Weakly-supervised Caricature Face Parsing through Domain Adaptation

A caricature is an artistic form of a person's picture in which certain striking characteristics are abstracted or exaggerated in order to create a humor or sarcasm effect. For numerous caricature related applications such as attribute recognition and caricature editing, face parsing is an essential pre-processing step that provides a complete facial structure understanding. However, current state-of-the-art face parsing methods require large amounts of labeled data on the pixel-level and such process for caricature is tedious and labor-intensive. For real photos, there are numerous labeled datasets for face parsing. Thus, we formulate caricature face parsing as a domain adaptation problem, where real photos play the role of the source domain, adapting to the target caricatures. Specifically, we first leverage a spatial transformer based network to enable shape domain shifts. A feed-forward style transfer network is then utilized to capture texture-level domain gaps. With these two steps, we synthesize face caricatures from real photos, and thus we can use parsing ground truths of the original photos to learn the parsing model. Experimental results on the synthetic and real caricatures demonstrate the effectiveness of the proposed domain adaptation algorithm. Code is available at: https://github.com/ZJULearning/CariFaceParsing .Comment: Accepted in ICIP 2019, code and model are available at https://github.com/ZJULearning/CariFaceParsin

Graphic-Card Cluster for Astrophysics (GraCCA) -- Performance Tests

In this paper, we describe the architecture and performance of the GraCCA system, a Graphic-Card Cluster for Astrophysics simulations. It consists of 16 nodes, with each node equipped with 2 modern graphic cards, the NVIDIA GeForce 8800 GTX. This computing cluster provides a theoretical performance of 16.2 TFLOPS. To demonstrate its performance in astrophysics computation, we have implemented a parallel direct N-body simulation program with shared time-step algorithm in this system. Our system achieves a measured performance of 7.1 TFLOPS and a parallel efficiency of 90% for simulating a globular cluster of 1024K particles. In comparing with the GRAPE-6A cluster at RIT (Rochester Institute of Technology), the GraCCA system achieves a more than twice higher measured speed and an even higher performance-per-dollar ratio. Moreover, our system can handle up to 320M particles and can serve as a general-purpose computing cluster for a wide range of astrophysics problems.Comment: Accepted for publication in New Astronom

Plasmonic Circular Nanostructure for Enhanced Light Absorption in Organic Solar Cells

This study attempts to enhance broadband absorption in advanced plasmonic circular nanostructures (PCN). Experimental results indicate that the concentric circular metallic gratings can enhance broadband optical absorption, due to the structure geometry and the excitation of surface plasmon mode. The interaction between plasmonic enhancement and the absorption characteristics of the organic materials (P3HT:PCBM and PEDOT:PSS) are also examined. According to those results, the organic material's overall optical absorption can be significantly enhanced by up to ~51% over that of a planar device. Additionally, organic materials are enhanced to a maximum of 65% for PCN grating pitch = 800 nm. As a result of the PCN's enhancement in optical absorption, incorporation of the PCN into P3HT:PCBM-based organic solar cells (OSCs) significantly improved the performance of the solar cells: short-circuit current increased from 10.125 to 12.249 and power conversion efficiency from 3.2% to 4.99%. Furthermore, optimizing the OSCs architectures further improves the performance of the absorption and PCE enhancement