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Strong correlation effects caused by Hund's coupling have been actively studied during the past decade. Hund's metal, strongly correlated while far from the Mott insulating limit, was studied as a representative example. However, recently, it was revealed that a typical Mott system also exhibits a sign of Hund physics by investigating the kink structure in the spectral function of $\mathrm{NiS_{2-x}Se_x}$. Therefore, to understand the Hund physics in a half-filled multi-orbital system near the metal-insulator transition, we studied pressure-induced metallic states of $\mathrm{NiS_2}$ by using density functional theory plus dynamical mean-field theory. Hund physics, responsible for suppressing local spin fluctuation, gives low-energy effective correlations, separated from Mott physics, which suppresses charge fluctuation at higher energy. This effect is prominent when $J$ becomes comparable to the quasiparticle kinetic energy, showing apparent scaling behavior of the kink position $E_{kink} \sim J \cdot Z$. We suggest that the Hund effect can also be observed in the optical conductivity as a non-Drude-like tail with $1/\omega$ frequency dependence and non-monotonic temperature evolution of the integrated optical spectral weight at a fixed frequency. Our study demonstrates the important role of Hund's coupling for electronic correlations even in a half-filled system., Comment: 10 pages, 5 figures with following supplementary material (7 pages, 5 figures)

Diagnosis based on medical images, such as X-ray images, often involves manual annotation of anatomical keypoints. However, this process involves significant human efforts and can thus be a bottleneck in the diagnostic process. To fully automate this procedure, deep-learning-based methods have been widely proposed and have achieved high performance in detecting keypoints in medical images. However, these methods still have clinical limitations: accuracy cannot be guaranteed for all cases, and it is necessary for doctors to double-check all predictions of models. In response, we propose a novel deep neural network that, given an X-ray image, automatically detects and refines the anatomical keypoints through a user-interactive system in which doctors can fix mispredicted keypoints with fewer clicks than needed during manual revision. Using our own collected data and the publicly available AASCE dataset, we demonstrate the effectiveness of the proposed method in reducing the annotation costs via extensive quantitative and qualitative results. A demo video of our approach is available on our project webpage., Comment: MICCAI 2022. The first two authors contributed equally. The last two authors are the co-corresponding authors

Recent methods for single image super-resolution (SISR) have demonstrated outstanding performance in generating high-resolution (HR) images from low-resolution (LR) images. However, most of these methods show their superiority using synthetically generated LR images, and their generalizability to real-world images is often not satisfactory. In this paper, we pay attention to two well-known strategies developed for robust super-resolution (SR), i.e., reference-based SR (RefSR) and zero-shot SR (ZSSR), and propose an integrated solution, called reference-based zero-shot SR (RZSR). Following the principle of ZSSR, we train an image-specific SR network at test time using training samples extracted only from the input image itself. To advance ZSSR, we obtain reference image patches with rich textures and high-frequency details which are also extracted only from the input image using cross-scale matching. To this end, we construct an internal reference dataset and retrieve reference image patches from the dataset using depth information. Using LR patches and their corresponding HR reference patches, we train a RefSR network that is embodied with a non-local attention module. Experimental results demonstrate the superiority of the proposed RZSR compared to the previous ZSSR methods and robustness to unseen images compared to other fully supervised SISR methods., Comment: Accepted by IEEE Transactions on Multimedia (TMM)

Recent research on image denoising has progressed with the development of deep learning architectures, especially convolutional neural networks. However, real-world image denoising is still very challenging because it is not possible to obtain ideal pairs of ground-truth images and real-world noisy images. Owing to the recent release of benchmark datasets, the interest of the image denoising community is now moving toward the real-world denoising problem. In this paper, we propose a grouped residual dense network (GRDN), which is an extended and generalized architecture of the state-of-the-art residual dense network (RDN). The core part of RDN is defined as grouped residual dense block (GRDB) and used as a building module of GRDN. We experimentally show that the image denoising performance can be significantly improved by cascading GRDBs. In addition to the network architecture design, we also develop a new generative adversarial network-based real-world noise modeling method. We demonstrate the superiority of the proposed methods by achieving the highest score in terms of both the peak signal-to-noise ratio and the structural similarity in the NTIRE2019 Real Image Denoising Challenge - Track 2:sRGB., Comment: To appear in CVPR 2019 workshop. The winners of the NTIRE2019 Challenge on Image Denoising Challenge: Track 2 sRGB

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is widely used to build optoelectronic devices. However, as a hygroscopic water-based acidic material, it brings major concerns for stability and degradation, resulting in an intense effort to replace it in organic photovoltaic (OPV) devices. In this work, we focus on the perfluorinated ionomer (PFI) polymeric additive to PEDOT:PSS. We demonstrate that it can reduce the relative amplitude of OPV device burn-in, and find two distinct regimes of influence. At low concentrations there is a subtle effect on wetting and work function, for instance, with a detrimental impact on the device characteristics, and above a threshold it changes the electronic and device properties. The abrupt threshold in the conducting polymer occurs for PFI concentrations greater than or equal to the PSS concentration and was revealed by monitoring variations in transmission, topography, work-function, wettability and OPV device characteristics. Below this PFI concentration threshold, the power conversion efficiency (PCE) of OPVs based on poly(3-hexylthiophene-2,5-diyl):[6,6]-phenyl-C61-butyric acid methyl ester (P3HT:PCBM) are impaired largely by low fill-factors due to poor charge extraction. Above the PFI concentration threshold, we recover the PCE before it is improved beyond the pristine PEDOT:PSS layer based OPV devices. Supplementary to the performance enhancement, PFI improves OPV device stability and lifetime. Our degradation study leads to the conclusion that PFI prevents water from diffusing to and from the hygrosopic PEDOT:PSS layer, which slows down the deterioration of the PEDOT:PSS layer and the aluminum electrode. These findings reveal mechanisms and opportunities that should be taken into consideration when developing components to inhibit OPV degradation., Comment: 14 pages, 9 figures, 1 table, Journal of Materials Chemistry A 2018

Controlling the interconnection of neighboring seeds (nanoflakes) to full coverage of the textured substrate is the main challenge for the large-scale conformal growth of atomic-thick transition metal dichalcogenides by chemical vapor deposition. Herein, we report on a controllable method for the conformal growth of monolayer MoS2 on not only planar but also micro- and nano-rugged SiO2/Si substrates via metal-organic chemical vapor deposition. The continuity of monolayer MoS2 on the rugged surface is evidenced by scanning electron microscopy, cross-section high-resolution transmission electron microscopy, photoluminescence (PL) mapping, and Raman mapping. Interestingly, the photo-responsivity (~254.5 mA/W) of as-grown MoS2 on the nano-rugged substrate exhibits 59 times higher than that of the planar sample (4.3 mA/W) under a small applied bias of 0.1 V. This value is record high when compared with all previous MoS2-based photocurrent generation under low or zero bias. Such a large enhancement in the photo-responsivity arises from a large active area for light-matter interaction and local strain for PL quenching, where the latter effect is the key factor and unique in the conformally grown monolayer on the nano-rugged surface. The result is a step toward the batch fabrication of modern atomic-thick optoelectronic devices., Comment: 25 pages, 5 figures