simulated mushroom body models. These models allowed us to simulate achromatic pattern experiments and compare the simulation performances of our two different bee-brain models—henceforth called ‘simulated bees’, to the performance of actual honeybees in these same specific experiments.
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Market forces influence helping behaviour in cooperatively breeding paper wasps

Market forces influence helping behaviour in cooperatively breeding paper wasps | Social Foraging | Scoop.it
In cooperatively breeding species, subordinates help to raise the dominant breeders’ offspring in return for benefits associated with group membership.
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Dynamic scaling in natural swarms

Collective behaviour in biological systems pitches us against theoretical challenges way beyond the borders of ordinary statistical physics. The lack of concepts like scaling and renormalization is particularly grievous, as it forces us to negotiate with scores of details whose relevance is often hard to assess. In an attempt to improve on this situation, we present here experimental evidence of the emergence of dynamic scaling laws in natural swarms. We find that spatio-temporal correlation functions in different swarms can be rescaled by using a single characteristic time, which grows with the correlation length with a dynamical critical exponent z~1. We run simulations of a model of self-propelled particles in its swarming phase and find z~2, suggesting that natural swarms belong to a novel dynamic universality class. This conclusion is strengthened by experimental evidence of non-exponential relaxation and paramagnetic spin-wave remnants, indicating that previously overlooked inertial effects are needed to describe swarm dynamics. The absence of a purely relaxational regime suggests that natural swarms are subject to a near-critical censorship of hydrodynamics.
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How Cern's data science is helping supermarkets waste less food

How Cern's data science is helping supermarkets waste less food | Social Foraging | Scoop.it
Retailers looking to make smarter decisions should take inspiration from the Large Hadron Collider at Cern. Not convinced? That’s exactly what professor Michael Feindt, founder and chief scientific adviser at BlueYonder is trying to do.

"When we do predictions, how can we then form optimal decisions?" he asked the audience at WIRED Retail 2016. The answer, he explained, was to trust the data and then automate decision making. "A lot of decisions you make will never be automated. But I think in operational decisions in retail we can automate up to 99 per cent."

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When AR takes over we will all rent virtual diamonds and have AI personal shoppers at home

When AR takes over we will all rent virtual diamonds and have AI personal shoppers at home
By LIAT CLARK
What should a store order? At what price? What quantity? When? All of these decisions are repeated over time. "This is usually done by hand or gut feeling, but it can be done better," said Feindt. "These are decisions that are very regular and that we have a lot of historic data about."
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Comparing Adobe and Google Analytics (with R)

Comparing Adobe and Google Analytics (with R) | Social Foraging | Scoop.it
Raise your hand if you’re running Adobe Analytics on your site. Okay, now keep your hands up if you also are running Google Analytics. Wow. Not very many hands went down there! There are lots of reasons that organizations find themselves running multiple web analytics platforms on their sites. Some are good. Many aren’t. Who’s to judge? …
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The Power of Expectation Can Restrain Hyper-Emotional Memories in the Brain

The Power of Expectation Can Restrain Hyper-Emotional Memories in the Brain | Social Foraging | Scoop.it
The creaking of an opening gate followed by a dog attack can disturb otherwise pleasant evening walks. The sound of that gate opening on subsequent walks will elicit an emotional response, and the power of this response will be different if the dog was a German shepherd or a poodle. Through repeated experiences, the neighborhood, the gate and the dog all become part of the brain’s emotional memory system. The core of this system–the amygdala–forges indelible links of experience when we are attacked or threatened but, thanks to the power of expectation, the strength of these emotional memories is proportional to the unpleasantness of the experience.

“Forming an emotional memory is all about learning and calibrating our internal expectations with repeated external stimuli from the environment,” says Joshua Johansen, a team leader at the RIKEN Brain Science Institute. An instructive signal like a dog attack should startle you–and your amygdala–the first time it happens, but over time, both your brain activity and your behavior will temper the reaction to the dog attack once you learn to expect when and how it happens, for example on a particular street, outside of a particular house. In a study published in Nature Neuroscience, Johansen and colleagues discovered a neural circuit that can temper the strength of emotional memories by restraining the amygdala’s over-responsiveness to expected but unpleasant stimuli.
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How do Asian honey bees make decisions using dance?

How do Asian honey bees make decisions using dance? | Social Foraging | Scoop.it
Decision making is hard. Decision making in a group is even harder. The vultures from Disney’s The Jungle Book come to mind. What we gonna do? I don’t know, whatcha wanna do? And so it goes.

Honey bees are an example of a superorganism. Not only do they work together to run their large and complex societies, they also work together to decide on a new home.

When honey bees decide it’s getting too cozy in their hive, half of the bees will leave with the old queen and swarm to an intermediate location. The remaining bees will stay home with a newly raised queen.
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