Robust PCA by Manifold Optimization

Robust PCA is a widely used statistical procedure to recover a underlying low-rank matrix with grossly corrupted observations. This work considers the problem of robust PCA as a nonconvex optimization problem on the manifold of low-rank matrices, and proposes two algorithms (for two versions of retractions) based on manifold optimization. It is shown that, with a proper designed initialization, the proposed algorithms are guaranteed to converge to the underlying low-rank matrix linearly. Compared with a previous work based on the Burer-Monterio decomposition of low-rank matrices, the proposed algorithms reduce the dependence on the conditional number of the underlying low-rank matrix theoretically. Simulations and real data examples confirm the competitive performance of our method

Cross-Linked PDMS Expansion Due to Submersion in Liquid and Supercritical CO2

Characterization of micro/nano-copper particles impregnated Polydimethylsiloxane (PDMS) submersed in supercritical carbon dioxide (scCO2) was studied. The purpose of this investigation was to advance micro-corrosion sensor technology utilizing PDMS and micro-metal particle composite as the sensing element currently under-development. One of the key challenges encountered was the removal of the native oxides inherently existing on the metal particles. Numerous techniques were experimented with to counter this problem at the UA Engineered Micro/Nano Systems Laboratory (EMNSL), with swell-based protocols being identified as the most promising solution. In terms of compatibility to Micro-electro-mechanical Systems (MEMS) fabrication, CO2 is often used in the release of stiction for sensitive microstructures. The experimental method was classified as low temperature techniques (less than 100 degrees Celsius). Commonly, the composite exhibits expansion ratio from 2.5% to 20%, exhibiting more sensitivity to the percentage content of the metal particles, albeit below those reported in literature for pure cross-linked PDMS. The expansion time-constant is found to be on the order of 100 to 1000 seconds

TV-GAN: Generative Adversarial Network Based Thermal to Visible Face Recognition

This work tackles the face recognition task on images captured using thermal camera sensors which can operate in the non-light environment. While it can greatly increase the scope and benefits of the current security surveillance systems, performing such a task using thermal images is a challenging problem compared to face recognition task in the Visible Light Domain (VLD). This is partly due to the much smaller amount number of thermal imagery data collected compared to the VLD data. Unfortunately, direct application of the existing very strong face recognition models trained using VLD data into the thermal imagery data will not produce a satisfactory performance. This is due to the existence of the domain gap between the thermal and VLD images. To this end, we propose a Thermal-to-Visible Generative Adversarial Network (TV-GAN) that is able to transform thermal face images into their corresponding VLD images whilst maintaining identity information which is sufficient enough for the existing VLD face recognition models to perform recognition. Some examples are presented in Figure 1. Unlike the previous methods, our proposed TV-GAN uses an explicit closed-set face recognition loss to regularize the discriminator network training. This information will then be conveyed into the generator network in the forms of gradient loss. In the experiment, we show that by using this additional explicit regularization for the discriminator network, the TV-GAN is able to preserve more identity information when translating a thermal image of a person which is not seen before by the TV-GAN