Electronic excitations in the edge-shared relativistic Mott insulator: Na2IrO3

We have investigated the excitation spectra of j(eff) = 1/2 Mott insulator Na2IrO3. Taking into account a relativistic multiplet structure of Ir ions, we have calculated the optical conductivity sigma(omega) and resonant inelastic x-ray scattering (RIXS) spectra, which manifest different features from those of a canonical j(eff) = 1/2 system Sr2IrO4. Distinctly from the two-peak structure in Sr2IrO4, sigma(omega) in Na2IrO3 has a broad single peak dominated by interband transitions from j(eff) = 3/2 to 1/2. RIXS spectra exhibit the spin-orbit (SO) exciton that has a two-peak structure arising from the crystal-field effect, and the magnon peak at energies much lower than in Sr2IrO4. In addition, a small peak near the optical-absorption edge is found in RIXS spectra, originating from the coupling between the electron-hole (e-h) excitation and the SO exciton. Our findings corroborate the validity of the relativistic electronic structure and importance of both itinerant and local features in Na2IrO3.open1122sciescopu

The mechanism of charge density wave in Pt-based layered superconductors: SrPt2As2 and LaPt2Si2

The intriguing coexistence of the charge density wave (CDW) and superconductivity in SrPt2As2 and LaPt2Si2 has been investigated based on the ab initio density functional theory band structure and phonon calculations. We have found that the CDW instabilities for both cases arise from the q-dependent electron-phonon coupling with quasi-nesting feature of the Fermi surface. The band structure obtained by the band-unfolding technique reveals the sizable q-dependent electron-phonon coupling responsible for the CDW instability. The local split distortions of Pt atoms in the [As-Pt-As] layers play an essential role in driving the five-fold supercell CDW instability as well as the phonon softening instability in SrPt2As2. By contrast, the CDW and phonon softening instabilities in LaPt2Si2 occur without split distortions of Pt atoms. The phonon calculations suggest that the CDW and the superconductivity coexist in [X-Pt-X] layers (X = As or Si) for both cases.1175Ysciescopu

Dimerization-Induced Fermi-Surface Reconstruction in IrTe2

We report a de Haas-van Alphen (dHvA) oscillation study on IrTe2 single crystals showing complex dimer formations. By comparing the angle dependence of dHvA oscillations with band structure calculations, we show distinct Fermi surface reconstruction induced by a 1/5-type and a 1/8-type dimerizations. This verifies that an intriguing quasi-two-dimensional conducting plane across the layers is induced by dimerization in both cases. A phase transition to the 1/8 phase with higher dimer density reveals that local instabilities associated with intra-and interdimer couplings are the main driving force for complex dimer formations in IrTe2.X11149sciescopu

Automatic Tumor Segmentation via False Positive Reduction Network for Whole-Body Multi-Modal PET/CT Images

Multi-modality Fluorodeoxyglucose (FDG) positron emission tomography / computed tomography (PET/CT) has been routinely used in the assessment of common cancers, such as lung cancer, lymphoma, and melanoma. This is mainly attributed to the fact that PET/CT combines the high sensitivity for tumor detection of PET and anatomical information from CT. In PET/CT image assessment, automatic tumor segmentation is an important step, and in recent years, deep learning based methods have become the state-of-the-art. Unfortunately, existing methods tend to over-segment the tumor regions and include regions such as the normal high uptake organs, inflammation, and other infections. In this study, we introduce a false positive reduction network to overcome this limitation. We firstly introduced a self-supervised pre-trained global segmentation module to coarsely delineate the candidate tumor regions using a self-supervised pre-trained encoder. The candidate tumor regions were then refined by removing false positives via a local refinement module. Our experiments with the MICCAI 2022 Automated Lesion Segmentation in Whole-Body FDG-PET/CT (AutoPET) challenge dataset showed that our method achieved a dice score of 0.9324 with the preliminary testing data and was ranked 1st place in dice on the leaderboard. Our method was also ranked in the top 7 methods on the final testing data, the final ranking will be announced during the 2022 MICCAI AutoPET workshop. Our code is available at: https://github.com/YigePeng/AutoPET_False_Positive_Reduction.Comment: Pre-print paper for 2022 MICCAI AutoPET Challeng

Improving Skin Lesion Segmentation via Stacked Adversarial Learning

Segmentation of skin lesions is an essential step in computer aided diagnosis (CAD) for the automated melanoma diagnosis. Recently, segmentation methods based on fully convolutional networks (FCNs) have achieved great success for general images. This success is primarily related to FCNs leveraging large labelled datasets to learn features that correspond to the shallow appearance and the deep semantics of the images. Such large labelled datasets, however, are usually not available for medical images. So researchers have used specific cost functions and post-processing algorithms to refine the coarse boundaries of the results to improve the FCN performance in skin lesion segmentation. These methods are heavily reliant on tuning many parameters and post-processing techniques. In this paper, we adopt the generative adversarial networks (GANs) given their inherent ability to produce consistent and realistic image features by using deep neural networks and adversarial learning concepts. We build upon the GAN with a novel stacked adversarial learning architecture such that skin lesion features can be learned, iteratively, in a class-specific manner. The outputs from our method are then added to the existing FCN training data, thus increasing the overall feature diversity. We evaluated our method on the ISIC 2017 skin lesion segmentation challenge dataset; we show that it is more accurate and robust when compared to the existing skin state-of-the-art methods