Evolutionary Multiobjective Optimization Driven by Generative Adversarial Networks (GANs)

Recently, increasing works have proposed to drive evolutionary algorithms using machine learning models. Usually, the performance of such model based evolutionary algorithms is highly dependent on the training qualities of the adopted models. Since it usually requires a certain amount of data (i.e. the candidate solutions generated by the algorithms) for model training, the performance deteriorates rapidly with the increase of the problem scales, due to the curse of dimensionality. To address this issue, we propose a multi-objective evolutionary algorithm driven by the generative adversarial networks (GANs). At each generation of the proposed algorithm, the parent solutions are first classified into real and fake samples to train the GANs; then the offspring solutions are sampled by the trained GANs. Thanks to the powerful generative ability of the GANs, our proposed algorithm is capable of generating promising offspring solutions in high-dimensional decision space with limited training data. The proposed algorithm is tested on 10 benchmark problems with up to 200 decision variables. Experimental results on these test problems demonstrate the effectiveness of the proposed algorithm

Periodicity and self-similarity of vortex evolution in a double-lid-driven cavity flow

AbstractThe flow configuration of the two-dimensional low Reynolds number flow in a rectangular cavity with two opposite moving lids and different depth-to-width ratios is investigated. The effects of aspect ratio varying from 0.15 to 6.6 on vortex structure in the cavity were numerated using the differential quadrature method. The critical aspect ratios, streamline patterns and bifurcation diagrams were presented. It is found that the vortex structure distributes in the transverse direction of cavity and the sub-eddy centers gradually merge as aspect ratio increases from 0.15 to 0.7. When the aspect ratio is larger than 0.7, the flow structure unfolds in the longitudinal direction of cavity and the number of vortices gradually increases with the aspect ratio increasing. The evolution of flow pattern exhibits the characteristics of periodicity and self-similarity. The large outer vortices evolve from the growth of new vortices in the middle region of cavity. The flow patterns are always symmetric about the cavity centre at different aspect ratios

Multimodal Image-to-Image Translation via a Single Generative Adversarial Network

Despite significant advances in image-to-image (I2I) translation with Generative Adversarial Networks (GANs) have been made, it remains challenging to effectively translate an image to a set of diverse images in multiple target domains using a pair of generator and discriminator. Existing multimodal I2I translation methods adopt multiple domain-specific content encoders for different domains, where each domain-specific content encoder is trained with images from the same domain only. Nevertheless, we argue that the content (domain-invariant) features should be learned from images among all the domains. Consequently, each domain-specific content encoder of existing schemes fails to extract the domain-invariant features efficiently. To address this issue, we present a flexible and general SoloGAN model for efficient multimodal I2I translation among multiple domains with unpaired data. In contrast to existing methods, the SoloGAN algorithm uses a single projection discriminator with an additional auxiliary classifier, and shares the encoder and generator for all domains. As such, the SoloGAN model can be trained effectively with images from all domains such that the domain-invariant content representation can be efficiently extracted. Qualitative and quantitative results over a wide range of datasets against several counterparts and variants of the SoloGAN model demonstrate the merits of the method, especially for the challenging I2I translation tasks, i.e., tasks that involve extreme shape variations or need to keep the complex backgrounds unchanged after translations. Furthermore, we demonstrate the contribution of each component using ablation studies.Comment: pages 13, 15 figure

Monitoring Rice Phenology Based on Backscattering Characteristics of Multi-Temporal RADARSAT-2 Datasets

Accurate estimation and monitoring of rice phenology is necessary for the management and yield prediction of rice. The radar backscattering coefficient, one of the most direct and accessible parameters has been proved to be capable of retrieving rice growth parameters. This paper aims to investigate the possibility of monitoring the rice phenology (i.e., transplanting, vegetative, reproductive, and maturity) using the backscattering coefficients or their simple combinations of multi-temporal RADARSAT-2 datasets only. Four RADARSAT-2 datasets were analyzed at 30 sample plots in Meishan City, Sichuan Province, China. By exploiting the relationships of the backscattering coefficients and their combinations versus the phenology of rice, HH/VV, VV/VH, and HH/VH ratios were found to have the greatest potential for phenology monitoring. A decision tree classifier was applied to distinguish the four phenological phases, and the classifier was effective. The validation of the classifier indicated an overall accuracy level of 86.2%. Most of the errors occurred in the vegetative and reproductive phases. The corresponding errors were 21.4% and 16.7%, respectively

Development of Pan-filovirus vaccine against Ebola and Marburg virus challenges

Filoviruses such as Ebola (EBOV) and Marburg (MARV) viruses cause deadly viral hemorrhagic fever in humans with high case fatality rates. To date, no licenced therapeutic or vaccine has been clinically approved to prevent infection. Several vaccine candidates are under development against the few most common filoviruses targeting the virus glycoprotein (GP). However, protective antibodies induced by such GP vaccines are usually limited to the same species. In contrast, T-cell vaccines offer an opportunity to design a single pan-filovirus vaccine protecting against all members of the Filoviridae family. In this study FILOcepX vaccines were constructed targeting the four most conserved regions among the viral proteomes with the aim to induce protective T-cell responses against different filoviruses. BALB/c mice were immunized with FILOcep 1 and 2 vaccines vectored by non-replicating engineered simian adenovirus and poxvirus MVA. Groups of 20 BALB/c mice were primed and boosted with either the FILOcep1 and FILOcep2 vaccines or control ChAdOx1- and MVA-vectored vaccines. Four animals in each group were sacrificed after 1 week of boosting to detect T-cell response for the FILOcepX antigen. High frequency T cells specific responses were detected in mice receiving the test vaccines by IFN-γ ELISPOT kits. Of the remaining 16 animals in each group, 8 were challenged with mouse-adapted EBOV and 8 were challenged with mouse adapted MARV in Containment Level 4 laboratory. All the mice in the control group either died or had to be euthanized between 4 and 6 days post challenge. On the other hand all the FILOcepX vaccinated mice maintained their normal body mass and survived till the end of the scheduled protocol on day 29 post challenge. These FILOcepX vaccines provided 100% protection against the lethal challenges with filoviruses of two different genera. Further evaluation the efficacy of this vaccine in non-human primates (NHPs) is warranted