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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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One form of neuron turned into another in brain

One form of neuron turned into another in brain | Social Foraging | Scoop.it
A new finding by stem cell biologists turns one of the basics of neurobiology on its head -- demonstrating that it is possible to turn one type of already differentiated neuron into another within the brain.

 

The discovery by Paola Arlotta and Caroline Rouaux "tells you that maybe the brain is not as immutable as we always thought, because at least during an early window of time one can reprogram the identity of one neuronal class into another," said Arlotta, an Associate Professor in Harvard's Department of Stem Cell and Regenerative Biology (SCRB).

 

The principle of direct lineage reprogramming of differentiated cells within the body was first proven by SCRB co-chair and Harvard Stem Cell Institute (HSCI) co-director Doug Melton and colleagues five years ago, when they reprogrammed exocrine pancreatic cells directly into insulin producing beta cells.

 

Arlotta and Rouaux now have proven that neurons too can change their mind. The work is being published on-line Jan. 20 by the journal Nature Cell Biology.

 

In their experiments, Arlotta targeted callosal projection neurons, which connect the two hemispheres of the brain, and turned them into neurons similar to corticospinal motor neurons, one of two populations of neurons destroyed in Amyotrophic Lateral Sclerosis (ALS), also known as Lou Gehrig's disease. To achieve such reprogramming of neuronal identity, the researchers used a transcription factor called Fezf2, which long as been known for playing a central role in the development of corticospinal neurons in the embryo.

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The Architecture of Open Source Applications

The Architecture of Open Source Applications | Social Foraging | Scoop.it

Architects look at thousands of buildings during their training, and study critiques of those buildings written by masters. In contrast, most software developers only ever get to know a handful of large programs well—usually programs they wrote themselves—and never study the great programs of history. As a result, they repeat one another's mistakes rather than building on one another's successes.

 

Our goal is to change that. In these two books, the authors of four dozen open source applications explain how their software is structured, and why. What are each program's major components? How do they interact? And what did their builders learn during their development? In answering these questions, the contributors to these books provide unique insights into how they think.

 

If you are a junior developer, and want to learn how your more experienced colleagues think, these books are the place to start. If you are an intermediate or senior developer, and want to see how your peers have solved hard design problems, these books can help you too.

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The Problem of Thresholding in Small-World Network Analysis

The Problem of Thresholding in Small-World Network Analysis | Social Foraging | Scoop.it

Graph theory deterministically models networks as sets of vertices, which are linked by connections. Such mathematical representation of networks, called graphs are increasingly used in neuroscience to model functional brain networks. It was shown that many forms of structural and functional brain networks have small-world characteristics, thus, constitute networks of dense local and highly effective distal information processing. Motivated by a previous small-world connectivity analysis of resting EEG-data we explored implications of a commonly used analysis approach. This common course of analysis is to compare small-world characteristics between two groups using classical inferential statistics. This however, becomes problematic when using measures of inter-subject correlations, as it is the case in commonly used brain imaging methods such as structural and diffusion tensor imaging with the exception of fibre tracking. Since for each voxel, or region there is only one data point, a measure of connectivity can only be computed for a group. To empirically determine an adequate small-world network threshold and to generate the necessary distribution of measures for classical inferential statistics, samples are generated by thresholding the networks on the group level over a range of thresholds. We believe that there are mainly two problems with this approach. First, the number of thresholded networks is arbitrary. Second, the obtained thresholded networks are not independent samples. Both issues become problematic when using commonly applied parametric statistical tests. Here, we demonstrate potential consequences of the number of thresholds and non-independency of samples in two examples (using artificial data and EEG data). Consequently alternative approaches are presented, which overcome these methodological issues.

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Entropy-Based Analysis and Bioinformatics-Inspired Integration of Global Economic Information Transfer

Entropy-Based Analysis and Bioinformatics-Inspired Integration of Global Economic Information Transfer | Social Foraging | Scoop.it

The assessment of information transfer in the global economic network helps to understand the current environment and the outlook of an economy. Most approaches on global networks extract information transfer based mainly on a single variable. This paper establishes an entirely new bioinformatics-inspired approach to integrating information transfer derived from multiple variables and develops an international economic network accordingly. In the proposed methodology, we first construct the transfer entropies (TEs) between various intra- and inter-country pairs of economic time series variables, test their significances, and then use a weighted sum approach to aggregate information captured in each TE. Through a simulation study, the new method is shown to deliver better information integration compared to existing integration methods in that it can be applied even when intra-country variables are correlated. Empirical investigation with the real world data reveals that Western countries are more influential in the global economic network and that Japan has become less influential following the Asian currency crisis.

