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Full-Text Articles in Theory and Algorithms
Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz
Using Torchattacks To Improve The Robustness Of Models With Adversarial Training, William S. Matos Díaz
Cybersecurity: Deep Learning Driven Cybersecurity Research in a Multidisciplinary Environment
Adversarial training has proven to be one of the most successful ways to defend models against adversarial examples. This process consists of training a model with an adversarial example to improve the robustness of the model. In this experiment, Torchattacks, a Pytorch library made for importing adversarial examples more easily, was used to determine which attack was the strongest. Later on, the strongest attack was used to train the model and make it more robust against adversarial examples. The datasets used to perform the experiments were MNIST and CIFAR-10. Both datasets were put to the test using PGD, FGSM, and …
Cylindrical Similarity Measurement For Helices In Medium-Resolution Cryo-Electron Microscopy Density Maps, Salim Sazzed, Peter Scheible, Maytha Alshammari, Willy Wriggers, Jing He
Cylindrical Similarity Measurement For Helices In Medium-Resolution Cryo-Electron Microscopy Density Maps, Salim Sazzed, Peter Scheible, Maytha Alshammari, Willy Wriggers, Jing He
College of Sciences Posters
Cryo-electron microscopy (cryo-EM) density maps at medium resolution (5-10 Å) reveal secondary structural features such as α-helices and β-sheets, but they lack the side chains details that would enable a direct structure determination. Among the more than 800 entries in the Electron Microscopy Data Bank (EMDB) of medium-resolution density maps that are associated with atomic models, a wide variety of similarities can be observed between maps and models. To validate such atomic models and to classify structural features, a local similarity criterion, the F1 score, is proposed and evaluated in this study. The F1 score is theoretically normalized to a …