Using Instagram data from 166 individuals, we applied machine learning tools to successfully identify markers of depression. Statistical features were computationally extracted from 43,950 participant Instagram photos, using color analysis, metadata components, and algorithmic face detection. Resulting models outperformed general practitioners' average diagnostic success rate for depression. These results held even when the analysis was restricted to posts made before depressed individuals were first diagnosed. Photos posted by depressed individuals were more likely to be bluer, grayer, and darker. Human ratings of photo attributes (happy, sad, etc.) were weaker predictors of depression, and were uncorrelated with computationally-generated features. These findings suggest new avenues for early screening and detection of mental illness.
Instagram photos reveal predictive markers of depression Andrew G. Reece, Christopher M. Danforth
We study the influence of selfish vs. polite behaviours on the dynamics of a pedestrian evacuation through a narrow exit. To this end, experiments involving about 80 participants with distinct prescribed behaviours are performed; reinjection of participants into the setup allowed us to improve the statistics. Notwithstanding the fluctuations in the instantaneous flow rate, we find that a stationary regime is almost immediately reached. The average flow rate increases monotonically with the fraction c\_s of vying (selfish) pedestrians, which corresponds to a "faster-is-faster" effect in our experimental conditions; it is also positively correlated with the average density of pedestrians in front of the door, up to nearly close-packing. At large c\_s , the flow displays marked intermittency, with bursts of quasi-simultaneous escapes. In addition to these findings, we wonder whether the effect of cooperation is specific to systems of intelligent beings, or whether it can be reproduced by a purely mechanical surrogate. To this purpose, we consider a bidimensional granular flow through an orifice in which some grains are made "cooperative" by repulsive magnetic interactions which impede their mutual collisions.
Influence of selfish and polite behaviours on a pedestrian evacuation through a narrow exit: A quantitative characterisation Alexandre Nicolas, Sebastián Bouzat, Marcelo Kuperman
This short paper uses the recently presented idea that the fundamental haploid-diploid lifecycle of all eukaryote organisms exploits a rudimentary form of the Baldwin effect. The general approach presented here differs from all previous known work using diploid representations within evolutionary computation. The role of recombination is also changed from that previously considered in both natural and artificial evolution under the new theory. Using the NK model of fitness landscapes and the RBNK model of gene regulatory networks it is here shown that varying landscape ruggedness varies the benefit of a haploid-diploid approach in comparison to the traditional haploid representation in both cases.
Mobile dating apps have become a popular means to meet potential partners. Although several exist, one recent addition stands out amongst all others. Tinder presents its users with pictures of people geographically nearby, whom they can either like or dislike based on first impressions. If two users like each other, they are allowed to initiate a conversation via the chat feature. In this paper we use a set of curated profiles to explore the behaviour of men and women in Tinder. We reveal differences between the way men and women interact with the app, highlighting the strategies employed. Women attain large numbers of matches rapidly, whilst men only slowly accumulate matches. To expand on our findings, we collect survey data to understand user intentions on Tinder. Most notably, our results indicate that a little effort in grooming profiles, especially for male users, goes a long way in attracting attention.
A First Look at User Activity on Tinder Gareth Tyson, Vasile C. Perta, Hamed Haddadi, Michael C. Seto
In the last years, network scientists have directed their interest to the multi-layer character of real-world systems, and explicitly considered the structural and dynamical organization of graphs made of diverse layers between its constituents. Most complex systems include multiple subsystems and layers of connectivity and, in many cases, the interdependent components of systems interact through many different channels. Such a new perspective is indeed found to be the adequate representation for a wealth of features exhibited by networked systems in the real world. The contributions presented in this Focus Issue cover, from different points of view, the many achievements and still open questions in the field of multi-layer networks, such as: new frameworks and structures to represent and analyze heterogeneous complex systems, different aspects related to synchronization and centrality of complex networks, interplay between layers, and applications to logistic, biological, social, and technological fields.
