Prey capture and meat-eating by the wild colobus monkey _Rhinopithecus bieti_ in Yunnan, China

If it is true that extant primates evolved from an insectivorous ancestor, then primate entomophagy would be a primitive trait. Many taxa, however, have undergone a dietary shift from entomophagy to phytophagy, evolving a specialised gut and dentition and becoming exclusive herbivores. The exclusively herbivorous taxa are the Malagasy families Indriidae and Lepilemuridae, and the Old World Monkey subfamily Colobinae, and among these meat-eating has not been observed except as an anomaly, with the sole exception of the Hanuman langur (_Semnopithecus entellus_), which feeds on insects seasonally, and a single observation of a nestling bird predated by wild Sichuan snub-nosed monkeys (_Rhinopithecus roxellana_). Here, we describe the regular capture of warm-blooded animals and the eating of meat by a colobine, the critically endangered Yunnan snub-nosed monkey (_Rhinopithecus bieti_). This monkey engages in scavenge hunting as a male-biased activity that may, in fact, be related to group structure and spatial spread. In this context, meat-eating can be regarded as an energy/nutrient maximization feeding strategy rather than as a consequence of any special characteristic of meat itself. The finding of meat-eating in forest-dwelling primates might provide new insights into the evolution of dietary habits in early humans

Boosting Adversarial Transferability by Achieving Flat Local Maxima

Transfer-based attack adopts the adversarial examples generated on the surrogate model to attack various models, making it applicable in the physical world and attracting increasing interest. Recently, various adversarial attacks have emerged to boost adversarial transferability from different perspectives. In this work, inspired by the fact that flat local minima are correlated with good generalization, we assume and empirically validate that adversarial examples at a flat local region tend to have good transferability by introducing a penalized gradient norm to the original loss function. Since directly optimizing the gradient regularization norm is computationally expensive and intractable for generating adversarial examples, we propose an approximation optimization method to simplify the gradient update of the objective function. Specifically, we randomly sample an example and adopt the first-order gradient to approximate the second-order Hessian matrix, which makes computing more efficient by interpolating two Jacobian matrices. Meanwhile, in order to obtain a more stable gradient direction, we randomly sample multiple examples and average the gradients of these examples to reduce the variance due to random sampling during the iterative process. Extensive experimental results on the ImageNet-compatible dataset show that the proposed method can generate adversarial examples at flat local regions, and significantly improve the adversarial transferability on either normally trained models or adversarially trained models than the state-of-the-art attacks.Comment: 17 pages, 5 figures, 6 table