Co-Teaching for Unsupervised Domain Adaptation and Expansion

Unsupervised Domain Adaptation (UDA) is known to trade a model's performance on a source domain for improving its performance on a target domain. To resolve the issue, Unsupervised Domain Expansion (UDE) has been proposed recently to adapt the model for the target domain as UDA does, and in the meantime maintain its performance on the source domain. For both UDA and UDE, a model tailored to a given domain, let it be the source or the target domain, is assumed to well handle samples from the given domain. We question the assumption by reporting the existence of cross-domain visual ambiguity: Due to the lack of a crystally clear boundary between the two domains, samples from one domain can be visually close to the other domain. We exploit this finding and accordingly propose in this paper Co-Teaching (CT) that consists of knowledge distillation based CT (kdCT) and mixup based CT (miCT). Specifically, kdCT transfers knowledge from a leader-teacher network and an assistant-teacher network to a student network, so the cross-domain visual ambiguity will be better handled by the student. Meanwhile, miCT further enhances the generalization ability of the student. Comprehensive experiments on two image-classification benchmarks and two driving-scene-segmentation benchmarks justify the viability of the proposed method

Automatic COVID-19 Detection from Cough Sounds Using Multi-Headed Convolutional Neural Networks

Novel coronavirus disease 2019 (Corona Virus Disease 2019, COVID-19) is rampant all over the world, threatening human life and health. Currently, the detection of the presence of nucleic acid from SARS-CoV-2 is mainly based on the nucleic acid test as the standard. However, this method not only takes up a lot of medical resources but also takes a long time to achieve detection results. According to medical analysis, the surface protein of the novel coronavirus can invade the respiratory epithelial cells of patients and cause severe inflammation of the respiratory system, making the cough of COVID-19 patients different from that of healthy people. In this study, the cough sound is used as a large-scale pre-screening method before the nucleic acid test. Firstly, the Mel spectrum features, Mel Frequency Cepstral Coefficients, and VGG embeddings features of cough sound are extracted and oversampling technology is used to balance the dataset for classes with a small number of samples. In terms of the model, we designed multi-headed convolutional neural networks to predict audio samples, and adopted an early stop method to avoid the over-fitting problem of the model. The performance of the model is measured by the binary cross-entropy loss function. Our model performs well on the dataset of the AICovidVN 115M challenge that its accuracy rate is 98.1%, and on the dataset of the University of Cambridge that its accuracy rate is 91.36%

Assessing the Use of GACOS Products for SBAS-InSAR Deformation Monitoring: A Case in Southern California

The Generic Atmospheric Correction Online Service (GACOS) products for interferometric synthetic aperture radar (InSAR) are widely used near-real-time and global-coverage atmospheric delay products which provide a new approach for the atmospheric correction of repeat-pass InSAR. However, it has not been determined whether these products can improve the accuracy of InSAR deformation monitoring. In this paper, GACOS products were used to correct atmospheric errors in short baseline subset (SBAS)-InSAR. Southern California in the U.S. was selected as the research area, and the effect of GACOS-based SBAS-InSAR was analyzed by comparing with classical SBAS-InSAR results and external global positioning system (GPS) data. The results showed that the accuracy of deformation monitoring was improved in the whole study area after GACOS correction, and the mean square error decreased from 0.34 cm/a to 0.31 cm/a. The improvement of the mid-altitude (15–140 m) point was the most obvious after GACOS correction, and the accuracy was increased by about 23%. The accuracy for low- and high-altitude areas was roughly equal and there was no significant improvement. Additionally, GACOS correction may increase the error for some points, which may be related to the low accuracy of GACOS turbulence data

Evaluation of Tidal Effect in Long-Strip DInSAR Measurements Based on GPS Network and Tidal Models

A long-strip differential interferometric synthetic aperture radar (DInSAR) measurement based on multi-frame image mosaicking is currently the realizable approach to measure large-scale ground deformation. As the spatial range of the mosaicked images increases, the nonlinear variation of ground ocean tidal loading (OTL) displacements is more significant, and using plane fitting to remove the large-scale errors will produce large tidal displacement residuals in a region with a complex coastline. To conveniently evaluate the ground tidal effect on mosaic DInSAR interferograms along the west coast of the U.S., a three-dimensional ground OTL displacements grid is generated by integrating tidal constituents’ estimation of the GPS reference station network and global/regional ocean tidal models. Meanwhile, a solid earth tide (SET) model based on IERS conventions is used to estimate the high-precision SET displacements. Experimental results show that the OTL and SET in a long-strip interferogram can reach 77.5 mm, which corresponds to a 19.3% displacement component. Furthermore, the traditional bilinear ramp fitting methods will cause 7.2~20.3 mm residual tidal displacement in the mosaicked interferograms, and the integrated tidal constituents displacements calculation method can accurately eliminate the tendency of tidal displacement in the long-strip interferograms

Recrystallization-Induced Surface Cracks of Carbon Ions Irradiated 6H-SiC after Annealing

Single crystal 6H-SiC wafers with 4° off-axis [0001] orientation were irradiated with carbon ions and then annealed at 900 °C for different time periods. The microstructure and surface morphology of these samples were investigated by grazing incidence X-ray diffraction (GIXRD), scanning electron microscopy (SEM), and transmission electron microscopy (TEM). Ion irradiation induced SiC amorphization, but the surface was smooth and did not have special structures. During the annealing process, the amorphous SiC was recrystallized to form columnar crystals that had a large amount of twin structures. The longer the annealing time was, the greater the amount of recrystallized SiC would be. The recrystallization volume fraction was accorded with the law of the Johnson–Mehl–Avrami equation. The surface morphology consisted of tiny pieces with an average width of approximately 30 nm in the annealed SiC. The volume shrinkage of irradiated SiC layer and the anisotropy of newly born crystals during annealing process produced internal stress and then induced not only a large number of dislocation walls in the non-irradiated layer but also the initiation and propagation of the cracks. The direction of dislocation walls was perpendicular to the growth direction of the columnar crystal. The longer the annealing time was, the larger the length and width of the formed crack would be. A quantitative model of the crack growth was provided to calculate the length and width of the cracks at a given annealing time