Recombining your way out of trouble: the genetic architecture of hybrid fitness under environmental stress

Hybridization between species is a fundamental evolutionary force that can both promote and delay adaptation. There is a deficit in our understanding of the genetic basis of hybrid fitness, especially in non-domesticated organisms. We also know little about how hybrid fitness changes as a function of environmental stress. Here, we made genetically variable F2 hybrid populations from two divergent Saccharomyces yeast species, exposed populations to ten toxins, and sequenced the most resilient hybrids on low coverage using ddRADseq. We expected to find strong negative epistasis and heterozygote advantage in the hybrid genomes. We investigated three aspects of hybridness: 1) hybridity, 2) interspecific heterozygosity, and 3) epistasis (positive or negative associations between non-homologous chromosomes). Linear mixed effect models revealed strong genotype-by-environment interactions with many chromosomes and chromosomal interactions showing species-biased content depending on the environment. Against our predictions, we found extensive selection against heterozygosity such that homozygous allelic combinations from the same species were strongly overrepresented in an otherwise hybrid genomic background. We also observed multiple cases of positive epistasis between chromosomes from opposite species, confirmed by epistasis- and selection-free simulations, which is surprising given the large divergence of the parental species (~15% genome-wide). Together, these results suggest that stress-resilient hybrid genomes can be assembled from the best features of both parents, without paying high costs of negative epistasis across large evolutionary distances. Our findings illustrate the importance of measuring genetic trait architecture in an environmental context when determining the evolutionary potential of hybrid populations

Using LIP to Gloss Over Faces in Single-Stage Face Detection Networks

This work shows that it is possible to fool/attack recent state-of-the-art face detectors which are based on the single-stage networks. Successfully attacking face detectors could be a serious malware vulnerability when deploying a smart surveillance system utilizing face detectors. We show that existing adversarial perturbation methods are not effective to perform such an attack, especially when there are multiple faces in the input image. This is because the adversarial perturbation specifically generated for one face may disrupt the adversarial perturbation for another face. In this paper, we call this problem the Instance Perturbation Interference (IPI) problem. This IPI problem is addressed by studying the relationship between the deep neural network receptive field and the adversarial perturbation. As such, we propose the Localized Instance Perturbation (LIP) that uses adversarial perturbation constrained to the Effective Receptive Field (ERF) of a target to perform the attack. Experiment results show the LIP method massively outperforms existing adversarial perturbation generation methods -- often by a factor of 2 to 10.Comment: to appear ECCV 2018 (accepted version

Nuclear matter and neutron matter for improved quark mass density- dependent model with ρ\rho mesons

A new improved quark mass density-dependent model including u, d quarks, $\sigma$ mesons, $\omega$ mesons and $\rho$ mesons is presented. Employing this model, the properties of nuclear matter, neutron matter and neutron star are studied. We find that it can describe above properties successfully. The results given by the new improved quark mass density- dependent model and by the quark meson coupling model are compared.Comment: 18 pages, 7 figure