Quantum-dot gain without inversion:Effects of dark plasmon-exciton hybridization

We propose an initial-state-dependent quantum-dot gain without population inversion in the vicinity of a resonant metallic nanoparticle. The gain originates from the hybridization of a dark plasmon-exciton and is accompanied by efficient energy transfer from the nanoparticle to the quantum dot. This hybridization of the dark plasmon-exciton, attached to the hybridization of the bright plasmon-exciton, strengthens nonlinear light-quantum emitter interactions at the nanoscale, thus the spectral overlap between the dark and the bright plasmons enhances the gain effect. This hybrid system has potential applications in ultracompact tunable quantum devices.Physics, Condensed MatterSCI(E)[email protected]

Dynamical properties of quantum many-body systems with long range interactions

Employing large-scale quantum Monte Carlo simulations, we systematically compute the energy spectra of the 2D spin-1/2 Heisenberg model with long-range interactions. With the $1/r^{\alpha}$ ferromagnetic and staggered antiferromagnetic interactions, we find the explicit range in $\alpha$ for {\color{black} the short-range Goldstone-type (gapless), anomalous Goldstone-type (gapless) and Higgs-type (gapped) spectra. Accompanied by the spin wave analysis, our numerical results vividly reveal how the long-range interactions alter the usual linear and quadratic magnon dispersions in 2D quantum magnets and give rise to anomalous dynamical exponents. Moreover, we find explicit case where the gapped excitation exists even when the Hamiltonian is extensive. This work provides the first set of unbiased dynamical data} of long-range quantum many-body systems and suggests that many universally accepted low-energy customs for short-range systems need to be substantially modified for long-range ones which are of immediate relevance to the ongoing experimental efforts from quantum simulators to 2D quantum moir\'e materials.Comment: 5 pages,3 figure

Finite-temperature critical behaviors in 2D long-range quantum Heisenberg model

The well-known Mermin-Wagner theorem prohibits the existence of finite-temperature spontaneous continuous symmetry breaking phase in systems with short-range interactions at spatial dimension $D\le 2$ [Phys. Rev. 158, 383; Phys. Rev. Lett. 17, 1133; Journal of Statistical Physics 175, 521-529]. For long-range interaction with monotonic power-law form ($1/r^{\alpha}$), the theorem further forbids a ferro- or antiferromagnetic order at finite temperature when $\alpha\ge 2D$[Phys. Rev. Lett. 87, 137203]. However, the situation for $\alpha \in (2,4)$ at $D=2$ is beyond the predicting power of the theorem and the situation is still unclear. Here we address this question by large-scale quantum Monte Carlo simulations, accompanied with field theoretical analysis. We find the spontaneous breaking of the $SU(2)$ symmetry for $\alpha \in (2,4)$ in ferromagnetic Heisenberg model with $1/r^{\alpha}$ interaction at $D=2$, and obtain the accurate critical exponents by finite-size analysis for $\alpha<3$ where the system is above the upper critical dimension with Gaussian fixed point and for $3\le\alpha<4$ where the system is below the upper critical dimension with non-Gaussian fixed point. Our results reveal the novel critical behaviors in 2D long-range Heisenberg models and will intrigue further experimental studies of quantum materials with long-range interaction beyond the realm of the Mermin-Wagner theorem

IFITM Genes, Variants, and Their Roles in the Control and Pathogenesis of Viral Infections

Interferon-induced transmembrane proteins (IFITMs) are a family of small proteins that localize in the plasma and endolysosomal membranes. IFITMs not only inhibit viral entry into host cells by interrupting the membrane fusion between viral envelope and cellular membranes, but also reduce the production of infectious virions or infectivity of progeny virions. Not surprisingly, some viruses can evade the restriction of IFITMs and even hijack the antiviral proteins to facilitate their infectious entry into host cells or promote the assembly of virions, presumably by modulating membrane fusion. Similar to many other host defense genes that evolve under the selective pressure of microorganism infection, IFITM genes evolved in an accelerated speed in vertebrates and many single-nucleotide polymorphisms (SNPs) have been identified in the human population, some of which have been associated with severity and prognosis of viral infection (e.g., influenza A virus). Here, we review the function and potential impact of genetic variation for IFITM restriction of viral infections. Continuing research efforts are required to decipher the molecular mechanism underlying the complicated interaction among IFITMs and viruses in an effort to determine their pathobiological roles in the context of viral infections in vivo

