Social Foraging
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Social Foraging
Dynamics of Social Interaction
Curated by Ashish Umre
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Neurobiologists investigate neuronal basis intelligence in birds

Neurobiologists investigate neuronal basis intelligence in birds | Social Foraging | Scoop.it
Scientists have long suspected that corvids – the family of birds including ravens, crows and magpies – are highly intelligent.

 

The Tübingen researchers are the first to investigate the brain physiology of crows' intelligent behavior. They trained crows to carry out memory tests on a computer. The crows were shown an image and had to remember it. Shortly afterwards, they had to select one of two test images on a touchscreen with their beaks based on a switching behavioral rules. One of the test images was identical to the first image, the other different. Sometimes the rule of the game was to select the same image, and sometimes it was to select the different one. The crows were able to carry out both tasks and to switch between them as appropriate. That demonstrates a high level of concentration and mental flexibility which few animal species can manage – and which is an effort even for humans.

 

The crows were quickly able to carry out these tasks even when given new sets of images. The researchers observed neuronal activity in the nidopallium caudolaterale, a brain region associated with the highest levels of cognition in birds. One group of nerve cells responded exclusively when the crows had to choose the same image – while another group of cells always responded when they were operating on the "different image" rule. By observing this cell activity, the researchers were often able to predict which rule the crow was following even before it made its choice.

 

The study published in Nature Communications provides valuable insights into the parallel evolution of intelligent behavior. "Many functions are realized differently in birds because a long evolutionary history separates us from these direct descendants of the dinosaurs," says Lena Veit. "This means that bird brains can show us an alternative solution out of how intelligent behavior is produced with a different anatomy." Crows and primates have different brains, but the cells regulating decision-making are very similar. They represent a general principle which has re-emerged throughout the history of evolution. "Just as we can draw valid conclusions on aerodynamics from a comparison of the very differently constructed wings of birds and bats, here we are able to draw conclusions about how the brain works by investigating the functional similarities and differences of the relevant brain areas in avian and mammalian brains," says Professor Andreas Nieder.


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Hardship Strengthens Mutual Bonds

Hardship Strengthens Mutual Bonds | Social Foraging | Scoop.it

Tiny sap-sucking insects that are a scourge to gardeners also have the upside of helping trees survive in seasonally dry forests in Central America. How? Scale insects use carbon they get from Cordia alliodora trees to make sugar-rich “honeydew” for Azteca pittieri ants, which in turn defend the trees against leaf-munching insects. Mutualism is often stronger when resources are scarce, but this interdependence usually involves a commodity that is traded directly between species. Now, in this issue of PLOS Biology, Pringle and colleagues show that lack of a resource that is not traded—water—intensifies the bonds between C. alliodora, scale insects, and ants.

 

Found from southern Mexico through South America, C. alliodora has stem hollows where ants nest and tend flocks of scale insects. Named for their protective coverings, scale insects are vampires to the vegetable kingdom, piercing plants with tubular mouths to drink straight from the vascular system. As they imbibe, they secrete honeydew for ants to harvest and eat. Rounding out this mutualistic circle, the ants patrol their C. alliodora host for beetle larvae, caterpillars, and other herbivores, biting them until they leave.

 

Previous studies suggested that plants may invest more carbon in ant defense during water stress. This scenario is particularly taxing for C. alliodora, which drops its leaves during the dry season and must make its carbon stores last long enough to grow new leaves during the next rainy season. But ant colonies must be maintained year-round to ensure defense of leaves during the growing season, safeguarding the production of carbon to get the trees through the next dry season.

 

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Large-Scale Information Flow in Conscious and Unconscious States: an ECoG Study in Monkeys

Large-Scale Information Flow in Conscious and Unconscious States: an ECoG Study in Monkeys | Social Foraging | Scoop.it

