Low-Energy Surface States in the Normal State of α\alpha-PdBi2 Superconductor

Topological superconductors as characterized by Majorana surface states has been actively searched for their significance in fundamental science and technological implication. The large spin-orbit coupling in Bi-Pd binaries has stimulated extensive investigations on the topological surface states in these superconducting compounds. Here we report a study of normal-state electronic structure in a centrosymmetric $\alpha$-PdBi2 within density functional theory calculations. By investigating the electronic structure from the bulk to slab geometries in this system, we predict for the first time that $\alpha$-PdBi2 can host orbital-dependent and asymmetric Rashba surface states near the Fermi energy. This study suggests that $\alpha$-PdBi2 will be a good candidate to explore the relationship between superconductivity and topology in condensed matter physics

Temperature-evolution of spectral function and optical conductivity in heavy fermion compound Ce2_{2}IrIn8_{8} under crystalline electric field

We investigate the role of the crystalline electric field (CEF) in the temperature ($T$)-evolution of the Kondo resonance states and its effect on optical conductivity. We perform the combined first principles calculation of the density functional theory and dynamical mean field theory on Ce$_{2}$IrIn$_{8}$. The calculated spectral function reproduces the experimental observed CEF states at low $T$, while it shows a drastic change of the Fermi surface upon increasing $T$. The effect of the CEF states on the Fermi surface as a function of $T$ is elucidated through the first principles calculations as well as the analysis on the Anderson impurity model. Consequently, we suggest the importance of the CEF-driven orbital anisotropy in the low-energy states of optical experiments.Comment: 6 pages, 4 figure

COVID-19 CXR Classification: Applying Domain Extension Transfer Learning and Deep Learning

The infectious coronavirus disease-19 (COVID-19) is a viral disease that affects the lungs, which caused great havoc when the epidemic rapidly spread around the world. Polymerase chain reaction (PCR) tests are conducted to screen for COVID-19 and respond to quarantine measures. However, PCR tests take a considerable amount of time to confirm the test results. Therefore, to supplement the accuracy and quickness of a COVID-19 diagnosis, we proposed an effective deep learning methodology as a quarantine response through COVID-19 chest X-ray image classification based on domain extension transfer learning. As part of the data preprocessing, contrast limited adaptive histogram equalization was applied to chest X-ray images using Medical Information Mart for Intensive Care (MIMIC)-IV obtained from the Beth Israel Deaconess Medical Center. The classification of the COVID-19 X-ray images was conducted using a pretrained ResNet-50. We also visualized and interpreted the classification performance of the model through explainable artificial intelligence and performed statistical tests to validate the reliability of the model. The proposed method correctly classified images with 96.7% accuracy, an improvement of about 9.9% over the reference model. This study is expected to help medical staff make an integrated decision in selecting the first confirmed case and contribute to suppressing the spread of the virus in the community

Lightweight Skip Connections With Efficient Feature Stacking for Respiratory Sound Classification

As the number of deaths from respiratory diseases due to COVID-19 and infectious diseases increases, early diagnosis is necessary. In general, the diagnosis of diseases is based on imaging devices (e.g., computed tomography and magnetic resonance imaging) as well as the patient’s underlying disease information. However, these examinations are time-consuming, incur considerable costs, and in a situation like the ongoing pandemic, face-to-face examinations are difficult to conduct. Therefore, we propose a lung disease classification model based on deep learning using non-contact auscultation. In this study, two respiratory specialists collected normal respiratory sounds and five types of abnormal sounds associated with lung disease, including those associated with four lung lesions in the left and right anterior chest and left and right posterior chest. For preprocessing and feature extraction, the noise was removed using three pass filters (low, band, and high), and respiratory sound features were extracted using the Log-Mel Spectrogram-Mel Frequency Cepstral Coefficient followed by feature stacking. Then, we propose a lung disease classification model of dense lightweight convolutional neural network-bidirectional gated recurrent unit skip connections using depthwise separable convolution based on the extracted respiratory sound information. The performance of the classification model was compared with both the baseline and the lightweight models. The results indicate that the proposed model achieves high performance and has an accuracy of 92.3%, sensitivity of 92.1%, specificity of 98.5%, and f1-score of 91.9%. Using the proposed model, we aim to contribute to the early detection of diseases during the COVID-19 pandemic