In this article, we present a family of adaptive stochastic optimization methods, which are associated with mirror maps that are widely used to capture the geometry properties of optimization problems during iteration processes. The well-known adaptive moment estimation (Adam)-type algorithm falls into the family when the mirror maps take the form of temporal adaptation. In the context of convex objective functions, we show that with proper step sizes and hyperparameters, the average regret can achieve the convergence rate ${\mathcal { O}}(T^{-(1/2)})$ after T iterations under some standard assumptions. We further improve it to $O(T^{-1}\log T)$ when the objective functions are strongly convex. In the context of smooth objective functions (not necessarily convex), based on properties of the strongly convex differentiable mirror map, our algorithms achieve convergence rates of order ${\mathcal { O}}(T^{-(1/2)})$ up to a logarithmic term, requiring large or increasing hyperparameters that are coincident with practical usage of Adam-type algorithms. Thus, our work gives explanations for the selection of the hyperparameters in Adam-type algorithms' implementation.

Dynamic taint analysis (DTA), as a fundamental analysis technique, is widely used in security, privacy, and diagnosis, etc. As DTA demands to collect and analyze massive taint data online, it suffers extremely high runtime overhead. Over the past decades, numerous attempts have been made to lower the overhead of DTA. Unfortunately, the reductions they achieved are marginal, causing DTA only applicable to the debugging/testing scenarios. In this paper, we propose and implement HardTaint, a system that can realize production-run dynamic taint tracking. HardTaint adopts a hybrid and systematic design which combines static analysis, selective hardware tracing and parallel graph processing techniques. The comprehensive evaluations demonstrate that HardTaint introduces only around 8% runtime overhead which is an order of magnitude lower than the state-of-the-arts, while without sacrificing any taint detection capability.

The demands of high-quality videos captured by camera become bigger due to the rapid development of pattern recognition and artificial intelligence. Video denoising is the key technology to obtain clear videos. However, the research on video denoising is far from enough now. In this paper, we propose a video denoising method based on convolutional neural network architecture to reduce the noise from the sensor system. We improve the loss function of noise estimation by imposing adaptive penalty on under-estimation error of noise level which makes our method perform robustly. Furthermore, we make use of multi-level features to guide the spatial denoising, where multilayer semantic information of the image is regarded as the perceptual loss. Instead of relying on Optical Flow solving the characterization of inter-frame information, we utilize U-Net-like structure to handle motion implicitly. It is less computationally expensive and avoids distortions caused by inaccurate flow and object occlusion. In order to locate temporal features and suppress useless information, the attention mechanism is introduced to the skip connections of the U-Net-like structure. Experimental results demonstrate that the proposed algorithm outputs more convincing results in both peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) indexes when processing Gaussian noise, synthetic real noise, and real noise compared with selected approaches.

Hardware tracing modules such as Intel Processor Trace perform continuous control-flow tracing of an end-to-end program execution with an ultra-low overhead. PT has been used in a variety of contexts to support applications such as testing, debugging, and performance diagnosis. However, these hardware modules have so far been used only to trace native programs, which are directly compiled down to machine code. As high-level languages (HLL) such as Java and Go become increasingly popular, there is a pressing need to extend these benefits to the HLL community. This paper presents JPortal, a JVM-based profiling tool that bridges the gap between HLL applications and low-level hardware traces by using a set of algorithms to precisely recover an HLL program’s control flow from PT traces. An evaluation of JPortal with the DaCapo benchmark shows that JPortal achieves an overall 80% accuracy for end-to-end control flow profiling with only a 4-16% runtime overhead.