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Evolution in a Changing Environment

Evolution in a Changing Environment | Social Foraging | Scoop.it

We propose a simple model for genetic adaptation to a changing environment, describing a fitness landscape characterized by two maxima. One is associated with “specialist” individuals that are adapted to the environment; this maximum moves over time as the environment changes. The other maximum is static, and represents “generalist” individuals not affected by environmental changes. The rest of the landscape is occupied by “maladapted” individuals. Our analysis considers the evolution of these three subpopulations. Our main result is that, in presence of a sufficiently stable environmental feature, as in the case of an unchanging aspect of a physical habitat, specialists can dominate the population. By contrast, rapidly changing environmental features, such as language or cultural habits, are a moving target for the genes; here, generalists dominate, because the best evolutionary strategy is to adopt neutral alleles not specialized for any specific environment. The model we propose is based on simple assumptions about evolutionary dynamics and describes all possible scenarios in a non-trivial phase diagram. The approach provides a general framework to address such fundamental issues as the Baldwin effect, the biological basis for language, or the ecological consequences of a rapid climate change.

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Durable Resistance to Crop Pathogens: An Epidemiological Framework to Predict Risk under Uncertainty

Durable Resistance to Crop Pathogens: An Epidemiological Framework to Predict Risk under Uncertainty | Social Foraging | Scoop.it

Increasing the durability of crop resistance to plant pathogens is one of the key goals of virulence management. Despite the recognition of the importance of demographic and environmental stochasticity on the dynamics of an epidemic, their effects on the evolution of the pathogen and durability of resistance has not received attention. We formulated a stochastic epidemiological model, based on the Kramer-Moyal expansion of the Master Equation, to investigate how random fluctuations affect the dynamics of an epidemic and how these effects feed through to the evolution of the pathogen and durability of resistance. We focused on two hypotheses: firstly, a previous deterministic model has suggested that the effect of cropping ratio (the proportion of land area occupied by the resistant crop) on the durability of crop resistance is negligible. Increasing the cropping ratio increases the area of uninfected host, but the resistance is more rapidly broken; these two effects counteract each other. We tested the hypothesis that similar counteracting effects would occur when we take account of demographic stochasticity, but found that the durability does depend on the cropping ratio. Secondly, we tested whether a superimposed external source of stochasticity (for example due to environmental variation or to intermittent fungicide application) interacts with the intrinsic demographic fluctuations and how such interaction affects the durability of resistance. We show that in the pathosystem considered here, in general large stochastic fluctuations in epidemics enhance extinction of the pathogen. This is more likely to occur at large cropping ratios and for particular frequencies of the periodic external perturbation (stochastic resonance). The results suggest possible disease control practises by exploiting the natural sources of stochasticity.

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Android in My Rice Cooker: Gateway to Future Cyber Home Invasion?

Over the past few days, there were several interesting stories in the news that caught my eye. The first was at Bloomberg News on 8 January; it reported how Google’s Android operating system software is increasingly being embedded into everything from refrigerators to rice-cookers. According to the story, Android creates a nice, symbiotic relationship between Google and product manufacturers. The manufacturers get a free and easy-to-use OS that allows them to create Internet-connected products, while Google is in a position to “collect more data to build its lucrative search business and one-up software rivals Microsoft Corp. (MSFT) and Apple Inc.”

 

Another goal of the manufacturers is to also create products that can “exchange information with less human intervention.” The Bloomberg story goes on to state that, “A television, for example, might show a pop-up message from a clothes dryer in the basement, indicating that the homeowner’s jeans are not yet dry. The user could press a button on the TV remote to automatically add 15 minutes to the dryer cycle. A connected rice cooker could determine what type of rice is being used and set cooking instructions accordingly.”