Consciousness is an emergent property of the complex brain network. In order to understand how consciousness is constructed, neural interactions within this network must be elucidated. Previous studies have shown that specific neural interactions between the thalamus and frontoparietal cortices; frontal and parietal cortices; and parietal and temporal cortices are correlated with levels of consciousness. However, due to technical limitations, the network underlying consciousness has not been investigated in terms of large-scale interactions with high temporal and spectral resolution. In this study, we recorded neural activity with dense electrocorticogram (ECoG) arrays and used the spectral Granger causality to generate a more comprehensive network that relates to consciousness in monkeys. We found that neural interactions were significantly different between conscious and unconscious states in all combinations of cortical region pairs. Furthermore, the difference in neural interactions between conscious and unconscious states could be represented in 4 frequency-specific large-scale networks with unique interaction patterns: 2 networks were related to consciousness and showed peaks in alpha and beta bands, while the other 2 networks were related to unconsciousness and showed peaks in theta and gamma bands. Moreover, networks in the unconscious state were shared amongst 3 different unconscious conditions, which were induced either by ketamine and medetomidine, propofol, or sleep. Our results provide a novel picture that the difference between conscious and unconscious states is characterized by a switch in frequency-specific modes of large-scale communications across the entire cortex, rather than the cessation of interactions between specific cortical regions.

 

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Harder than Expected: Increased Conflict in Clearly Disadvantageous Delayed Choices in a Computer Game

Harder than Expected: Increased Conflict in Clearly Disadvantageous Delayed Choices in a Computer Game | Social Foraging | Scoop.it

When choosing between immediate and temporally delayed goods, people sometimes decide disadvantageously. Here, we aim to provide process-level insight into differences between individually determined advantageous and disadvantageous choices. Participants played a computer game, deciding between two different rewards of varying size and distance by moving an agent towards the chosen reward. We calculated individual models of advantageous choices and characterized the decision process by analyzing mouse movements. The larger amount of participants’ choices was classified as advantageous and the disadvantageous choices were biased towards choosing sooner/smaller rewards. The deflection of mouse movements indicated more conflict in disadvantageous choices compared with advantageous choices when the utilities of the options differed clearly. Further process oriented analysis revealed that disadvantageous choices were biased by a tendency for choice-repetition and an undervaluation of the value information in favour of the delay information, making rather simple choices harder than could be expected from the properties of the decision situation.

 

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Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion

Information Filtering in Sparse Online Systems: Recommendation via Semi-Local Diffusion | Social Foraging | Scoop.it

With the rapid growth of the Internet and overwhelming amount of information and choices that people are confronted with, recommender systems have been developed to effectively support users’ decision-making process in the online systems. However, many recommendation algorithms suffer from the data sparsity problem, i.e. the user-object bipartite networks are so sparse that algorithms cannot accurately recommend objects for users. This data sparsity problem makes many well-known recommendation algorithms perform poorly. To solve the problem, we propose a recommendation algorithm based on the semi-local diffusion process on the user-object bipartite network. The simulation results on two sparse datasets, Amazon and Bookcross, show that our method significantly outperforms the state-of-the-art methods especially for those small-degree users. Two personalized semi-local diffusion methods are proposed which further improve the recommendation accuracy. Finally, our work indicates that sparse online systems are essentially different from the dense online systems, so it is necessary to reexamine former algorithms and conclusions based on dense data in sparse systems.

 

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Speech Recognition in Natural Background Noise

Speech Recognition in Natural Background Noise | Social Foraging | Scoop.it

In the real world, human speech recognition nearly always involves listening in background noise. The impact of such noise on speech signals and on intelligibility performance increases with the separation of the listener from the speaker. The present behavioral experiment provides an overview of the effects of such acoustic disturbances on speech perception in conditions approaching ecologically valid contexts. We analysed the intelligibility loss in spoken word lists with increasing listener-to-speaker distance in a typical low-level natural background noise. The noise was combined with the simple spherical amplitude attenuation due to distance, basically changing the signal-to-noise ratio (SNR). Therefore, our study draws attention to some of the most basic environmental constraints that have pervaded spoken communication throughout human history. We evaluated the ability of native French participants to recognize French monosyllabic words (spoken at 65.3 dB(A), reference at 1 meter) at distances between 11 to 33 meters, which corresponded to the SNRs most revealing of the progressive effect of the selected natural noise (−8.8 dB to −18.4 dB). Our results showed that in such conditions, identity of vowels is mostly preserved, with the striking peculiarity of the absence of confusion in vowels. The results also confirmed the functional role of consonants during lexical identification. The extensive analysis of recognition scores, confusion patterns and associated acoustic cues revealed that sonorant, sibilant and burst properties were the most important parameters influencing phoneme recognition. . Altogether these analyses allowed us to extract a resistance scale from consonant recognition scores. We also identified specific perceptual consonant confusion groups depending of the place in the words (onset vs. coda). Finally our data suggested that listeners may access some acoustic cues of the CV transition, opening interesting perspectives for future studies.

