Automatically detecting software vulnerabilities in source code is an important problem that has attracted much attention.
Extensive experiments are conducted to demonstrate the power of our new datasets in benchmarking state-of-the-art multi-source domain adaptation methods, as well as the advantage of our proposed model.
Secondly, we propose the Prototypical Adversarial Domain Adaptation (PADA) model which utilizes unlabeled bridge domains to align feature distribution between source and target with a large discrepancy.
Most existing work that grounds natural language phrases in images starts with the assumption that the phrase in question is relevant to the image.
We describe the design of a 20-liter test stand constructed to study fundamental properties of liquid argon (LAr).
Instrumentation and Detectors High Energy Physics - Experiment