Administrative registry data can be used to construct population-scale networks whose ties reflect shared social contexts between persons. With machine learning, such networks can be encoded into numerical representations -- embeddings -- that automatically capture individuals' position within the network. We created embeddings for all persons in the Dutch population from a population-scale network that represents five shared contexts: neighborhood, work, family, household, and school. To assess the informativeness of these embeddings, we used them to predict right-wing populist voting. Embeddings alone predicted right-wing populist voting above chance-level but performed worse than individual characteristics. Combining the best subset of embeddings with individual characteristics only slightly improved predictions. After transforming the embeddings to make their dimensions more sparse and orthogonal, we found that one embedding dimension was strongly associated with the outcome. Mapping this dimension back to the population network revealed differences in network structure related to right-wing populist voting between different school ties and achieved education levels. Our study contributes methodologically by demonstrating how population-scale network embeddings can be made interpretable, and substantively by linking structural network differences in education to right-wing populist voting.

With the increasing computing power, using data-driven approaches to co-design a robot's morphology and controller has become a promising way. However, most existing data-driven methods require training the controller for each morphology to calculate fitness, which is time-consuming. In contrast, the dual-network framework utilizes data collected by individual networks under a specific morphology to train a population network that provides a surrogate function for morphology optimization. This approach replaces the traditional evaluation of a diverse set of candidates, thereby speeding up the training. Despite considerable results, the online training of both networks impedes their performance. To address this issue, we propose a concurrent network framework that combines online and offline reinforcement learning (RL) methods. By leveraging the behavior cloning term in a flexible manner, we achieve an effective combination of both networks. We conducted multiple sets of comparative experiments in the simulator and found that the proposed method effectively addresses issues present in the dual-network framework, leading to overall algorithmic performance improvement. Furthermore, we validated the algorithm on a real robot, demonstrating its feasibility in a practical application.

At the forefront of neuroscientific research is the study of functional connectivity; defined as the statistical dependencies across spatially remote brain regions [Friston, 1994, 2011]. While traditional neuroimaging studies focused on the roles of specific brain regions, there has recently been a significant shift towards understanding the connectivity across regions [Smith, 2012]. This shift has been partially catalyzed by recent advances in imaging techniques. In particular, the introduction of functional MRI (fMRI) has played a crucial role by providing a noninvasive mechanism through which to obtain whole-brain coverage of neuronal activity [Huettel, Song and McCarthy, 2004, Poldrack, Mumford and Nichols, 2011]. The focus of this work involves estimating functional connectivity networks from fMRI data, however the methodology presented can also be used in conjunction with other imaging modalities. From a statistical perspective, Gaussian Graphical models (GGMs) are often employed to model functional connectivity [Smith et al., 2011, Varoquaux and Craddock, 2013]. In this manner, undirected connectivity networks can be inferred by studying the conditional independence structures across brain regions [Lauritzen, 1996].