 

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Simulating the Evolution of the Human Family: Cooperative Breeding Increases in Harsh Environments

Simulating the Evolution of the Human Family: Cooperative Breeding Increases in Harsh Environments | Social Foraging | Scoop.it

Verbal and mathematical models that consider the costs and benefits of behavioral strategies have been useful in explaining animal behavior and are often used as the basis of evolutionary explanations of human behavior. In most cases, however, these models do not account for the effects that group structure and cultural traditions within a human population have on the costs and benefits of its members' decisions. Nor do they consider the likelihood that cultural as well as genetic traits will be subject to natural selection. In this paper, we present an agent-based model that incorporates some key aspects of human social structure and life history. We investigate the evolution of a population under conditions of different environmental harshness and in which selection can occur at the level of the group as well as the level of the individual. We focus on the evolution of a socially learned characteristic related to individuals' willingness to contribute to raising the offspring of others within their family group. We find that environmental harshness increases the frequency of individuals who make such contributions. However, under the conditions we stipulate, we also find that environmental variability can allow groups to survive with lower frequencies of helpers. The model presented here is inevitably a simplified representation of a human population, but it provides a basis for future modeling work toward evolutionary explanations of human behavior that consider the influence of both genetic and cultural transmission of behavior.

 

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High-Frequency Stimulation of Excitable Cells and Networks

High-Frequency Stimulation of Excitable Cells and Networks | Social Foraging | Scoop.it

High-frequency (HF) stimulation has been shown to block conduction in excitable cells including neurons and cardiac myocytes. However, the precise mechanisms underlying conduction block are unclear. Using a multi-scale method, the influence of HF stimulation is investigated in the simplified FitzhHugh-Nagumo and biophysically-detailed Hodgkin-Huxley models. In both models, HF stimulation alters the amplitude and frequency of repetitive firing in response to a constant applied current and increases the threshold to evoke a single action potential in response to a brief applied current pulse. Further, the excitable cells cannot evoke a single action potential or fire repetitively above critical values for the HF stimulation amplitude. Analytical expressions for the critical values and thresholds are determined in the FitzHugh-Nagumo model. In the Hodgkin-Huxley model, it is shown that HF stimulation alters the dynamics of ionic current gating, shifting the steady-state activation, inactivation, and time constant curves, suggesting several possible mechanisms for conduction block. Finally, we demonstrate that HF stimulation of a network of neurons reduces the electrical activity firing rate, increases network synchronization, and for a sufficiently large HF stimulation, leads to complete electrical quiescence. In this study, we demonstrate a novel approach to investigate HF stimulation in biophysically-detailed ionic models of excitable cells, demonstrate possible mechanisms for HF stimulation conduction block in neurons, and provide insight into the influence of HF stimulation on neural networks.

 

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Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector

Synaptic Plasticity in Neural Networks Needs Homeostasis with a Fast Rate Detector | Social Foraging | Scoop.it

Hebbian changes of excitatory synapses are driven by and further enhance correlations between pre- and postsynaptic activities. Hence, Hebbian plasticity forms a positive feedback loop that can lead to instability in simulated neural networks. To keep activity at healthy, low levels, plasticity must therefore incorporate homeostatic control mechanisms. We find in numerical simulations of recurrent networks with a realistic triplet-based spike-timing-dependent plasticity rule (triplet STDP) that homeostasis has to detect rate changes on a timescale of seconds to minutes to keep the activity stable. We confirm this result in a generic mean-field formulation of network activity and homeostatic plasticity. Our results strongly suggest the existence of a homeostatic regulatory mechanism that reacts to firing rate changes on the order of seconds to minutes.

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Brain Connectivity Study Reveals Striking Differences Between Men and Women

Brain Connectivity Study Reveals Striking Differences Between Men and Women | Social Foraging | Scoop.it

A new brain connectivity study from Penn Medicine published today in the Proceedings of National Academy of Sciencesfound striking differences in the neural wiring of men and women that’s lending credence to some commonly-held beliefs about their behavior.

 

In one of the largest studies looking at the “connectomes” of the sexes, Ragini Verma, PhD, an associate professor in the department of Radiology at the Perelman School of Medicine at the University of Pennsylvania, and colleagues found greater neural connectivity from front to back and within one hemisphere in males, suggesting their brains are structured to facilitate connectivity between perception and coordinated action. In contrast, in females, the wiring goes between the left and right hemispheres, suggesting that they facilitate communication between the analytical and intuition.