Introduction to Focus Issue: Complex Dynamics in Networks, Multilayered Structures and Systems Stefano Boccaletti, Regino Criado, Miguel Romance and Joaquín J. Torres
Predictive models for human mobility have important applications in many fields such as traffic control, ubiquitous computing and contextual advertisement. The predictive performance of models in literature varies quite broadly, from as high as 93% to as low as under 40%. In this work we investigate which factors influence the accuracy of next-place prediction, using a high-precision location dataset of more than 400 users for periods between 3 months and one year. We show that it is easier to achieve high accuracy when predicting the time-bin location than when predicting the next place. Moreover we demonstrate how the temporal and spatial resolution of the data can have strong influence on the accuracy of prediction. Finally we uncover that the exploration of new locations is an important factor in human mobility, and we measure that on average 20-25% of transitions are to new places, and approx. 70% of locations are visited only once. We discuss how these mechanisms are important factors limiting our ability to predict human mobility.
Understanding Predictability and Exploration in Human Mobility Andrea Cuttone, Sune Lehmann, Marta C. González
The living-together of distinct organisms in a single termite nest along with the termite builder colony, is emblematic in its ecological and evolutionary significance. On top of preserving biodiversity, these interspecific and intraspecific symbioses provide useful examples of interindividual associations thought to underly transitions in organic evolution. Being interindividual in nature, such processes may involve emergent phenomena and hence call for analytical solutions provided by computing tools and modelling, as opposed to classical biological methods of analysis. Here we provide selected examples of such solutions, showing that termite studies may profit from a symbiotic-like link with computing science to open up wide and new research avenues in ecology and evolution.
Fruitful symbioses between termites and computers Og DeSouza, Elio Tuci, Octavio Miramontes
Systems Biology is a young and rapidly evolving research field, which combines experimental techniques and mathematical modeling in order to achieve a mechanistic understanding of processes underlying the regulation and evolution of living systems. Systems Biology is often associated with an Engineering approach: The purpose is to formulate a data-rich, detailed simulation model that allows to perform numerical (‘in silico’) experiments and then draw conclusions about the biological system. While methods from Engineering may be an appropriate approach to extending the scope of biological investigations to experimentally inaccessible realms and to supporting data-rich experimental work, it may not be the best strategy in a search for design principles of biological systems and the fundamental laws underlying Biology. Physics has a long tradition of characterizing and understanding emergent collective behaviors in systems of interacting units and searching for universal laws. Therefore, it is natural that many concepts used in Systems Biology have their roots in Physics. With an emphasis on Theoretical Physics, we will here review the ‘Physics core’ of Systems Biology, show how some success stories in Systems Biology can be traced back to concepts developed in Physics, and discuss how Systems Biology can further benefit from its Theoretical Physics foundation.
Noise is widely understood to be something that interferes with a signal or process. Thus, it is generally thought to be destructive, obscuring signals and interfering with function. However, early in the 20th century, mechanical engineers found that mechanisms inducing additional vibration in mechanical systems could prevent sticking and hysteresis. This so-called "dither" noise was later introduced in an entirely different context at the advent of digital information transmission and recording in the early 1960s. Ironically, the addition of noise allows one to preserve information that would otherwise be lost when the signal or image is digitized. As we shall see, the benefits of added noise in these contexts are closely related to the phenomenon which has come to be known as stochastic resonance, the original version of which appealed to noise to explain how small periodic fluctuations in the eccentricity of the earth's orbit might be amplified in such a way as to bring about the observed periodic transitions in climate from ice age to temperate age and back. These noise-induced transitions have since been invoked to explain a wide array of biological phenomena, including the foraging and tracking behavior of ants. Many biological phenomena, from foraging to gene expression, are noisy, involving an element of randomness. In this paper, we illustrate the general principles behind dithering and stochastic resonance using examples from image processing, and then show how the constructive use of noise can carry over to systems found in nature.
Noise and Function Steven Weinstein, Theodore P. Pavlic
Urban structures encompass settlements, characterized by the spatial distribution of built-up areas, but also transportation structures, to connect these built-up areas. These two structures are very different in their origin and function, fulfilling complementary needs: (i) to access space, and (ii) to occupy space. Their evolution cannot be understood by looking at the dynamics of urban aggregations and transportation systems separately. Instead, existing built-up areas feed back on the further development of transportation structures, and the availability of the latter feeds back on the future growth of urban aggregations. To model this co-evolution, we propose an agent-based approach that builds on existing agent-based models for the evolution of trail systems and of urban settlements. The key element in these separate approaches is a generalized communication of agents by means of an adaptive landscape. This landscape is only generated by the agents, but once it exists, it feeds back on their further actions. The emerging trail system or urban aggregation results as a self-organized structure from these collective interactions. In our co-evolutionary approach, we couple these two separate models by means of meta-agents that represent humans with their different demands for housing and mobility. We characterize our approach as a statistical ensemble approach, which allows to capture the potential of urban evolution in a bottom-up manner, but can be validated against empirical observations.
