Non-classicalities via perturbing local unitary operations

We study the nonclassical correlations in a two-qubit state by the perturbing local unitary operation method. We find that the definitions of various non-classicalities including quantum discord (QD), measurement-induced nonlocality (MIN) and so on usually do not have a unique definition when expressed as the perturbation of local unitary operations, so a given non-classicality can lead to different definitions of its dual non-classicality. In addition, it is shown that QD and MIN are not the corresponding dual expressions in a simple set of unitary operations, even though they are in their original definitions. In addition, we also consider the non-classicalities in general $2\otimes d$ dimensional systems

Antidumping Petition: To File or Not To File

Given the “normal value†of a product as common knowledge in an import-competing market, the profitability of a home firm in filing an antidumping (AD) petition against its foreign rival is shown to depend on the marginal cost differential between the home and foreign firms. When the marginal cost differential is “significantly large,†the home firm's ability to put the foreign firm at the risk of an AD violation is limited. But when the marginal cost differential is “significantly small,†the home firm is able to increase its output and lower the price of the product below its normal value, putting the foreign firm in the situation of an illegal dumping. One interesting implication is that, relative to the case without an AD law, the home firm has a stronger incentive to undertake cost-reducing activities (e.g., R&D investment or the adoption of a more efficient technology) under the law.antidumping laws, antidumping duties, dumping margins

MAT: A Multi-strength Adversarial Training Method to Mitigate Adversarial Attacks

Some recent works revealed that deep neural networks (DNNs) are vulnerable to so-called adversarial attacks where input examples are intentionally perturbed to fool DNNs. In this work, we revisit the DNN training process that includes adversarial examples into the training dataset so as to improve DNN's resilience to adversarial attacks, namely, adversarial training. Our experiments show that different adversarial strengths, i.e., perturbation levels of adversarial examples, have different working zones to resist the attack. Based on the observation, we propose a multi-strength adversarial training method (MAT) that combines the adversarial training examples with different adversarial strengths to defend adversarial attacks. Two training structures - mixed MAT and parallel MAT - are developed to facilitate the tradeoffs between training time and memory occupation. Our results show that MAT can substantially minimize the accuracy degradation of deep learning systems to adversarial attacks on MNIST, CIFAR-10, CIFAR-100, and SVHN.Comment: 6 pages, 4 figures, 2 table