Anticipating regime shifts by mixing early warning signals from different nodes.

Real systems showing regime shifts, such as ecosystems, are often composed of many dynamical elements interacting on a network. Various early warning signals have been proposed for anticipating regime shifts from observed data. However, it is unclear how one should combine early warning signals from different nodes for better performance. Based on theory of stochastic differential equations, we propose a method to optimize the node set from which to construct an early warning signal. The proposed method takes into account that uncertainty as well as the magnitude of the signal affects its predictive performance, that a large magnitude or small uncertainty of the signal in one situation does not imply the signal's high performance, and that combining early warning signals from different nodes is often but not always beneficial. The method performs well particularly when different nodes are subjected to different amounts of dynamical noise and stress.

Early warnings for multi-stage transitions in dynamics on networks.

Successfully anticipating sudden major changes in complex systems is a practical concern. Such complex systems often form a heterogeneous network, which may show multi-stage transitions in which some nodes experience a regime shift earlier than others as an environment gradually changes. Here we investigate early warning signals for networked systems undergoing a multi-stage transition. We found that knowledge of both the ongoing multi-stage transition and network structure enables us to calculate effective early warning signals for multi-stage transitions. Furthermore, we found that small subsets of nodes could anticipate transitions as well as or even better than using all the nodes. Even if we fix the network and dynamical system, no single best subset of nodes provides good early warning signals, and a good choice of sentinel nodes depends on the tipping direction and the current stage of the dynamics within a multi-stage transition, which we systematically characterize.

Does gender structure social networks across domains of cooperation? An exploration of gendered networks among matrilineal and patrilineal Mosuo.

Cooperative networks are essential features of human society. Evolutionary theory hypothesizes that networks are used differently by men and women, yet the bulk of evidence supporting this hypothesis is based on studies conducted in a limited range of contexts and on few domains of cooperation. In this paper, we compare individual-level cooperative networks from two communities in Southwest China that differ systematically in kinship norms and institutions-one matrilineal and one patrilineal-while sharing an ethnic identity. Specifically, we investigate whether network structures differ based on prevailing kinship norms and type of gendered cooperative activity, one woman-centred (preparation of community meals) and one man-centred (farm equipment lending). Our descriptive results show a mixture of 'feminine' and 'masculine' features in all four networks. The matrilineal meals network stands out in terms of high degree skew. Exponential random graph models reveal a stronger role for geographical proximity in patriliny and a limited role of affinal relatedness across all networks. Our results point to the need to consider domains of cooperative activity alongside gender and cultural context to fully understand variation in how women and men leverage social relationships toward different ends. This article is part of the theme issue 'Cooperation among women: evolutionary and cross-cultural perspectives'.

Market integration, income inequality, and kinship system among the Mosuo of China.

Increased access to defensible material wealth is hypothesised to escalate inequality. Market integration, which creates novel opportunities in cash economies, provides a means of testing this hypothesis. Using demographic data collected from 505 households among the matrilineal and patrilineal Mosuo in 2017, we test whether market integration is associated with increased material wealth, whether increased material wealth is associated with wealth inequality, and whether being in a matrilineal vs. patrilineal kinship system alters the relationship between wealth and inequality. We find evidence that market integration, measured as distance to the nearest source of tourism and primary source of household income, is associated with increased household income and 'modern' asset value. Both village-level market integration and mean asset value were associated negatively, rather than positively, with inequality, contrary to predictions. Finally, income, modern wealth and inequality were higher in matrilineal communities that were located closer to the centre of tourism and where tourism has long provided a relatively stable source of income. However, we also observed exacerbated inequality with increasing farm animal value in patriliny. We conclude that the forces affecting wealth and inequality depend on local context and that the importance of local institutions is obscured by aggregate statistics drawn from modern nation states.

Generation and influence of eccentric ideas on social networks.

Studying extreme ideas in routine choices and discussions is of utmost importance to understand the increasing polarization in society. In this study, we focus on understanding the generation and influence of extreme ideas in routine conversations which we label "eccentric" ideas. The eccentricity of any idea is defined as the deviation of that idea from the norm of the social neighborhood. We collected and analyzed data from two sources of different nature: public social media and online experiments in a controlled environment. We compared the popularity of ideas against their eccentricity to understand individuals' fascination towards eccentricity. We found that more eccentric ideas have a higher probability of getting a greater number of "likes". Additionally, we demonstrate that the social neighborhood of an individual conceals eccentricity changes in one's own opinions and facilitates generation of eccentric ideas at a collective level.

Utterance Clustering Using Stereo Audio Channels.

Utterance clustering is one of the actively researched topics in audio signal processing and machine learning. This study aims to improve the performance of utterance clustering by processing multichannel (stereo) audio signals. Processed audio signals were generated by combining left- and right-channel audio signals in a few different ways and then by extracting the embedded features (also called d-vectors) from those processed audio signals. This study applied the Gaussian mixture model for supervised utterance clustering. In the training phase, a parameter-sharing Gaussian mixture model was obtained to train the model for each speaker. In the testing phase, the speaker with the maximum likelihood was selected as the detected speaker. Results of experiments with real audio recordings of multiperson discussion sessions showed that the proposed method that used multichannel audio signals achieved significantly better performance than a conventional method with mono-audio signals in more complicated conditions.