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Decision making in ant foragers (Lasius niger) facing conflicting private and social information

Decision making in ant foragers (Lasius niger) facing conflicting private and social information | Social Foraging | Scoop.it

Foragers of many ant species use pheromone trails to guide nestmates to food sources. During foraging, individual workers can also learn the route to a food source. Foragers of the mass-recruiting ant Lasius niger use both pheromone trails and memory to locate a food source. As a result, an experienced forager can have a conflict between social information (trail pheromones) and private information (route memory) at trail bifurcations. We tested decision making in L. niger foragers facing such an informational conflict in situations where both the strength of the pheromone trail and the number of previous visits to the food source varied. Foragers quickly learned the branch at a T bifurcation that leads to a food source, with 74.6% choosing correctly after one previous visit and 95.3% after three visits. Pheromone trails had a weaker effect on choice behaviour of naïve ants, with only 61.6% and 70.2% choosing the branch that had been marked by one or 20 foragers versus an unmarked branch. When there was a conflict between private and social information, memory overrides pheromone after just one previous visit to a food source. Most ants, 82–100%, chose the branch where they had collected food during previous foraging trips, with the proportion depending on the number of previous trips (1 v. 3) but not on the strength of the pheromone trail (1 v. 20). In addition, the presence of a pheromone trail at one branch in a bifurcation had no effect on the time it took an experienced ant to choose the correct branch (the branch without pheromone). These results suggest that private information (navigational memory) dominates over social information (chemical tail) in orientation decisions during foraging activities in experienced L. niger foragers.

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Social Learning in Insects — From Miniature Brains to Consensus Building

Communication and learning from each other are part of the success of insect societies. Here, we review a spectrum of social information usage in insects — from inadvertently provided cues to signals shaped by selection specifically for information transfer. We pinpoint the sensory modalities involved and, in some cases, quantify the adaptive benefits. Well substantiated cases of social learning among the insects include learning about predation threat and floral rewards, the transfer of route information using a symbolic ‘language’ (the honeybee dance) and the rapid spread of chemosensory preferences through honeybee colonies via classical conditioning procedures. More controversial examples include the acquisition of motor memories by observation, teaching in ants and behavioural traditions in honeybees. In many cases, simple mechanistic explanations can de identified for such complex behaviour patterns.

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Video: Discovery of the Spider That Builds Spider Decoys

Video: Discovery of the Spider That Builds Spider Decoys | Social Foraging | Scoop.it

In December, we reported on a new species of spider discovered in Peru. Tiny, and likely a new member of the genus Cyclosa, the spider builds large, spider-shaped decoys — and vibrates its web, acting as a master puppeteer.

Here is a video shot at the moment the spiders were discovered.

 

“I don’t know of any potential species discovery that has been caught on video to the same level that this one has been,” said the videographer who goes by Destin, who was accompanying biologist Phil Torres in the Peruvian Amazon.

 

“It’s fun to go back and watch the video because it reminds me of how confused and perplexed we were.”

 

Indeed, the captured moment of discovery includes the following exchange:

 

“It’s a tiny spider disguised as a big spider!”

 

“Shut up.”

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How Did Topological Data Analysis (TDA) Lead to the Birth of Ayasdi?

How Did Topological Data Analysis (TDA) Lead to the Birth of Ayasdi? | Social Foraging | Scoop.it

http://www.ayasdi.com/

 

Topological Data Analysis (TDA) brings together mathematics with computer science, and uses algorithms and concepts from algebraic topology to extract insights from complex multi-dimensional data structures. In more layman’s terms: Topological Data Analysis studies the underlying shape of data with the principle that “Data has Shape, Shape has Meaning.”

 

While Topological Data Analysis (TDA) may seem like something only for math people, Ayasdi’s founder and Stanford Mathematics Professor Gunnar Carlsson had a goal to make TDA into something that anyone could use without having a Ph.D. in mathematics.

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Ant foraging and geodesic paths in labyrinths: Analytical and computational results

Ant foraging and geodesic paths in labyrinths: Analytical and computational results | Social Foraging | Scoop.it

In this paper we propose a mechanism for the formation of paths of minimal length between two points (trails) by a collection of individuals undergoing reinforced random walks. This is the case, for instance, of ant colonies in search for food and the development of ant trails connecting nest and food source. Our mechanism involves two main ingredients: (1) the reinforcement due to the gradients in the concentration of some substance (pheromones in the case of ants) and (2) the persistence understood as the tendency to preferably follow straight directions in the absence of any external effect. Our study involves the formulation and analysis of suitable Markov chains for the motion in simple labyrinths, that will be understood as graphs, and numerical computations in more complex graphs reproducing experiments performed in the past with ants.

