Age Progression and Regression with Spatial Attention Modules

Age progression and regression refers to aesthetically render-ing a given face image to present effects of face aging and rejuvenation, respectively. Although numerous studies have been conducted in this topic, there are two major problems: 1) multiple models are usually trained to simulate different age mappings, and 2) the photo-realism of generated face images is heavily influenced by the variation of training images in terms of pose, illumination, and background. To address these issues, in this paper, we propose a framework based on conditional Generative Adversarial Networks (cGANs) to achieve age progression and regression simultaneously. Particularly, since face aging and rejuvenation are largely different in terms of image translation patterns, we model these two processes using two separate generators, each dedicated to one age changing process. In addition, we exploit spatial attention mechanisms to limit image modifications to regions closely related to age changes, so that images with high visual fidelity could be synthesized for in-the-wild cases. Experiments on multiple datasets demonstrate the ability of our model in synthesizing lifelike face images at desired ages with personalized features well preserved, and keeping age-irrelevant regions unchanged

PASCAL: A Learning-aided Cooperative Bandwidth Control Policy for Hierarchical Storage Systems

Nowadays, the Hierarchical Storage System (HSS) is considered as an ideal model to meet the cost-performance demand. The data migration between storing tiers of HSS is the way to achieve the cost-performance goal. The bandwidth control is to limit the maximum amount of data migration. Most of previous research about HSS focus on studying the data migration policy instead of bandwidth control. However, the recent research about cache and networking optimization suggest that the bandwidth control has significant impact on the system performance. Few previous work achieves a satisfactory bandwidth control in HSS since it is hard to control bandwidth for so many data migration tasks simultaneously. In this paper, we first give a stochastic programming model to formalize the bandwidth control problem in HSS. Then we propose a learning-aided bandwidth control policy for HSS, named \Pascal{}, which learns to control the bandwidth of different data migration task in an cooperative way. We implement \Pascal{} on a commercial HSS and compare it with three strong baselines over a group of workloads. Our evaluation on the physical system shows that \Pascal{} can effectively decrease 1.95X the tail latency and greatly improve throughput stability (2X $\downarrow$ throughput jitter), and meanwhile keep the throughput at a relatively high level

Clustered Error Correction of Codeword-Stabilized Quantum Codes

Codeword stabilized (CWS) codes are a general class of quantum codes that includes stabilizer codes and many families of non-additive codes with good parameters. For such a non-additive code correcting all t-qubit errors, we propose an algorithm that employs a single measurement to test all errors located on a given set of t qubits. Compared with exhaustive error screening, this reduces the total number of measurements required for error recovery by a factor of about 3^t.Comment: 4 pages, 2 figures, revtex4; number of editorial changes in v

Predicting and Improving Throughput, Responsiveness and Battery Life of Computer Systems by Machine Learning

The Machine Learning (ML) algorithms are increasingly explored in varies of fields including designing and optimizing computer systems. Recent research, such as optimizing memory/cache prefetching by ML training or predicting traffic pattern in throughput processors, also exhibits a promising future of introducing ML into computer system design and optimization. Throughput optimization in throughput-oriented processors is imperative as the computing workload of parallel and cloud computing have been growing rapidly in recent years. At the same time, throughput optimization can be time consuming when applying conventional design and optimizing process as the design space is prohibitively huge. In the first part of this dissertation, we firstly define a huge and complicated design space in silicon interposer-based throughput processors, then utilize Monte Carlo Tree Search model (MCTS) to exploit and explore the design space. The evaluation results show that the system performance is improved by over 20\% with only 0.05\% design space is assessed. Performance and power (PnP) are also the most important metrics that are utilized by original equipment manufacturers (OEMs) to conduct the design of a device. Current PnP measurements rely on manual hardware swapping and testing for systems which is time consuming and not financially-efficient. A fast and accurate PnP value prediction solution can guide OEMs to understanding the basic behaviour of different hardware components, and, more importantly, shortens the time to market of a device. In the second work, we explore the common ground between natural language processing (NLP) problems and system PnP prediction problems, and develop an NLP-like solution to resolve the problem. The solution is available to extract the inter- and intra-relationship among the existing system components and to predict the behavior of system components that have not appeared before. The results of our evaluation demonstrate that the solution achieves as high as 94\% labeling accuracy in a real-measured dataset

Semantic-aware One-shot Face Re-enactment with Dense Correspondence Estimation

One-shot face re-enactment is a challenging task due to the identity mismatch between source and driving faces. Specifically, the suboptimally disentangled identity information of driving subjects would inevitably interfere with the re-enactment results and lead to face shape distortion. To solve this problem, this paper proposes to use 3D Morphable Model (3DMM) for explicit facial semantic decomposition and identity disentanglement. Instead of using 3D coefficients alone for re-enactment control, we take the advantage of the generative ability of 3DMM to render textured face proxies. These proxies contain abundant yet compact geometric and semantic information of human faces, which enable us to compute the face motion field between source and driving images by estimating the dense correspondence. In this way, we could approximate re-enactment results by warping source images according to the motion field, and a Generative Adversarial Network (GAN) is adopted to further improve the visual quality of warping results. Extensive experiments on various datasets demonstrate the advantages of the proposed method over existing start-of-the-art benchmarks in both identity preservation and re-enactment fulfillment