Joseph T. Lizier, Frank Bauer, Fatihcan M. Atay, and Jürgen Jost

PNAS 120 (37) e2303332120

How does the quality of synchronization between coupled oscillators depend on the network structure that connects them? This question has been at the forefont of studies on the structure–dynamics relationship, one of the most important open problems in complex systems. We present a method to fully and generally relate the structure of a network to its synchronizability, without assumptions made by previous approaches such as symmetric connections. Moreover, we reveal the impact of patterns of network connections among small groups of nodes (motifs) on the whole of network synchronizability. Our results implicate the prevalence of clustered structure such as feedforward and feedback loops as the most important factor in synchronizability.

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ARYO JAMSHIDPEY, MARCO DORIGO, AND MARY KATHERINE HEINRICH

INTELLIGENT COMPUTING 13 Sep 2023 Vol 2 Article ID: 0044 DOI: 10.34133/icomputing.0044

In collective perception, agents sample spatial data and use the samples to agree on some estimate. In this paper, we identify the sources of statistical uncertainty that occur in collective perception and note that improving the accuracy of fully decentralized approaches, beyond a certain threshold, might be intractable. We propose self-organizing hierarchy as an approach to improve accuracy in collective perception by reducing or eliminating some of the sources of uncertainty. Using self-organizing hierarchy, aspects of centralization and decentralization can be combined: robots can understand their relative positions system-wide and fuse their information at one point, without requiring, e.g., a fully connected or static communication network. In this way, multi-sensor fusion techniques that were designed for fully centralized systems can be applied to a self-organized system for the first time, without losing the key practical benefits of decentralization. We implement simple proof-of-concept fusion in a self-organizing hierarchy approach and test it against three fully decentralized benchmark approaches. We test the perceptual accuracy of the approaches for absolute conditions that are uniform time-invariant, time-varying, and spatially nonuniform with high heterogeneity, as well as the scalability and fault tolerance of their accuracy. We show that, under our tested conditions, the self-organizing hierarchy approach is generally more accurate, more consistent, and faster than the other approaches and also that its accuracy is more scalable and comparably fault-tolerant. Under spatially nonuniform conditions, our results indicate that the four approaches are comparable in terms of similarity to the reference samples. In future work, extending these results to additional methods, such as collective probability distribution fitting, is likely to be much more straightforward in the self-organizing hierarchy approach than in the decentralized approaches.

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L. Feola, A. Reina, M. S. Talamali, V. Trianni. Multi-Swarm Interaction through Augmented Reality for Kilobots. IEEE Robotics and Automation Letters 8(11), 2023.

Research with swarm robotics systems can be complicated, time-consuming, and often expensive in terms of space and resources. The situation is even worse for studies involving multiple, possibly heterogeneous robot swarms. Augmented reality can provide an interesting solution to these problems, as demonstrated by the ARK system (Augmented Reality for Kilobots), which enhanced the experimentation possibilities with Kilobots, also relieving researchers from demanding tracking and logging activities. However, ARK is limited in mostly enabling experimentation with a single swarm. In this paper, we introduce M-ARK, a system to support studies on multi-swarm interaction. M-ARK is based on the synchronisation over a network connection of multiple ARK systems, whether real or simulated, serving a twofold purpose: (i) to study the interaction of multiple, possibly heterogeneous swarms, and (ii) to enable a gradual transition from simulation to reality. Moreover, M-ARK enables the interaction between swarms dislocated across multiple labs worldwide, encouraging scientific collaboration and advancement in multi-swarm interaction studies.

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George F. R. Ellis

Entropy 2023, 25(9), 1301

This paper considers how a classification of causal effects as comprising efficient, formal, material, and final causation can provide a useful understanding of how emergence takes place in biology and technology, with formal, material, and final causation all including cases of downward causation; they each occur in both synchronic and diachronic forms. Taken together, they underlie why all emergent levels in the hierarchy of emergence have causal powers (which is Noble’s principle of biological relativity) and so why causal closure only occurs when the upwards and downwards interactions between all emergent levels are taken into account, contra to claims that some underlying physics level is by itself causality complete. A key feature is that stochasticity at the molecular level plays an important role in enabling agency to emerge, underlying the possibility of final causation occurring in these contexts.

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Cate Heine, Kevin P. O’Keeffe, Paolo Santi, Li Yan & Carlo Ratti
Scientific Reports volume 13, Article number: 14064 (2023)

Human mobility is a key driver of infectious disease spread. Recent literature has uncovered a clear pattern underlying the complexity of human mobility in cities: 𝑟⋅𝑓, the product of distance traveled r and frequency of return f per user to a given location, is invariant across space. This paper asks whether the invariant 𝑟⋅𝑓 also serves as a driver for epidemic spread, so that the risk associated with human movement can be modeled by a unifying variable 𝑟⋅𝑓. We use two large-scale datasets of individual human mobility to show that there is in fact a simple relation between r and f and both speed and spatial dispersion of disease spread. This discovery could assist in modeling spread of disease and inform travel policies in future epidemics—based not only on travel distance r but also on frequency of return f.

Read the full article at: www.nature.com