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Visual Data Mining of Biological Networks: One Size Does Not Fit All

Visual Data Mining of Biological Networks: One Size Does Not Fit All | Social Foraging | Scoop.it

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

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The science behind 'beatboxing'

The science behind 'beatboxing' | Social Foraging | Scoop.it

Using the mouth, lips, tongue and voice to generate sounds that one might never expect to come from the human body is the specialty of the artists known as beatboxers. Now scientists have used scanners to peer into a beatboxer as he performed his craft to reveal the secrets of this mysterious art.

 

The human voice has long been used to generate percussion effects in many cultures, including North American scat singing, Celtic lilting and diddling, and Chinese kouji performances. In southern Indian classical music, konnakol is the percussive speech of the solkattu rhythmic form.  In contemporary pop music, the relatively young vocal art form of beatboxing is an element of hip-hop culture.

 

Until now, the phonetics of these percussion effects were not examined in detail. For instance, it was unknown to what extent beatboxers produced sounds already used within human language.

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Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex

Network Self-Organization Explains the Statistics and Dynamics of Synaptic Connection Strengths in Cortex | Social Foraging | Scoop.it

The information processing abilities of neural circuits arise from their synaptic connection patterns. Understanding the laws governing these connectivity patterns is essential for understanding brain function. The overall distribution of synaptic strengths of local excitatory connections in cortex and hippocampus is long-tailed, exhibiting a small number of synaptic connections of very large efficacy. At the same time, new synaptic connections are constantly being created and individual synaptic connection strengths show substantial fluctuations across time. It remains unclear through what mechanisms these properties of neural circuits arise and how they contribute to learning and memory. In this study we show that fundamental characteristics of excitatory synaptic connections in cortex and hippocampus can be explained as a consequence of self-organization in a recurrent network combining spike-timing-dependent plasticity (STDP), structural plasticity and different forms of homeostatic plasticity. In the network, associative synaptic plasticity in the form of STDP induces a rich-get-richer dynamics among synapses, while homeostatic mechanisms induce competition. Under distinctly different initial conditions, the ensuing self-organization produces long-tailed synaptic strength distributions matching experimental findings. We show that this self-organization can take place with a purely additive STDP mechanism and that multiplicative weight dynamics emerge as a consequence of network interactions. The observed patterns of fluctuation of synaptic strengths, including elimination and generation of synaptic connections and long-term persistence of strong connections, are consistent with the dynamics of dendritic spines found in rat hippocampus. Beyond this, the model predicts an approximately power-law scaling of the lifetimes of newly established synaptic connection strengths during development. Our results suggest that the combined action of multiple forms of neuronal plasticity plays an essential role in the formation and maintenance of cortical circuits.

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Visual Data Mining of Biological Networks: One Size Does Not Fit All

Visual Data Mining of Biological Networks: One Size Does Not Fit All | Social Foraging | Scoop.it

High-throughput technologies produce massive amounts of data. However, individual methods yield data specific to the technique used and biological setup. The integration of such diverse data is necessary for the qualitative analysis of information relevant to hypotheses or discoveries. It is often useful to integrate these datasets using pathways and protein interaction networks to get a broader view of the experiment. The resulting network needs to be able to focus on either the large-scale picture or on the more detailed small-scale subsets, depending on the research question and goals. In this tutorial, we illustrate a workflow useful to integrate, analyze, and visualize data from different sources, and highlight important features of tools to support such analyses.

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Approximate Bayesian Computation

Approximate Bayesian Computation | Social Foraging | Scoop.it

PLOS Computational Biology is an open-accesApproximate Bayesian computation (ABC) constitutes a class of computational methods rooted in Bayesian statistics. In all model-based statistical inference, the likelihood function is of central importance, since it expresses the probability of the observed data under a particular statistical model, and thus quantifies the support data lend to particular values of parameters and to choices among different models. For simple models, an analytical formula for the likelihood function can typically be derived. However, for more complex models, an analytical formula might be elusive or the likelihood function might be computationally very costly to evaluate. ABC methods bypass the evaluation of the likelihood function. In this way, ABC methods widen the realm of models for which statistical inference can be considered. ABC methods are mathematically well-founded, but they inevitably make assumptions and approximations whose impact needs to be carefully assessed. Furthermore, the wider application domain of ABC exacerbates the challenges of parameter estimation and model selection. ABC has rapidly gained popularity over the last years and in particular for the analysis of complex problems arising in biological sciences (e.g., in population genetics, ecology, epidemiology, and systems biology).s

