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This paper presents the Extension Interface Model (EIM) and bpftime, which together enable safer and more efficient extension of userspace applications than the current state-of-the-art. EIM is a new model that treats each required feature of an extension as a resource, including concrete hardware resources (e.g., memory) and abstract ones (e.g., the ability to invoke a function from the extended application). An extension manager, i.e., the person who manages a deployment, uses EIM to specify only the resources an extension needs to perform its task. bpftime is a new extension framework that enforces an EIM specification. Compared to prior systems, bpftime is efficient because it uses extended Berkeley Packet Filter (eBPF)-style verification, hardware-supported isolation features (e.g., Intel MPK), and dynamic binary rewriting. Moreover, bpftime is easy to adopt into existing workflows since it is compatible with the current eBPF ecosystem. We demonstrate the usefulness of EIM and bpftime across 6 use cases that improve security, monitor and enhance performance, and explore configuration trade-offs. 

Learning from noisy labels is a long-standing problem in machine learning for real applications. One of the main research lines focuses on learning a label corrector to purify potential noisy labels. However, these methods typically rely on strict assumptions and are limited to certain types of label noise. In this paper, we reformulate the label-noise problem from a generative-model perspective, i.e., labels are generated by gradually refining an initial random guess. This new perspective immediately enables existing powerful diffusion models to seamlessly learn the stochastic generative process. Once the generative uncertainty is modeled, we can perform classification inference using maximum likelihood estimation of labels. To mitigate the impact of noisy labels, we propose Label-Retrieval- Augmented (LRA) diffusion model 1, which leverages neighbor consistency to effectively construct pseudo-clean labels for diffusion training. Our model is flexible and general, allowing easy incorporation of different types of conditional information, e.g., use of pre-trained models, to further boost model performance. Extensive experiments are conducted for evaluation. Our model achieves new state-of-the-art (SOTA) results on all standard real-world benchmark datasets. Remarkably, by incorporating conditional information from the powerful CLIP model, our method can boost the current SOTA accuracy by 10-20 absolute points in many cases.