Shadow kernels: A general mechanism for kernel specialization in existing operating systems

Existing operating systems share a common kernel text section amongst all processes. It is not possible to perform kernel specialization or tuning such that different applications execute text optimized for their kernel use despite the benefits of kernel specialization for performance guided optimization, exokernels, kernel fastpaths, and cheaper hardware access. Current specialization primitives involve system wide changes to kernel text, which can have adverse effects on other processes sharing the kernel due to the global side-effects. We present shadow kernels: a primitive that allows multiple kernel text sections to coexist in a contemporary operating system. By remapping kernel virtual memory on a context-switch, or for individual system calls, we specialize the kernel on a fine-grained basis. Our implementation of shadow kernels uses the Xen hypervisor so can be applied to any operating system that runs on Xen.This work was principally supported by internal funds from the Computer Laboratory at the University of Cambridge; and also by the Engineering and Physical Sciences Research Council [grant number EP/K503009/1].This is the final version of the article. It first appeared from ACM via http://dx.doi.org/10.1145/2797022.279702

Dependency-aware Attention Control for Unconstrained Face Recognition with Image Sets

This paper targets the problem of image set-based face verification and identification. Unlike traditional single media (an image or video) setting, we encounter a set of heterogeneous contents containing orderless images and videos. The importance of each image is usually considered either equal or based on their independent quality assessment. How to model the relationship of orderless images within a set remains a challenge. We address this problem by formulating it as a Markov Decision Process (MDP) in the latent space. Specifically, we first present a dependency-aware attention control (DAC) network, which resorts to actor-critic reinforcement learning for sequential attention decision of each image embedding to fully exploit the rich correlation cues among the unordered images. Moreover, we introduce its sample-efficient variant with off-policy experience replay to speed up the learning process. The pose-guided representation scheme can further boost the performance at the extremes of the pose variation.Comment: Fixed the unreadable code in CVF version. arXiv admin note: text overlap with arXiv:1707.00130 by other author

Hierarchically Clustered Adaptive Quantization CMAC and Its Learning Convergence

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