Blue Phosphorene Oxide: Strain-tunable Quantum Phase Transitions and Novel 2D Emergent Fermions

Tunable quantum phase transitions and novel emergent fermions in solid state materials are fascinating subjects of research. Here, we propose a new stable two-dimensional (2D) material, the blue phosphorene oxide (BPO), which exhibits both. Based on first-principles calculations, we show that its equilibrium state is a narrow-bandgap semiconductor with three bands at low energy. Remarkably, a moderate strain can drive a semiconductor-to-semimetal quantum phase transition in BPO. At the critical transition point, the three bands cross at a single point at Fermi level, around which the quasiparticles are a novel type of 2D pseudospin-1 fermions. Going beyond the transition, the system becomes a symmetry-protected semimetal, for which the conduction and valence bands touch quadratically at a single Fermi point that is protected by symmetry, and the low-energy quasiparticles become another novel type of 2D double Weyl fermions. We construct effective models characterizing the phase transition and these novel emergent fermions, and we point out several exotic effects, including super Klein tunneling, supercollimation, and universal optical absorbance. Our result reveals BPO as an intriguing platform for the exploration of fundamental properties of quantum phase transitions and novel emergent fermions, and also suggests its great potential in nanoscale device applications.Comment: 23 pages, 5 figure

Study Majorana Neutrino Contribution to B-meson Semi-leptonic Rare Decays

B meson semi-leptonic rare decays are sensitive to new physics beyond standard model. We study the $B^{-}\to \pi^{-}\mu^{+}\mu^{-}$ process and investigate the Majorana neutrino contribution to its decay width. The constraints on the Majorana neutrino mass and mixing parameter are obtained from this decay channel with the latest LHCb data. Utilizing the best fit for the parameters, we study the lepton number violating decay $B^{-}\to \pi^{+}\mu^{-}\mu^{-}$, and find its branching ratio is about $6.4\times10^{-10}$, which is consistent with the LHCb data reported recently.Comment: 10 pages, 3 figure

Memory augment is All You Need for image restoration

Image restoration is a low-level vision task, most CNN methods are designed as a black box, lacking transparency and internal aesthetics. Although some methods combining traditional optimization algorithms with DNNs have been proposed, they all have some limitations. In this paper, we propose a three-granularity memory layer and contrast learning named MemoryNet, specifically, dividing the samples into positive, negative, and actual three samples for contrastive learning, where the memory layer is able to preserve the deep features of the image and the contrastive learning converges the learned features to balance. Experiments on Derain/Deshadow/Deblur task demonstrate that these methods are effective in improving restoration performance. In addition, this paper's model obtains significant PSNR, SSIM gain on three datasets with different degradation types, which is a strong proof that the recovered images are perceptually realistic. The source code of MemoryNet can be obtained from https://github.com/zhangbaijin/MemoryNe

NeAI: A Pre-convoluted Representation for Plug-and-Play Neural Ambient Illumination

Recent advances in implicit neural representation have demonstrated the ability to recover detailed geometry and material from multi-view images. However, the use of simplified lighting models such as environment maps to represent non-distant illumination, or using a network to fit indirect light modeling without a solid basis, can lead to an undesirable decomposition between lighting and material. To address this, we propose a fully differentiable framework named neural ambient illumination (NeAI) that uses Neural Radiance Fields (NeRF) as a lighting model to handle complex lighting in a physically based way. Together with integral lobe encoding for roughness-adaptive specular lobe and leveraging the pre-convoluted background for accurate decomposition, the proposed method represents a significant step towards integrating physically based rendering into the NeRF representation. The experiments demonstrate the superior performance of novel-view rendering compared to previous works, and the capability to re-render objects under arbitrary NeRF-style environments opens up exciting possibilities for bridging the gap between virtual and real-world scenes. The project and supplementary materials are available at https://yiyuzhuang.github.io/NeAI/.Comment: Project page: <a class="link-external link-https" href="https://yiyuzhuang.github.io/NeAI/" rel="external noopener nofollow">https://yiyuzhuang.github.io/NeAI/</a

Genes related to the very early stage of ConA-induced fulminant hepatitis: a gene-chip-based study in a mouse model

<p>Abstract</p> <p>Background</p> <p>Due to the high morbidity and mortality of fulminant hepatitis, early diagnosis followed by early effective treatment is the key for prognosis improvement. So far, little is known about the gene expression changes in the early stage of this serious illness. Identification of the genes related to the very early stage of fulminant hepatitis development may provide precise clues for early diagnosis.</p> <p>Results</p> <p>Balb/C mice were used for ConA injection to induce fulminant hepatitis that was confirmed by pathological and biochemical examination. After a gene chip-based screening, the data of gene expression in the liver, was further dissected by ANOVA analysis, gene expression profiles, gene network construction and real-time RT-PCR.</p> <p>At the very early stage of ConA-triggered fulminant hepatitis, totally 1,473 genes with different expression variations were identified. Among these, 26 genes were finally selected for further investigation. The data from gene network analysis demonstrate that two genes, MPDZ and Acsl1, localized in the core of the network.</p> <p>Conclusions</p> <p>At the early stages of fulminant hepatitis, expression of twenty-six genes involved in protein transport, transcription regulation and cell metabolism altered significantly. These genes form a network and have shown strong correlation with fulminant hepatitis development. Our study provides several potential targets for the early diagnosis of fulminant hepatitis.</p