 

“These maps show us a stark difference--and complementarity--in the architecture of the human brain that helps provide a potential neural basis as to why men excel at certain tasks, and women at others,” said Verma.

 

For instance, on average, men are more likely better at learning and performing a single task at hand, like cycling or navigating directions, whereas women have superior memory and social cognition skills, making them more equipped for multitasking and creating solutions that work for a group. They have a mentalistic approach, so to speak.

 

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Amazon Is Experimenting With Autonomous Flying Delivery Drones

Amazon Is Experimenting With Autonomous Flying Delivery Drones | Social Foraging | Scoop.it

Between launching a charity-friendly buying program, announcing Sunday deliveries, and gearing up for the first wave of frenzied holiday shoppers, Amazon has been busy these past few weeks. But that didn't stop CEO Jeff Bezos from spending a decent chunk of time talking to Charlie Rose on 60 Minutes about something, well, new.

 

60 Minutes has been more than happy to tease the unveiling with a clip of Bezos leading Rose into a room to show him something that elicited an “Oh my God!” from the veteran TV journo. The exclamation seemed to stem from a place of pleasure rather than worry, but the segment just aired and the truth is out.

 

So what did Bezos have up his proverbial sleeves? Amazon Prime Air drones that could feasibly be used as autonomous delivery vehicles. To hear the chief executive tell it, those electric drones - or “octocopters” as he referred to them - could make for delivery times as low as 30 minutes. Naturally, the size of those drones means there's a strict upper limit to how much cargo they can carry, but Bezos says they can carry packages of up to five pounds for round trips as long as 10 miles. Thankfully for Amazon, that means nearly 86 percent of the items that it carries can be lashed onto one of its sky-bound couriers.

 

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StartupNectar-The Biommimicry Business Incubator

StartupNectar-The Biommimicry Business Incubator | Social Foraging | Scoop.it

"StartupNectar enables early-stage, biomimicry-based ventures to access resources and gain traction in the marketplace. The incubation model is informed by nature’s strategies for creating conditions conducive to life".


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Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance

Average Is Optimal: An Inverted-U Relationship between Trial-to-Trial Brain Activity and Behavioral Performance | Social Foraging | Scoop.it

It is well known that even under identical task conditions, there is a tremendous amount of trial-to-trial variability in both brain activity and behavioral output. Thus far the vast majority of event-related potential (ERP) studies investigating the relationship between trial-to-trial fluctuations in brain activity and behavioral performance have only tested a monotonic relationship between them. However, it was recently found that across-trial variability can correlate with behavioral performance independent of trial-averaged activity. This finding predicts a U- or inverted-U- shaped relationship between trial-to-trial brain activity and behavioral output, depending on whether larger brain variability is associated with better or worse behavior, respectively. Using a visual stimulus detection task, we provide evidence from human electrocorticography (ECoG) for an inverted-U brain-behavior relationship: When the raw fluctuation in broadband ECoG activity is closer to the across-trial mean, hit rate is higher and reaction times faster. Importantly, we show that this relationship is present not only in the post-stimulus task-evoked brain activity, but also in the pre-stimulus spontaneous brain activity, suggesting anticipatory brain dynamics. Our findings are consistent with the presence of stochastic noise in the brain. They further support attractor network theories, which postulate that the brain settles into a more confined state space under task performance, and proximity to the targeted trajectory is associated with better performance.

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Interdisciplinary Symposium on Complex Systems 2014

Interdisciplinary Symposium on Complex Systems (ISCS'14)

Center for the Study of Complex Systems (CSDC) 
Department of Physics and Astronomy
University of Florence, Florence, Italy
September 15 - 19, 2014

https://sites.google.com/site/complexsystems2014/


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Water Stress Strengthens Mutualism Among Ants, Trees, and Scale Insects

Water Stress Strengthens Mutualism Among Ants, Trees, and Scale Insects | Social Foraging | Scoop.it

Abiotic environmental variables strongly affect the outcomes of species interactions. For example, mutualistic interactions between species are often stronger when resources are limited. The effect might be indirect: water stress on plants can lead to carbon stress, which could alter carbon-mediated plant mutualisms. In mutualistic ant–plant symbioses, plants host ant colonies that defend them against herbivores. Here we show that the partners' investments in a widespread ant–plant symbiosis increase with water stress across 26 sites along a Mesoamerican precipitation gradient. At lower precipitation levels, Cordia alliodora trees invest more carbon in Azteca ants via phloem-feeding scale insects that provide the ants with sugars, and the ants provide better defense of the carbon-producing leaves. Under water stress, the trees have smaller carbon pools. A model of the carbon trade-offs for the mutualistic partners shows that the observed strategies can arise from the carbon costs of rare but extreme events of herbivory in the rainy season. Thus, water limitation, together with the risk of herbivory, increases the strength of a carbon-based mutualism.