A conceptual approach to model co-evolution of urban structures Frank Schweitzer, Vahan Nanumyan
We study the Braess paradox in the transport network as originally proposed by Braess with totally asymmetric exclusion processes (TASEPs) on the edges. The Braess paradox describes the counterintuitive situation where adding an additional edge to a road network leads to a user optimum with higher traveltimes for all network users. Traveltimes on the TASEPs are nonlinear in the density and jammed states can occur due to the microscopic exclusion principle. Furthermore the individual edges influence each other. This leads to a much more realistic description of traffic-like transport on the network than in previously studied linear macroscopic mathematical models. Furthermore the stochastic dynamics allows to explore the effects of fluctuations on the network performance. We observe that for low densities the added edge leads to lower traveltimes. For slightly higher densities the Braess paradox in its classical sense occurs in a small density regime. In a large regime of intermediate densities strong fluctuations in the traveltimes dominate the system's behaviour. These fluctuations are due to links that are in a domain wall or coexistence phase. At high densities the added link leads to lower traveltimes. We present a phase diagram predicting in which state the system will be, depending on the global density and crucial length ratios.
The Braess Paradox in a network of totally asymmetric exclusion processes Stefan Bittihn, Andreas Schadschneider
With big data, we can multiply our options and filter out things we don’t want to see. But there is much to be said for making discoveries through pure serendipity: contingency and randomness often furnish the transformational or counterintuitive ideas that propel humanity forward.
We propose a kind of Extended Intelligence (EI), understanding intelligence as a fundamentally distributed phenomenon. As we develop increasingly powerful tools to process information and network that processing, aren’t we just adding new pieces to the EI that every actor in the network is a part of?
The mesoscopic level of brain organization, describing the organization and dynamics of small circuits of neurons including from few tens to few thousands, has recently received considerable experimental attention. It is useful for describing small neural systems of invertebrates, and in mammalian neural systems it is often seen as a middle ground that is fundamental to link single neuron activity to complex functions and behavior. However, and somewhat counter-intuitively, the behavior of neural networks of small and intermediate size can be much more difficult to study mathematically than that of large networks, and appropriate mathematical methods to study the dynamics of such networks have not been developed yet. Here we consider a model of a network of firing-rate neurons with arbitrary finite size, and we study its local bifurcations using an analytical approach. This analysis, complemented by numerical studies for both the local and global bifurcations, shows the emergence of strong and previously unexplored finite-size effects that are particularly hard to detect in large networks. This study advances the tools available for the comprehension of finite-size neural circuits, going beyond the insights provided by the mean-field approximation and the current techniques for the quantification of finite-size effects.
Fasoli D, Cattani A, Panzeri S (2016) The Complexity of Dynamics in Small Neural Circuits. PLoS Comput Biol 12(8): e1004992. doi:10.1371/journal.pcbi.1004992
Fecal transplants are increasingly utilized for treatment of recurrent infections (i.e., Clostridium difficile) in the human gut and as a general research tool for gain-of-function experiments (i.e., gavage of fecal pellets) in animal models. Changes observed in the recipient's biology are routinely attributed to bacterial cells in the donor feces (~10^11 per gram of human wet stool). Here, we examine the literature and summarize findings on the composition of fecal matter in order to raise cautiously the profile of its multipart nature. In addition to viable bacteria, which may make up a small fraction of total fecal matter, other components in unprocessed human feces include colonocytes (~10^7 per gram of wet stool), archaea (~10^8 per gram of wet stool), viruses (~108 per gram of wet stool), fungi (~10^6 per gram of wet stool), protists, and metabolites. Thus, while speculative at this point and contingent on the transplant procedure and study system, nonbacterial matter could contribute to changes in the recipient's biology. There is a cautious need for continued reductionism to separate out the effects and interactions of each component.
