39,907 research outputs found
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 dimensional systems
Antidumping Petition: To File or Not To File
Given the ââŹĹnormal valueâ⏠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 ââŹĹsignificantly large,â⏠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 ââŹĹsignificantly small,â⏠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
Modeling the pulse signal by wave-shape function and analyzing by synchrosqueezing transform
We apply the recently developed adaptive non-harmonic model based on the
wave-shape function, as well as the time-frequency analysis tool called
synchrosqueezing transform (SST) to model and analyze oscillatory physiological
signals. To demonstrate how the model and algorithm work, we apply them to
study the pulse wave signal. By extracting features called the spectral pulse
signature, {and} based on functional regression, we characterize the
hemodynamics from the radial pulse wave signals recorded by the
sphygmomanometer. Analysis results suggest the potential of the proposed signal
processing approach to extract health-related hemodynamics features
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