Quantum criticality and entanglement for 2d long-range Heisenberg bilayer

The study of quantum criticality and entanglement in systems with long-range (LR) interactions is still in its early stages, with many open questions remaining. In this work, we investigate critical exponents and scaling of entanglement entropies (EE) in the LR bilayer Heisenberg model using large-scale quantum Monte Carlo (QMC) simulations and the recently developed nonequilibrium increment algorithm for measuring EE. By applying modified (standard) finite-size scaling (FSS) above (below) the upper critical dimension and field theory analysis, we obtain precise critical exponents in three regimes: the LR Gaussian regime with a Gaussian fixed point, the short-range (SR) regime with Wilson-Fisher (WF) exponents, and a LR non-Gaussian regime where the critical exponents vary continuously from LR Gaussian to SR values. We compute the R\'enyi EE both along the critical line and in the N\'eel phase and observe that as the LR interaction is enhanced, the area-law contribution in EE gradually vanishes both at quantum critical points (QCPs) and in the N\'eel phase. The log-correction in EE arising from sharp corners at the QCPs also decays to zero as LR interaction grows, whereas the log-correction for N\'eel states, caused by the interplay of Goldstone modes and restoration of the symmetry in a finite system, is enhanced as LR interaction becomes stronger. We also discuss relevant experimental settings to detect these nontrivial properties in critical behavior and entanglement information for quantum many-body systems with LR interactions.Comment: 5pages, 4 figure

Impacts of sea-land and mountain-valley circulations on the air pollution in Beijing-Tianjin-Hebei (BTH): A case study

In the study, observational data analyses and the WRF-CHEM model simulations are used to investigate the role of sea-land and mountain-valley breeze circulations in a severe air pollution event occurred in Beijing-Tianjin-Hebei (BTH) during August 9-10, 2013. Both the wind observations and the model simulations have clearly indicated the evolution of the sea-land and mountain-valley breeze circulations during the event. The WRF-CHEM model generally reproduces the local meteorological circulations and also performs well in simulating temporal variations and spatial distributions of fine particulate matters (PM2.5) and ozone (O-3) concentrations compared to observations in BTH. The model results have shown that the offshore land breeze transports the pollutants formed in Shandong province to the Bohai Gulf in the morning, causing the formation of high O-3 and PM2.5 concentrations over the gulf. The onshore sea breeze not only causes the formation of a convergence zone to induce upward movement, mitigating the surface pollution to some degree, also recirculates the pollutants over the gulf to deteriorate the air quality in the coastal area. The upward valley breeze brings the pollutants in the urban area of Beijing to the mountain area in the afternoon, and the downward mountain breeze transports the pollutants back during nighttime. The intensity of the mountain-valley breeze circulation is weak compared to the land-sea breeze circulation in BTH. It is worth noting that the local circulations play an important role when the large-scale meteorological conditions are relatively weak. (C) 2017 Elsevier Ltd. All rights reserved

Total Phosphorus and Nitrogen Dynamics and Influencing Factors in Dongting Lake Using Landsat Data

Total phosphorus (TP) and total nitrogen (TN) reflect the state of eutrophication. However, traditional point-based water quality monitoring methods are time-consuming and labor-intensive, and insufficient to estimate and assess water quality at a large scale. In this paper, we constructed machine learning models for TP and TN inversion using measured data and satellite imagery band reflectance, and verified it by in situ data. Atmospheric correction was performed on the Landsat Top of Atmosphere (TOP) data by removing the effect of the adjacency effect and correcting differences between Landsat sensors. Then, using the established model, the TP and TN patterns in Dongting Lake with a spatial resolution of 30 m from 1996 to 2021 were derived for the first time. The annual and monthly spatio-temporal variation characteristics of TP and TN in Dongting Lake were investigated in details, and the influences of hydrometeorological elements on water quality variations were analyzed. The results show that the established empirical model can accurately estimate TP with coefficient (R2) ≥ 0.70, root mean square error (RMSE) ≤ 0.057 mg/L, mean relative error (MRE) ≤ 0.23 and TN with R2 ≥ 0.73, RMSE ≤ 0.48 mg/L and MRE ≤ 0.20. From 1996 to 2021, TP in Dongting Lake showed a downward trend and TN showed an upward trend, while the summer value was much higher than the other seasons. Furthermore, the influencing factors on TP and TN variations were investigated and discussed. Between 1996 and 2003, the main contributors to the change of water quality in Dongting Lake were external inputs such as water level and flow. The significant changes in water quantity and sediment characteristics following the operation of the Three Gorges Dam (TGD) in 2003 also had an impact on the water quality in Dongting Lake

Bipartite Graph Pre-training for Unsupervised Extractive Summarization with Graph Convolutional Auto-Encoders

Pre-trained sentence representations are crucial for identifying significant sentences in unsupervised document extractive summarization. However, the traditional two-step paradigm of pre-training and sentence-ranking, creates a gap due to differing optimization objectives. To address this issue, we argue that utilizing pre-trained embeddings derived from a process specifically designed to optimize cohensive and distinctive sentence representations helps rank significant sentences. To do so, we propose a novel graph pre-training auto-encoder to obtain sentence embeddings by explicitly modelling intra-sentential distinctive features and inter-sentential cohesive features through sentence-word bipartite graphs. These pre-trained sentence representations are then utilized in a graph-based ranking algorithm for unsupervised summarization. Our method produces predominant performance for unsupervised summarization frameworks by providing summary-worthy sentence representations. It surpasses heavy BERT- or RoBERTa-based sentence representations in downstream tasks.Comment: Accepted by the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023