 

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Modelling the Effects of Prey Size and Distribution on Prey Capture Rates of Two Sympatric Marine Predators

Modelling the Effects of Prey Size and Distribution on Prey Capture Rates of Two Sympatric Marine Predators | Social Foraging | Scoop.it

Understanding how prey capture rates are influenced by feeding ecology and environmental conditions is fundamental to assessing anthropogenic impacts on marine higher predators. We compared how prey capture rates varied in relation to prey size, prey patch distribution and prey density for two species of alcid, common guillemot (Uria aalge) and razorbill (Alca torda) during the chick-rearing period. We developed a Monte Carlo approach parameterised with foraging behaviour from bird-borne data loggers, observations of prey fed to chicks, and adult diet from water-offloading, to construct a bio-energetics model. Our primary goal was to estimate prey capture rates, and a secondary aim was to test responses to a set of biologically plausible environmental scenarios. Estimated prey capture rates were 1.5±0.8 items per dive (0.8±0.4 and 1.1±0.6 items per minute foraging and underwater, respectively) for guillemots and 3.7±2.4 items per dive (4.9±3.1 and 7.3±4.0 items per minute foraging and underwater, respectively) for razorbills. Based on species' ecology, diet and flight costs, we predicted that razorbills would be more sensitive to decreases in 0-group sandeel (Ammodytes marinus) length (prediction 1), but guillemots would be more sensitive to prey patches that were more widely spaced (prediction 2), and lower in prey density (prediction 3). Estimated prey capture rates increased non-linearly as 0-group sandeel length declined, with the slope being steeper in razorbills, supporting prediction 1. When prey patches were more dispersed, estimated daily energy expenditure increased by a factor of 3.0 for guillemots and 2.3 for razorbills, suggesting guillemots were more sensitive to patchier prey, supporting prediction 2. However, both species responded similarly to reduced prey density (guillemot expenditure increased by 1.7; razorbill by 1.6), thus not supporting prediction 3. This bio-energetics approach complements other foraging models in predicting likely impacts of environmental change on marine higher predators dependent on species-specific foraging ecologies.

 

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Are Accuracy and Reaction Time Affected via Different Processes?

Are Accuracy and Reaction Time Affected via Different Processes? | Social Foraging | Scoop.it

A recent study by van Ede et al. (2012) shows that the accuracy and reaction time in humans of tactile perceptual decisions are affected by an attentional cue via distinct cognitive and neural processes. These results are controversial as they undermine the notion that accuracy and reaction time are influenced by the same latent process that underlie the decision process. Typically, accumulation-to-bound models (like the drift diffusion model) can explain variability in both accuracy and reaction time by a change of a single parameter. To elaborate the findings of van Ede et al., we fitted the drift diffusion model to their behavioral data. Results show that both changes in accuracy and reaction time can be partly explained by an increase in the accumulation of sensory evidence (drift rate). In addition, a change in non-decision time is necessary to account for reaction time changes as well. These results provide a subtle explanation of how the underlying dynamics of the decision process might give rise to differences in both the speed and accuracy of perceptual tactile decisions. Furthermore, our analyses highlight the importance of applying a model-based approach, as the observed changes in the model parameters might be ecologically more valid, since they have an intuitive relationship with the neuronal processes underlying perceptual decision making.

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An Evolutionary Model of Cooperation, Fairness and Altruistic Punishment in Public Good Games

An Evolutionary Model of Cooperation, Fairness and Altruistic Punishment in Public Good Games | Social Foraging | Scoop.it

We identify and explain the mechanisms that account for the emergence of fairness preferences and altruistic punishment in voluntary contribution mechanisms by combining an evolutionary perspective together with an expected utility model. We aim at filling a gap between the literature on the theory of evolution applied to cooperation and punishment, and the empirical findings from experimental economics. The approach is motivated by previous findings on other-regarding behavior, the co-evolution of culture, genes and social norms, as well as bounded rationality. Our first result reveals the emergence of two distinct evolutionary regimes that force agents to converge either to a defection state or to a state of coordination, depending on the predominant set of self- or other-regarding preferences. Our second result indicates that subjects in laboratory experiments of public goods games with punishment coordinate and punish defectors as a result of an aversion against disadvantageous inequitable outcomes. Our third finding identifies disadvantageous inequity aversion as evolutionary dominant and stable in a heterogeneous population of agents endowed initially only with purely self-regarding preferences. We validate our model using previously obtained results from three independently conducted experiments of public goods games with punishment.