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A Neural Network Model to Translate Brain Developmental Events across Mammalian Species

A Neural Network Model to Translate Brain Developmental Events across Mammalian Species | Social Foraging | Scoop.it

Translating the timing of brain developmental events across mammalian species using suitable models has provided unprecedented insights into neural development and evolution. More importantly, these models can prove to be useful abstractions and predict unknown events across species from known empirical event timing data retrieved from published literature. Such predictions can be especially useful since the distribution of the event timing data is skewed with a majority of events documented only across a few selected species. The present study investigates the choice of single hidden layer feed-forward neural networks (FFNN) for predicting the unknown events from the empirical data. A leave-one-out cross-validation approach is used to determine the optimal number of units in the hidden layer and the decay parameter for the FFNN. It is shown that unlike the present Finlay-Darlington (FD) model, FFNN does not impose any constraints on the functional form of the model and falls under the class of semiparametric regression models that can approximate any continuous function. The results from FFNN as well as FD model also indicate that a majority of events with large absolute prediction errors correspond to those of primates and late events comprising the tail of event timing data distribution with minimal representation in the empirical data. These results also indicate that accurate prediction of primate events may be challenging.

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Evolutionary Optimization of Protein Folding

Evolutionary Optimization of Protein Folding | Social Foraging | Scoop.it

Nature has shaped the make up of proteins since their appearance, 3.8 billion years ago. However, the fundamental drivers of structural change responsible for the extraordinary diversity of proteins have yet to be elucidated. Here we explore if protein evolution affects folding speed. We estimated folding times for the present-day catalog of protein domains directly from their size-modified contact order. These values were mapped onto an evolutionary timeline of domain appearance derived from a phylogenomic analysis of protein domains in 989 fully-sequenced genomes. Our results show a clear overall increase of folding speed during evolution, with known ultra-fast downhill folders appearing rather late in the timeline. Remarkably, folding optimization depends on secondary structure. While alpha-folds showed a tendency to fold faster throughout evolution, beta-folds exhibited a trend of folding time increase during the last 1.5 billion years that began during the “big bang” of domain combinations. As a consequence, these domain structures are on average slow folders today. Our results suggest that fast and efficient folding of domains shaped the universe of protein structure. This finding supports the hypothesis that optimization of the kinetic and thermodynamic accessibility of the native fold reduces protein aggregation propensities that hamper cellular functions.

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Ripple Credit System Could Help or Harm Bitcoin

Ripple Credit System Could Help or Harm Bitcoin | Social Foraging | Scoop.it
Decentralized, peer-to-peer credit could either be the exchange Bitcoiners want—or the nascent currency’s first credible competition

 

This Christmas season I bought my very first bitcoins. The idea was to give them as gifts to my family, thus setting the Pecks out on the adventure of figuring out how Bitcoin—the four-year-old decentralized, stateless cryptocurrency—works and why it might be important. I enjoy giving homework as a present.

 

The adventure, however, turned out to be my own, as I first had to find a way to get some bitcoins. Apparently there are a few ways: You can join the network that creates bitcoins and mint them at a slow, unpredictable pace. You can accept them as payment. You can buy them from a centralized exchange. Or you can find a random stranger online who wants to sell them and meet up with him on the street. I chose the latter, an option that landed me in a coffee shop three days before Christmas, handing over US $100 cash to a man I had never met, whose father had promised to immediately transfer me bitcoins from his home in the Caribbean.

 

And so he did. But this arrangement is far from perfect, as are all of the alternatives. Each requires that a Bitcoin buyer trust either strangers to follow through on their promises or a centralized exchange to broker the deal. Confidence in Bitcoin’s centralized exchanges is especially wobbly following a series of hacking incidents last year. Bitcoinica and BitFloor, the two hardest hit, are still struggling to pay back their customers. And a larger problem looms: the specter of government regulation. If any country decides to outlaw Bitcoin, bearing down on the exchanges will be the easiest way to do it.

