299 citations · 322 across the 3 of their papers we have counts for
6 papers
Fuzziness-tuned: Improving the Transferability of Adversarial Examples
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
With the development of adversarial attacks, adversairal examples have been widely used to enhance the robustness of the training models on deep neural networks. Although considera…
Evolution of the post merger remnants from the coalescence of oxygen-neon and carbon-oxygen white dwarf pairs
Chengyuan Wu, Heran Xiong, Jie Lin +4
Although multidimensional simulations have investigated the processes of double WD mergers, post-merger evolution only focused on the carbon-oxygen (CO) WD or helium (He) WD merger…
Improving the Robustness and Generalization of Deep Neural Network with Confidence Threshold Reduction
Xiangyuan Yang, Jie Lin, Hanlin Zhang +2
Deep neural networks are easily attacked by imperceptible perturbation. Presently, adversarial training (AT) is the most effective method to enhance the robustness of the model aga…
Evidence for marginal stability in emulsions
Jie Lin, Ivane Jorjadze, Lea-Laetitia Pontani +2
We report the first measurements of the effect of pressure on vibrational modes in emulsions, which serve as a model for soft frictionless spheres at zero temperature. As a functio…
Scaling Description of Non-Local Rheology
Thomas Gueudré, Jie Lin, Alberto Rosso +1
Non-locality is crucial to understand the plastic flow of an amorphous material, and has been successfully described by the fluidity, along with a cooperativity length scale ξ. We…
Scaling description of the yielding transition in soft amorphous solids at zero temperature
Jie Lin, Edan Lerner, Alberto Rosso +1
Yield stress materials flow if a sufficiently large shear stress is ap- plied. Although such materials are ubiquitous and relevant for indus- try, there is no accepted microscopic…