Global routing plays a critical role in modern chip design. The routing paths generated by global routers often form a rectilinear Steiner tree (RST).

We evaluate the identified RSTs' robustness against more attacks on top of two networks on CIFAR-10 as a complement for Sec. As observed from Tab. 1, we can see that the RSTs searched by PGD-7 training are also robust against other attacks. As observed in Figure 1, RSTs drawn from randomly initialized networks achieve a comparable natural accuracy with the RTTs drawn from naturally/adversarially trained networks and adversarial RTTs generally achieve the best natural accuracy. Trained), (2) adversarially trained dense models (Dense Adv. Trained 70.70 74.35 77.20 77.71 75.55 79.22 78.85 77.33 0 81.28 Dense Adv.

There has been an enormous interest in deploying deep neural networks (DNNs) into numerous real-world applications requiring strict security.

We explore combining dropout with robust training methods and obtain better generalization.

Deep Neural Networks (DNNs) are known to be vulnerable to adversarial attacks, i.e., an imperceptible perturbation to the input can mislead DNNs trained on clean images into making erroneous predictions. To tackle this, adversarial training is currently the most effective defense method, by augmenting the training set with adversarial samples generated on the fly.

Global routing plays a critical role in modern chip design. The routing paths generated by global routers often form a rectilinear Steiner tree (RST).

We explore combining dropout with robust training methods and obtain better generalization.

We introduce UniRST, the first unified RST-style discourse parser capable of handling 18 treebanks in 11 languages without modifying their relation inventories. To overcome inventory incompatibilities, we propose and evaluate two training strategies: Multi-Head, which assigns separate relation classification layer per inventory, and Masked-Union, which enables shared parameter training through selective label masking. We first benchmark monotreebank parsing with a simple yet effective augmentation technique for low-resource settings. We then train a unified model and show that (1) the parameter efficient Masked-Union approach is also the strongest, and (2) UniRST outperforms 16 of 18 mono-treebank baselines, demonstrating the advantages of a single-model, multilingual end-to-end discourse parsing across diverse resources.

This paper presents DeDisCo, Georgetown University's entry in the DISRPT 2025 shared task on discourse relation classification. We test two approaches, using an mt5-based encoder and a decoder based approach using the openly available Qwen model. We also experiment on training with augmented dataset for low-resource languages using matched data translated automatically from English, as well as using some additional linguistic features inspired by entries in previous editions of the Shared Task. Our system achieves a macro-accuracy score of 71.28, and we provide some interpretation and error analysis for our results.