Bojanova DP, Bordenstein SR (2016) Fecal Transplants: What Is Being Transferred? PLoS Biol 14(7): e1002503. doi:10.1371/journal.pbio.1002503
Rule 1: Use GitHub to Track Your Projects Rule 2: GitHub for Single Users, Teams, and Organizations Rule 3: Developing and Collaborating on New Features: Branching and Forking Rule 4: Naming Branches and Commits: Tags and Semantic Versions Rule 5: Let GitHub Do Some Tasks for You: Integrate Rule 6: Let GitHub Do More Tasks for You: Automate Rule 7: Use GitHub to Openly and Collaboratively Discuss, Address, and Close Issues Rule 8: Make Your Code Easily Citable, and Cite Source Code! Rule 9: Promote and Discuss Your Projects: Web Page and More Rule 10: Use GitHub to Be Social: Follow and Watch
Perez-Riverol Y, Gatto L, Wang R, Sachsenberg T, Uszkoreit J, Leprevost FdV, et al. (2016) Ten Simple Rules for Taking Advantage of Git and GitHub. PLoS Comput Biol 12(7): e1004947. doi:10.1371/journal.pcbi.1004947
Herbert Simon's classic rich-gets-richer model is one of the simplest empirically supported mechanisms capable of generating heavy-tail size distributions for complex systems. Simon argued analytically that a population of flavored elements growing by either adding a novel element or randomly replicating an existing one would afford a distribution of group sizes with a power-law tail. Here, we show that, in fact, Simon's model does not produce a simple power law size distribution as the initial element has a dominant first-mover advantage, and will be overrepresented by a factor proportional to the inverse of the innovation probability. The first group's size discrepancy cannot be explained away as a transient of the model, and may therefore be many orders of magnitude greater than expected. We demonstrate how Simon's analysis was correct but incomplete, and expand our alternate analysis to quantify the variability of long term rankings for all groups. We find that the expected time for a first replication is infinite, and show how an incipient group must break the mechanism to improve their odds of success. Our findings call for a reexamination of preceding work invoking Simon's model and provide a revised understanding going forward.
Simon's fundamental rich-gets-richer model entails a dominant first-mover advantage Peter Sheridan Dodds, David Rushing Dewhurst, Fletcher F. Hazlehurst, Colin M. Van Oort, Lewis Mitchell, Andrew J. Reagan, Jake Ryland Williams, Christopher M. Danforth
What do societies, the Internet, and the human brain have in common? The immediate answer might be "not that much", but in reality they are all examples of complex relational systems, whose emerging behaviours are largely determined by the non-trivial networks of interactions among their constituents, namely individuals, computers, or neurons. In the last two decades, network scientists have proposed models of increasing complexity to better understand real-world systems. Only recently we have realised that multiplexity, i.e. the coexistence of several types of interactions among the constituents of a complex system, is responsible for substantial qualitative and quantitative differences in the type and variety of behaviours that a complex system can exhibit. As a consequence, multilayer and multiplex networks have become a hot topic in complexity science. Here we provide an overview of some of the measures proposed so far to characterise the structure of multiplex networks, and a selection of models aiming at reproducing those structural properties and at quantifying their statistical significance. Focusing on a subset of relevant topics, this brief review is a quite comprehensive introduction to the most basic tools for the analysis of multiplex networks observed in the real-world. The wide applicability of multiplex networks as a framework to model complex systems in different fields, from biology to social sciences, and the colloquial tone of the paper will make it an interesting read for researchers working on both theoretical and experimental analysis of networked systems.
The new challenges of multiplex networks: measures and models Federico Battiston, Vincenzo Nicosia, Vito Latora
The concept of city or urban resilience has emerged as one of the key challenges for the next decades. As a consequence, institutions like the United Nations or Rockefeller Foundation have embraced initiatives that increase or improve it. These efforts translate into funded programs both for action on the ground and to develop quantification of resilience, under the for of an index. Ironically, on the academic side there is no clear consensus regarding how resilience should be quantified, or what it exactly refers to in the urban context. Here we attempt to link both extremes providing an example of how to exploit large, publicly available, worldwide urban datasets, to produce objective insight into one of the possible dimensions of urban resilience. We do so via well-established methods in complexity science, such as percolation theory --which has a long tradition at providing valuable information on the vulnerability in complex systems. Our findings uncover large differences among studied cities, both regarding their infrastructural fragility and the imbalances in the distribution of critical services.
