Evolution occurs in populations of reproducing individuals. The structure of a population can affect which traits evolve. Understanding evolutionary game dynamics in structured populations remains difficult. Mathematical results are known for special structures in which all individuals have the same number of neighbours. The general case, in which the number of neighbours can vary, has remained open. For arbitrary selection intensity, the problem is in a computational complexity class that suggests there is no efficient algorithm. Whether a simple solution for weak selection exists has remained unanswered. Here we provide a solution for weak selection that applies to any graph or network. Our method relies on calculating the coalescence times of random walks. We evaluate large numbers of diverse population structures for their propensity to favour cooperation. We study how small changes in population structure-graph surgery-affect evolutionary outcomes. We find that cooperation flourishes most in societies that are based on strong pairwise ties.

ultivated peanut or groundnut (A. hypogaea L.) is among the most important oil and food legumes, grown on 25 million ha between latitudes 40° N and 40° S with annual production of ~46 million tons (http://www.fao.org/faostat/en/#home). It presumably was domesticated in South America ~6,000 years ago and then was widely distributed in post-Columbian times 1. Combining richness in seed oil (~46-58%) and protein (~22-32%), peanut is important in fighting malnutrition and ensuring food security.

Silicon anode solid-state batteries Research on solid-state batteries has focused on lithium metal anodes. Alloy-based anodes have received less attention in part due to their lower specific capacity even though they should be safer. Tan et al . developed a slurry-based approach to create films from micrometer-scale silicon particles that can be used in anodes with carbon binders. When incorporated into solid-state batteries, they showed good performance across a range of temperatures and excellent cycle life in full cells. —MSL

Despite the recent success of deep-learning based semantic segmentation, deploying a pre-trained road scene segmenter to a city whose images are not presented in the training set would not achieve satisfactory performance due to dataset biases. Instead of collecting a large number of annotated images of each city of interest to train or refine the segmenter, we propose an unsupervised learning approach to adapt road scene segmenters across different cities. By utilizing Google Street View and its timemachine feature, we can collect unannotated images for each road scene at different times, so that the associated static-object priors can be extracted accordingly. By advancing a joint global and class-specific domain adversarial learning framework, adaptation of pre-trained segmenters to that city can be achieved without the need of any user annotation or interaction. We show that our method improves the performance of semantic segmentation in multiple cities across continents, while it performs favorably against state-of-the-art approaches requiring annotated training data.