Previous studies investigating altruistic punishment have confounded the effects of two independent variables: information transmission (or breach of privacy) and personal identification (or breach of anonymity).Here we report findings from a brief study in which participants were asked to respond to a social norm violation (i.e., an anonymous acto

Due to the powerful ability of data fitting, deep neural networks have been applied in a wide range of applications in many key areas.However, in recent years, it was found that some adversarial samples easily fool the deep neural networks.These input samples are generated by adding a few small perturbations based on the original sample, making a v

Deep neural networks (DNNs) have promoted much of the recent progress in hyperspectral image (HSI) classification, which depends on extensive labeled samples and deep network structure and has achieved surprisingly good generalization capacity.However, due to the expensive labeling cost, the labeled samples are scarce in most practice cases, which

Abstract Small ruminants are suited to a wide variety of habitats and thus represent promising study models for identifying genes underlying adaptations.Here, we considered local Mediterranean breeds of goats (n = 17) and sheep (n = 25) from Italy, France and Spain.Based on historical archives, we selected the breeds potentially most linked to a te

Abstract Background The prediction of coronavirus disease in 2019 (COVID-19) in broader regions has been widely researched, but for specific areas such as urban areas the predictive models were rarely studied.It may be inaccurate to apply predictive models from a broad region directly to a small area.This paper builds a prediction approach for smal