Robustness and Resilience of cities around the world Sofiane Abbar, Tahar Zanouda, Javier Borge-Holthoefer
Despite the traditional focus of network science on static networks, most networked systems of scientific interest are characterized by temporal links. By disrupting the paths, link temporality has been shown to frustrate many dynamical processes on networks, from information spreading to accessibility. Considering the ubiquity of temporal networks in nature, we must ask: Are there any advantages of the networks' temporality? Here we develop an analytical framework to explore the control properties of temporal networks, arriving at the counterintuitive conclusion that temporal networks, compared to their static (i.e. aggregated) counterparts, reach controllability faster, demand orders of magnitude less control energy, and the control trajectories, through which the system reaches its final states, are significantly more compact than those characterizing their static counterparts. The combination of analytical, numerical and empirical results demonstrates that temporality ensures a degree of flexibility that would be unattainable in static networks, significantly enhancing our ability to control them.
The fundamental advantages of temporal networks Aming Li, Sean P. Cornelius, Yang-Yu Liu, Long Wang, Albert-László Barabási
In response to failures of central planning, the Chinese government has experimented not only with free-market trade zones, but with allowing non-profit foundations to operate in a decentralized fashion. A network study shows how these foundations have connected together by sharing board members, in a structural parallel to what is seen in corporations in the United States. This board interlock leads to the emergence of an elite group with privileged network positions. While the presence of government officials on non-profit boards is widespread, state officials are much less common in a subgroup of foundations that control just over half of all revenue in the network. This subgroup, associated with business elites, not only enjoys higher levels of within-elite links, but even preferentially excludes government officials from the nodes with higher degree. The emergence of this structurally autonomous sphere is associated with major political and social events in the state-society relationship.
State power and elite autonomy: The board interlock network of Chinese non-profits Ji Ma, Simon DeDeo
The emerging field of Nominal Computation Theory is concerned with the theory of Nominal Sets and its applications to Computer Science. We investigate here the impact of nominal sets on the definition of Cellular Automata and on their computational capabilities, with a special focus on the emergent behavioural properties of this new model and their significance in the context of computation-oriented interpretations of physical phenomena. A preliminary investigation of the relations between Nominal Cellular Automata and Wolfram's Elementary Cellular Automata is also carried out.
As Physics did in previous centuries, there is currently a common dream of extracting generic laws of nature in economics, sociology, neuroscience, by focalising the description of phenomena to a minimal set of variables and parameters, linked together by causal equations of evolution whose structure may reveal hidden principles. This requires a huge reduction of dimensionality (number of degrees of freedom) and a change in the level of description. Beyond the mere necessity of developing accurate techniques affording this reduction, there is the question of the correspondence between the initial system and the reduced one. In this paper, we offer a perspective towards a common framework for discussing and understanding multi-level systems exhibiting structures at various spatial and temporal levels. We propose a common foundation and illustrate it with examples from different fields. We also point out the difficulties in constructing such a general setting and its limitations.
Perspectives on Multi-Level Dynamics Fatihcan M. Atay, Sven Banisch, Philippe Blanchard, Bruno Cessac, Eckehard Olbrich
We examine the hypothesis, that short-term synaptic plasticity (STSP) may generate self-organized motor patterns. We simulated sphere-shaped autonomous robots, within the LPZRobots simulation package, containing three weights moving along orthogonal internal rods. The position of a weight is controlled by a single neuron receiving excitatory input from the sensor, measuring its actual position, and inhibitory inputs from the other two neurons. The inhibitory connections are transiently plastic, following physiologically inspired STSP-rules. We find that a wide palette of motion patterns are generated through the interaction of STSP, robot, and environment (closed-loop configuration), including various forward meandering and circular motions, together with chaotic trajectories. The observed locomotion is robust with respect to additional interactions with obstacles. In the chaotic phase the robot is seemingly engaged in actively exploring its environment. We believe that our results constitute a concept of proof that transient synaptic plasticity, as described by STSP, may potentially be important for the generation of motor commands and for the emergence of complex locomotion patterns, adapting seamlessly also to unexpected environmental feedback. Induced (by collisions) and spontaneous mode switching are observed. We find that locomotion may follow transiently unstable limit cycles. The degeneracy of the propagating state with respect to the direction of propagating is, in our analysis, one of the drivings for the chaotic wandering, which partly involves a smooth diffusion of the angle of propagation.
Closed-loop robots driven by short-term synaptic plasticity: Emergent explorative vs. limit-cycle locomotion Laura Martin, Bulcsú Sándor, Claudius Gros
Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns.
Higher-order organization of complex networks Austin R. Benson, David F. Gleich, Jure Leskovec
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