An efficient cascade bicyclization strategy for the construction of γ-lactam containing 3,3-disubstituted oxindole derivatives under silver catalysis is described. The method enables the sequential assembly of both unactivated and activated...
<p>Convolution-augmented transformers (Conformers) are recently proposed in various speech-domain applications, such as automatic speech recognition (ASR) and speech separation, as they can capture both local and global dependencies. In this paper, we propose a conformer-based metric generative adversarial network (CMGAN) for speech enhancement (SE) in the time-frequency (TF) domain. The generator encodes the magnitude and complex spectrogram information using two-stage conformer blocks to model both time and frequency dependencies. The decoder then decouples the estimation into a magnitude mask decoder branch to filter out unwanted distortions and a complex refinement branch to further improve the magnitude estimation and implicitly enhance the phase information. Additionally, we include a metric discriminator to alleviate metric mismatch by optimizing the generator with respect to a corresponding evaluation score. Objective and subjective evaluations illustrate that CMGAN is able to show superior performance compared to state-of-the-art methods in three speech enhancement tasks (denoising, dereverberation and super-resolution). For instance, quantitative denoising analysis on Voice Bank+DEMAND dataset indicates that CMGAN outperforms various previous models with a margin, i.e., PESQ of 3.41 and SSNR of 11.10 dB. </p>
<p>Convolution-augmented transformers (Conformers) are recently proposed in various speech-domain applications, such as automatic speech recognition (ASR) and speech separation, as they can capture both local and global dependencies. In this paper, we propose a conformer-based metric generative adversarial network (CMGAN) for speech enhancement (SE) in the time-frequency (TF) domain. The generator encodes the magnitude and complex spectrogram information using two-stage conformer blocks to model both time and frequency dependencies. The decoder then decouples the estimation into a magnitude mask decoder branch to filter out unwanted distortions and a complex refinement branch to further improve the magnitude estimation and implicitly enhance the phase information. Additionally, we include a metric discriminator to alleviate metric mismatch by optimizing the generator with respect to a corresponding evaluation score. Objective and subjective evaluations illustrate that CMGAN is able to show superior performance compared to state-of-the-art methods in three speech enhancement tasks (denoising, dereverberation and super-resolution). For instance, quantitative denoising analysis on Voice Bank+DEMAND dataset indicates that CMGAN outperforms various previous models with a margin, i.e., PESQ of 3.41 and SSNR of 11.10 dB. </p>
Inertia may significantly influence the transient deformation process and the steady-state structure of a deformable capsule. The behavior of a two-dimensional deformable capsule in shear flow at finite Reynolds numbers (Re) is studied numerically. By simulating numerous cases with different Re and frequencies (f), we observed persistent oscillation, asymmetric oscillation, deflected oscillation, and stable modes. The phase diagram in the Re-f plane is presented. At low frequencies, a capsule shows a phase-lag phenomenon between the deformation and the applied shear. At moderate frequencies, the anomaly of decreasing maximum deformation with increasing Re is observed. The anomaly is attributed to the mode shift. In addition, a scaling law of the maximum deformation of the capsule as a function of Re and f is proposed. The study may shed some light on the identification and screening of cells in vitro, as well as the transport and breakup of cells in vivo.
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