 

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An Algorithm for Network-Based Gene Prioritization That Encodes Knowledge Both in Nodes and in Links

An Algorithm for Network-Based Gene Prioritization That Encodes Knowledge Both in Nodes and in Links | Social Foraging | Scoop.it

Candidate gene prioritization aims to identify promising new genes associated with a disease or a biological process from a larger set of candidate genes. In recent years, network-based methods – which utilize a knowledge network derived from biological knowledge – have been utilized for gene prioritization. Biological knowledge can be encoded either through the network's links or nodes. Current network-based methods can only encode knowledge through links. This paper describes a new network-based method that can encode knowledge in links as well as in nodes.

Results

We developed a new network inference algorithm called the Knowledge Network Gene Prioritization (KNGP) algorithm which can incorporate both link and node knowledge. The performance of the KNGP algorithm was evaluated on both synthetic networks and on networks incorporating biological knowledge. The results showed that the combination of link knowledge and node knowledge provided a significant benefit across 19 experimental diseases over using link knowledge alone or node knowledge alone.

Conclusions

The KNGP algorithm provides an advance over current network-based algorithms, because the algorithm can encode both link and node knowledge. We hope the algorithm will aid researchers with gene prioritization.

 

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Human Sensorimotor Communication: A Theory of Signaling in Online Social Interactions

Human Sensorimotor Communication: A Theory of Signaling in Online Social Interactions | Social Foraging | Scoop.it

Although the importance of communication is recognized in several disciplines, it is rarely studied in the context of online social interactions and joint actions. During online joint actions, language and gesture are often insufficient and humans typically use non-verbal, sensorimotor forms of communication to send coordination signals. For example, when playing volleyball, an athlete can exaggerate her movements to signal her intentions to her teammates (say, a pass to the right) or to feint an adversary. Similarly, a person who is transporting a table together with a co-actor can push the table in a certain direction to signal where and when he intends to place it. Other examples of “signaling” are over-articulating in noisy environments and over-emphasizing vowels in child-directed speech. In all these examples, humans intentionally modify their action kinematics to make their goals easier to disambiguate. At the moment no formal theory exists of these forms of sensorimotor communication and signaling. We present one such theory that describes signaling as a combination of a pragmatic and a communicative action, and explains how it simplifies coordination in online social interactions. We cast signaling within a “joint action optimization” framework in which co-actors optimize the success of their interaction and joint goals rather than only their part of the joint action. The decision of whether and how much to signal requires solving a trade-off between the costs of modifying one’s behavior and the benefits in terms of interaction success. Signaling is thus an intentional strategy that supports social interactions; it acts in concert with automatic mechanisms of resonance, prediction, and imitation, especially when the context makes actions and intentions ambiguous and difficult to read. Our theory suggests that communication dynamics should be studied within theories of coordination and interaction rather than only in terms of the maximization of information transmission.

 

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Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems

Agent-Based Model with Asymmetric Trading and Herding for Complex Financial Systems | Social Foraging | Scoop.it

For complex financial systems, the negative and positive return-volatility correlations, i.e., the so-called leverage and anti-leverage effects, are particularly important for the understanding of the price dynamics. However, the microscopic origination of the leverage and anti-leverage effects is still not understood, and how to produce these effects in agent-based modeling remains open. On the other hand, in constructing microscopic models, it is a promising conception to determine model parameters from empirical data rather than from statistical fitting of the results.

