Drosophila (Sophophora) subpulchrella Takamori and Watabe, sp. nov., of the D. suzukii subgroup in the D. melanogaster species group, is described from Japan and southern China, and compared with its sibling species, D. pulchrella Tan et al. distributed in the Yun-Gui Highland, south-western China. The results of cross-experiments show a complete pre-mating isolation between D. subpulchrella and D. pulchrella.
Deep Neural Networks (DNNs) have revolutionized a wide range of industries, from healthcare and finance to automotive, by offering unparalleled capabilities in data analysis and decisionmaking. Despite their transforming impact, DNNs face two critical challenges: the vulnerability to adversarial attacks and the increasing computational costs associated with more complex and larger models. In this paper, we introduce an effective method designed to simultaneously enhance adversarial robustness and execution efficiency. Unlike prior studies that enhance robustness via uniformly injecting noise, we introduce a non-uniform noise injection algorithm, strategically applied at each DNN layer to disrupt adversarial perturbations introduced in attacks. By employing approximation techniques, our approach identifies and protects essential neurons while strategically introducing noise into non-essential neurons. Our experimental results demonstrate that our method successfully enhances both robustness and efficiency across several attack scenarios, model architectures, and datasets.
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