Multi-Style Unsupervised Image Synthesis Using Generative Adversarial Nets

Unsupervised cross-domain image-to-image translation is a very active topic in computer vision and graphics. This task has two challenges: 1) lack of paired training data and 2) numerous possible outputs from a single image. The existing methods rely on either paired data or perform one-to-one translation. A novel Multi-Style Unsupervised image synthesis model using Generative Adversarial Nets (MSU-GAN) is proposed in this paper to overcome these disadvantages. Firstly, the encoder-decoder structure is used to map the image to domain-shared content features space and domain-specific style features space. Secondly, to translate an image into another domain, the content code and the style code are combined to synthesize the resulting image. Finally, the bidirectional cycle-consistency loss is used for the unpaired training data; the inter-domain adversarial loss and the reconstruction loss are used to ensure the output image’s realism. Simultaneously, MSU-GAN is able to synthesize multi-style images due to disentangled representation. A Multi-Style Unsupervised Feature-Wise image synthesis model using Generative Adversarial Nets (MSU-FW-GAN) based on the MSU-GAN is proposed for the shape variation tasks. There are two different testing strategies, which include random style transfer and style guide transfer. For objective comparison, the proposed model performs well on all evaluation metrics. The random style transfer experiment results show that compared with CycleGAN on the photo2portraits dataset, MSU-FW-GAN FID, IS scores dropped by 12.77% and 8.06%. For the summer2winter dataset, MSU-GAN FID and IS scores increased by 24.51% and 3.64%. Qualitative results show that without paired training data, MSU-GAN and MSU-FW-GAN can synthesize multi-style and better realistic images on various tasks

Sparse Method for Direction of Arrival Estimation Using Denoised Fourth-Order Cumulants Vector

Fourth-order cumulants (FOCs) vector-based direction of arrival (DOA) estimation methods of non-Gaussian sources may suffer from poor performance for limited snapshots or difficulty in setting parameters. In this paper, a novel FOCs vector-based sparse DOA estimation method is proposed. Firstly, by utilizing the concept of a fourth-order difference co-array (FODCA), an advanced FOCs vector denoising or dimension reduction procedure is presented for arbitrary array geometries. Then, a novel single measurement vector (SMV) model is established by the denoised FOCs vector, and efficiently solved by an off-grid sparse Bayesian inference (OGSBI) method. The estimation errors of FOCs are integrated in the SMV model, and are approximately estimated in a simple way. A necessary condition regarding the number of identifiable sources of our method is presented that, in order to uniquely identify all sources, the number of sources K must fulfill K ≤ ( M 4 − 2 M 3 + 7 M 2 − 6 M ) / 8 . The proposed method suits any geometry, does not need prior knowledge of the number of sources, is insensitive to associated parameters, and has maximum identifiability O ( M 4 ) , where M is the number of sensors in the array. Numerical simulations illustrate the superior performance of the proposed method

A Two-Step Cross-Linked Hydrogel Immobilization Strategy for Diacetylchitobiose Deacetylase

Free enzymes often face economic problems due to their non-recyclability, which limits their applications for industrial manufacturing. Organic biopolymers are frequently used to fabricate hydrogel for enzyme immobilization due to their advantages of non-toxicity, biocompatibility, biodegradability, and flexibility. However, for highly thermostable enzymes, simple cross-linking causes either low immobilizing efficiency or low thermal stability. Herein, we developed a novel enzyme immobilization strategy with two-step cross-linked gelatin hydrogel for thermostable enzymes working at high temperature. The hydrogel was firstly “soft cross-linked” to immobilize most enzyme molecules and then “hard cross-linked” to gain strong thermal stability. We selected the enzyme diacetylchitobiose deacetylase (Dac), which was firstly derived from hyperthermophilic bacteria, to demonstrate the advantages of our method. With the optimized fabrication steps, our hydrogel showed ~87% Dac immobilization efficiency and excellent stability against heating, dehydrating, long-time storing, and massive recycling. Importantly, our hydrogel showed ~85.0% relative enzyme activity at 80 °C and retained ~65.8% activity after 10 rounds of catalysis. This strategy showed high immobilizing efficiency and strong thermal stability and we believe it could improve the industrial potential for those enzymes