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Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks

Modeling Integrated Cellular Machinery Using Hybrid Petri-Boolean Networks | Social Foraging | Scoop.it

The behavior and phenotypic changes of cells are governed by a cellular circuitry that represents a set of biochemical reactions. Based on biological functions, this circuitry is divided into three types of networks, each encoding for a major biological process: signal transduction, transcription regulation, and metabolism. This division has generally enabled taming computational complexity dealing with the entire system, allowed for using modeling techniques that are specific to each of the components, and achieved separation of the different time scales at which reactions in each of the three networks occur. Nonetheless, with this division comes loss of information and power needed to elucidate certain cellular phenomena. Within the cell, these three types of networks work in tandem, and each produces signals and/or substances that are used by the others to process information and operate normally. Therefore, computational techniques for modeling integrated cellular machinery are needed. In this work, we propose an integrated hybrid model (IHM) that combines Petri nets and Boolean networks to model integrated cellular networks. Coupled with a stochastic simulation mechanism, the model simulates the dynamics of the integrated network, and can be perturbed to generate testable hypotheses. Our model is qualitative and is mostly built upon knowledge from the literature and requires fine-tuning of very few parameters. We validated our model on two systems: the transcriptional regulation of glucose metabolism in human cells, and cellular osmoregulation in S. cerevisiae. The model produced results that are in very good agreement with experimental data, and produces valid hypotheses. The abstract nature of our model and the ease of its construction makes it a very good candidate for modeling integrated networks from qualitative data. The results it produces can guide the practitioner to zoom into components and interconnections and investigate them using such more detailed mathematical models.

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Self-Replicating USBs Spread Software Faster than an Internet Connection

Self-Replicating USBs Spread Software Faster than an Internet Connection | Social Foraging | Scoop.it

Getting hold of software, even freeware, is a significant challenge in the developing world. Bandwidth is such a precious commodity in these places that even modest downloads are beyond the budget of most people. 

 

The map above reveals the problem. It shows the density of IPv4 addresses around the world, a useful proxy for the density of internet servers.  Clearly the internet is not yet evenly distributed.

And therein lies the problem. In most parts of the world, a free operating system that is several gigabytes in size will take too long and cost too much to download. Consequently, much of the best freeware simply hasn’t spread to those who would benefit from it most.

 

That looks set to change, at least in part, thanks to some neat work by Thierry Monteil at the Université Montpellier II in France. This guy has devised a cheap and simple way to transmit large software packages without using the internet and at rates that eclipse all but the best internet connections.

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Listening to the World's Oceans: Searching for Marine Mammals by Detecting and Classifying Terabytes of Bioacoustic Data...

Collective Dynamics of Complex Systems Research Group Seminar Series November 20, 2013 Peter Dugan (Bioacoustics, Cornell University) "Listening to the World's…

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Sudden Progress on Prime Number Problem Has Mathematicians Buzzing

Sudden Progress on Prime Number Problem Has Mathematicians Buzzing | Social Foraging | Scoop.it

On May 13, an obscure mathematician — one whose talents had gone so unrecognized that he had worked at a Subway restaurant to make ends meet — garnered worldwide attention and accolades from the mathematics community for settling a long-standing open question about prime numbers, those numbers divisible by only one and themselves. Yitang Zhang, a lecturer at the University of New Hampshire, showed that even though primes get increasingly rare as you go further out along the number line, you will never stop finding pairs of primes separated by at most 70 million. His finding was the first time anyone had managed to put a finite bound on the gaps between prime numbers, representing a major leap toward proving the centuries-old twin primes conjecture, which posits that there are infinitely many pairs of primes separated by only two (such as 11 and 13).

 

In the months that followed, Zhang found himself caught up in a whirlwind of activity and excitement: He has lectured on his work at many of the nation’s preeminent universities, has received offers of jobs from top institutions in China and Taiwan and a visiting position at the Institute for Advanced Study in Princeton, N.J., and has been told that he will be promoted to full professor at the University of New Hampshire.

Meanwhile, Zhang’s work raised a question: Why 70 million? There is nothing magical about that number — it served Zhang’s purposes and simplified his proof. Other mathematicians quickly realized that it should be possible to push this separation bound quite a bit lower, although not all the way down to two.

 

By the end of May, mathematicians had uncovered simple tweaks to Zhang’s argument that brought the bound below 60 million. A May 30 blog post by Scott Morrison of the Australian National University in Canberra ignited a firestorm of activity, as mathematicians vied to improve on this number, setting one record after another. By June 4, Terence Tao of the University of California, Los Angeles, a winner of the Fields Medal, mathematics’ highest honor, had created a “Polymath project,” an open, online collaboration to improve the bound that attracted dozens of participants.

 

For weeks, the project moved forward at a breathless pace. “At times, the bound was going down every thirty minutes,” Tao recalled. By July 27, the team had succeeded in reducing the proven bound on prime gaps from 70 million to 4,680.

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