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

Improving the Validity of Squeeze Film Air-Damping Model of MEMS Devices with Border Effect

Evaluation of squeezed film air damping is critical in the design and control of dynamic MEMS devices. The published squeezed film air damping models are generally derived from the analytical solutions of Reynolds equation or its other modified forms under the supposition of trivial pressure boundary conditions on the peripheral borders. These treatments ignoring the border effect can not give faithful result for structure with smaller air venting gap or the double-gimbaled structure in which the inner frame and outer one affect the air venting. In this paper, we use Green’s function to solve the nonlinear Reynolds equation with inhomogeneous boundary conditions. For two typical normal motion cases of parallel plate, the analytical models of squeeze film damping force with border effect are established. The viscous and inertial losses with real values and image values acoustic impedance are all included in the model. These models reduced the time consumption while giving satisfactory result. Without multifield coupling analysis, the estimation of the dynamic behavior of MEMS device is also allowed, and the simulation of the system performance is more convenient

Quantum phase transition of the Jaynes-Cummings model in the strong-coupling regime

We propose an experimentally feasible scheme to show the quantum phase transition of the Janeys-Cummings (JC) model by manipulating the transition frequency of a two-level system in a quantum Rabi model with strong coupling. By tunning the modulation frequency and amplitude, the ratio of the effective coupling strength of the rotating terms to the effective cavity (atomic transition) frequency can enter the deep-strong coupling regime, while the counter-rotating terms can be neglected. Thus a deep-strong JC model is obtained. The effective vacuum Rabi frequency is increased by two orders of magnitude compared to the original vacuum Rabi frequency. Our scheme works in both atom-cavity resonance and off-resonance cases, and it is valid in a broad range. The emerge of the quantum phase transition is indicated by the non-zero average cavity photons of the ground state. We also show the dependence of the phase diagram on the atom-cavity detuning and modulation parameters. All the parameters used are within the reach of current experiment technology. Our scheme provides a new mechanism for investigating the critical phenomena of finite component system without requiring classical field limit and opens a door for studying fundamental quantum phenomena in strong coupling regime that occurs in ultrastrong even deep-strong coupling regime.Comment: 7 pages, 5 figure

Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language Model

This paper introduces a new data augmentation method for neural machine translation that can enforce stronger semantic consistency both within and across languages. Our method is based on Conditional Masked Language Model (CMLM) which is bi-directional and can be conditional on both left and right context, as well as the label. We demonstrate that CMLM is a good technique for generating context-dependent word distributions. In particular, we show that CMLM is capable of enforcing semantic consistency by conditioning on both source and target during substitution. In addition, to enhance diversity, we incorporate the idea of soft word substitution for data augmentation which replaces a word with a probabilistic distribution over the vocabulary. Experiments on four translation datasets of different scales show that the overall solution results in more realistic data augmentation and better translation quality. Our approach consistently achieves the best performance in comparison with strong and recent works and yields improvements of up to 1.90 BLEU points over the baseline.Comment: Accepted to COLING 2022 Main Conference (Long paper). https://coling2022.org

IsaB Inhibits Autophagic Flux to Promote Host Transmission of Methicillin-Resistant Staphylococcus aureus.

Methicillin-resistant Staphylococcus aureus (MRSA) has emerged as a major nosocomial pathogen that is widespread in both health-care facilities and in the community at large, as a result of direct host-to-host transmission. Several virulence factors are associated with pathogen transmission to naive hosts. Immunodominant surface antigen B (IsaB) is a virulence factor that helps Staphylococcus aureus to evade the host defense system. However, the mechanism of IsaB on host transmissibility remains unclear. We found that IsaB expression was elevated in transmissible MRSA. Wild-type isaB strains inhibited autophagic flux to promote bacterial survival and elicit inflammation in THP-1 cells and mouse skin. MRSA isolates with increased IsaB expression showed decreased autophagic flux, and the MRSA isolate with the lowest IsaB expression showed increased autophagic flux. In addition, recombinant IsaB rescued the virulence of the isaB deletion strain and increased the group A streptococcus (GAS) virulence in vivo. Together, these results reveal that IsaB diminishes autophagic flux, thereby allowing MRSA to evade host degradation. These findings suggest that IsaB is a suitable target for preventing or treating MRSA infection