Month: November 2020

Social Trajectory Planning for Urban Autonomous Surface Vessels

Shinkyu Park; Michal Cap; Javier Alonso-Mora; Carlo Ratti; Daniela Rus

IEEE Transactions on Robotics

In this article, we propose a trajectory planning algorithm that enables autonomous surface vessels to perform socially compliant navigation in a city’s canal. The key idea behind the proposed algorithm is to adopt an optimal control formulation in which the deviation of movements of the autonomous vessel from nominal movements of human-operated vessels is penalized. Consequently, given a pair of origin and destination points, it finds vessel trajectories that resemble those of human-operated vessels. To formulate this, we adopt kernel density estimation (KDE) to build a nominal movement model of human-operated vessels from a prerecorded trajectory dataset, and use a Kullback–Leibler control cost to measure the deviation of the autonomous vessel’s movements from the model. We establish an analogy between our trajectory planning approach and the maximum entropy inverse reinforcement learning (MaxEntIRL) approach to explain how our approach can learn the navigation behavior of human-operated vessels. On the other hand, we distinguish our approach from the MaxEntIRL approach in that it does not require well-defined bases, often referred to as features, to construct its cost function as required in many of inverse reinforcement learning approaches in the trajectory planning context. Through experiments using a dataset of vessel trajectories collected from the automatic identification system, we demonstrate that the trajectories generated by our approach resemble those of human-operated vessels and that using them for canal navigation is beneficial in reducing head-on encounters between vessels and improving navigation safety.

How Do You Know When Society Is About to Fall Apart?

“Civilizations are fragile, impermanent things,” Tainter writes. Nearly every one that has ever existed has also ceased to exist, yet “understanding disintegration has remained a distinctly minor concern in the social sciences.” It is only a mild overstatement to suggest that before Tainter, collapse was simply not a thing.

Source: https://www.nytimes.com/2020/11/04/magazine/societal-collapse.html 

Shared Partisanship Dramatically Increases Social Tie Formation in a Twitter Field Experiment

Mohsen Mosleh, Cameron Martel, Dean Eckles, David G. Rand

 

Americans are much more likely to be socially connected to co-partisans, both in daily life and on social media. But this observation does not necessarily mean that shared partisanship per se drives social tie formation, because partisanship is confounded with many other factors. Here, we test the causal effect of shared partisanship on the formation of social ties in a field experiment on Twitter. We created bot accounts that self-identified as people who favored the Democratic or Republican party, and that varied in the strength of that identification. We then randomly assigned 842 Twitter users to be followed by one of our accounts. Users were roughly three times more likely to reciprocally follow-back bots whose partisanship matched their own, and this was true regardless of the bot’s strength of identification. Interestingly, there was no partisan asymmetry in this preferential follow-back behavior: Democrats and Republicans alike were much more likely to reciprocate follows from co-partisans. These results demonstrate a strong causal effect of shared partisanship on the formation of social ties in an ecologically valid field setting, and have important implications for political psychology, social media, and the politically polarized state of the American public.

 

Source: psyarxiv.com

Symmetry-Independent Stability Analysis of Synchronization Patterns

Yuanzhao Zhang and Adilson E. Motter

SIAM Rev., 62(4), 817–836.
https://doi.org/10.1137/19M127358X 

 

The field of network synchronization has seen tremendous growth following the introduction of the master stability function (MSF) formalism, which enables the efficient stability analysis of synchronization in large oscillator networks. However, to make further progress we must overcome the limitations of this celebrated formalism, which focuses on global synchronization and requires both the oscillators and their interaction functions to be identical, while many systems of interest are inherently heterogeneous and exhibit complex synchronization patterns. Here, we establish a generalization of the MSF formalism that can characterize the stability of any cluster synchronization pattern, even when the oscillators and/or their interaction functions are nonidentical. The new framework is based on finding the finest simultaneous block diagonalization of matrices in the variational equation and does not rely on information about network symmetry. This leads to an algorithm that is error-tolerant and orders of magnitude faster than existing symmetry-based algorithms. As an application, we rigorously characterize the stability of chimera states in networks with multiple types of interactions.

Inferring evolutionary pathways and directed genotype networks of foodborne pathogens

Cliff OM, McLean N, Sintchenko V, Fair KM, Sorrell TC, Kauffman S, & Prokopenko, M.

PLoS Comput Biol 16(10): e1008401

 

We study emergence and evolution of foodborne pathogens, and provide a new method for public health surveillance dealing with genetically diverse and spatiotemporally distributed epidemic scenarios. The proposed method interprets the surveillance data through genotype networks, and discovers how the most dominant strains of infection emerge and adapt. The approach allows us to correlate the strength of epidemics with genetic features of observed pathogens. This could open a way to predict and contain epidemics closer to their source, enabling more timely and precise allocations of public health resources, as well as efficient interventions during epidemics. This should make a significant economic and social impact, improving health of the population, while also safeguarding national and international supply chains.

Source: journals.plos.org