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Uncovering the complexity of ant foraging trail

The common garden ant Lasius niger use both trail pheromones and memory of past visits to navigate to and from food sources. In a recent paper we demonstrated a synergistic effect between route memory and trail pheromones: the presence of trail pheromones results in experienced ants walking straighter and faster. We also found that experienced ants leaving a pheromone trail deposit less pheromone. Here we focus on another finding of the experiment: the presence of cuticular hydrocarbons (CHCs), which are used as home range markers by ants, also affects pheromone deposition behavior. When walking on a trail on which CHCs are present but trail pheromones are not, experienced foragers deposit less pheromone on the outward journey than on the return journey. The regulatory mechanisms ants use during foraging and recruitment behavior is subtle and complex, affected by multiple interacting factors such as route memory, travel direction and the presence trail pheromone and home-range markings.

 

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Synergy between social and private information increases foraging efficiency in ants

Insect societies integrate many information sources to organize collective activities such as foraging. Many ants use trail pheromones to guide foragers to food sources, but foragers can also use memories to find familiar locations of stable food sources. Route memories are often more accurate than trail pheromones in guiding ants, and are often followed in preference to trail pheromones when the two conflict. Why then does the system expend effort in producing and acquiring seemingly redundant and low-quality information, such as trail pheromones, when route memory is available? Here we show that, in the ant Lasius niger, trail pheromones and route memory act synergistically during foraging; increasing walking speed and straightness by 25 and 30 per cent, respectively, and maintaining trail pheromone deposition, but only when used together. Our results demonstrate a previously undescribed major role of trail pheromones: to complement memory by allowing higher confidence in route memory. This highlights the importance of multiple interacting information sources in the efficient running of complex adaptive systems.

 

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John Herndon's curator insight, February 15, 2013 2:26 PM

Management lesson from nature: Encourage your employees to take your guidance to heart, but give them automony and safety to find their own path to get the job done. The individual's own approach may be more efficient and productive for their personality style. Promote sharing of lessons learned. This will form a self supporting guidance system which will become the foundation for the future individuals that need to complete a similar task.

 

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Facebook's Bold, Compelling and Scary Engine of Discovery: The Inside Story of Graph Search

Beast had a birthday last week. The First Dog of social networking — live-in companion to Mark Zuckerberg and his bride, Priscilla Chan — turned two. The proud owners baked a cake for the Hungarian sheepdog and decided to throw an impromptu party. Naturally, when it came time to compile the guest list, the couple turned to Facebook, the $67 billion company that Zuckerberg founded in his dorm room nine years ago.

 

To date, sorting through your Facebook friends could be a frustrating task. Although the site has a search bar, there has been no easy way to quickly cull contacts based on specific criteria. But Zuckerberg was testing a major new feature that Facebook would announce on Jan. 15 — one that promises to transform its user experience, threaten its competitors, and torment privacy activists. It’s called Graph Search, and it will eventually allow a billion people to dive into the vast trove of stored information about them and their network of friends. In Zuckerberg’s case, it allowed him to type “Friends of Priscilla and me who live around Palo Alto” and promptly receive a list of potential celebrants. “We invited five people over who were obvious dog lovers,” he says.

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OCE Postdoctoral Fellowship - Evolutionary Aerial Robotics @ CSIRO, Brisbane, Australia

CSIRO ICT Centre's Autonomous Systems Lab is offering a prestigious OCE Postdoctoral Fellowship for a talented and dedicated recent (or near) PhD graduate with experience in the fields of Evolutionary Computing and/or Aerial Robotics.


The successful candidatewill work on a new project in Evolutionary Aerial Robotics (EAR) with the objective of developing autonomous Unmanned Aircraft Systems(UAS) that create and evolve their own autonomy systems (i.e. the UAS “brain”), and thereby shortcutting and outperforming the expensive and time-consuming classical engineering approach.


This is an interdisciplinary project that is concerned with developing novel autonomy technologies that will allow future Unmanned UAS to exhibit advanced autonomous behaviours using artificial evolution algorithms. This will include research and development of novel or improved evolutionary algorithms for UAS, applying them to a variety of aspects including flight control, bio-inspired navigation, 3D obstacle avoidance, failure detection and autonomy architectures.

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'Good Times' brainwave app blocks phone calls when the user is busy, wins 1st prize ($30,000) of AT&T Hackathon

'Good Times' brainwave app blocks phone calls when the user is busy, wins 1st prize ($30,000) of AT&T Hackathon | Social Foraging | Scoop.it
Developed in 24 hours only, 'Good Times', a brain-controlled application connects to the Necomimi brainwave cat ears and blocks phone calls when the user is busy at work or a conversation.
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