Enhanced coherent light-matter interaction and room-temperature quantum yield of plasmonic resonances engineered by a chiral exceptional point

Strong dissipation of plasmonic resonances is detrimental to quantum manipulation. To enhance the quantum coherence, we propose to tailor the local density of states (LDOS) of plasmonic resonances by integrating with a photonic cavity operating at a chiral exceptional point (CEP), where the phase of light field can offer a new degree of freedom to flexibly manipulate the quantum states. A quantized few-mode theory is employed to reveal that the LDOS of the proposed hybrid cavity can evolve into sub-Lorentzian lineshape, with order-of-magnitude linewidth narrowing and additionally a maximum of eightfold enhancement compared to the usual plasmonic-photonic cavity without CEP. This results in the enhanced coherent light-matter interaction accompanied by the reduced dissipation of polaritonic states. Furthermore, a scattering theory based on eigenmode decomposition is present to elucidate two mechanisms responsible for the significant improvement of quantum yield at CEP, the reduction of plasmonic absorption by the Fano interference and the enhancement of cavity radiation through the superscattering. Importantly, we find the latter allows achieving a near-unity quantum yield at room temperature; in return, high quantum yield is beneficial to experimentally verify the enhanced LDOS at CEP by measuring the fluorescence lifetime of a quantum emitter. Therefore, our work demonstrates that the plasmonic resonances in CEP-engineered environment can serve as a promising platform for exploring the quantum states control by virtue of the non-Hermiticity of open optical resonators and building the high-performance quantum devices for sensing, spectroscopy, quantum information processing and quantum computing.Comment: 20 pages,9 figure

21 cm foreground removal using AI and frequency-difference technique

The deep learning technique has been employed in removing foreground contaminants from 21 cm intensity mapping, but its effectiveness is limited by the large dynamic range of the foreground amplitude. In this study, we develop a novel foreground removal technique grounded in U-Net networks. The essence of this technique lies in introducing an innovative data preprocessing step specifically, utilizing the temperature difference between neighboring frequency bands as input, which can substantially reduce the dynamic range of foreground amplitudes by approximately two orders of magnitude. This reduction proves to be highly advantageous for the U-Net foreground removal. We observe that the HI signal can be reliably recovered, as indicated by the cross-correlation power spectra showing unity agreement at the scale of $k < 0.3 h^{-1}$Mpc in the absence of instrumental effects. Moreover, accounting for the systematic beam effects, our reconstruction displays consistent auto-correlation and cross-correlation power spectrum ratios at the $1\sigma$ level across scales $k \lesssim 0.1 h^{-1}$Mpc, with only a 10% reduction observed in the cross-correlation power spectrum at $k\simeq0.2 h^{-1}$Mpc. The effects of redshift-space distortion are also reconstructed successfully, as evidenced by the quadrupole power spectra matching. In comparison, our method outperforms the traditional Principal Component Analysis method, which derived cross-correlation ratios are underestimated by around 75%. We simulated various white noise levels in the map and found that the mean cross-correlation ratio $\bar{R}_\mathrm{cross} \gtrsim 0.75$ when the level of the thermal noise is smaller than or equal to that of the HI signal. We conclude that the proposed frequency-difference technique can significantly enhance network performance by reducing the amplitude range of foregrounds and aiding in the prevention of HI loss.Comment: 18 pages, 16 figure

Future Gradient Descent for Adapting the Temporal Shifting Data Distribution in Online Recommendation Systems

One of the key challenges of learning an online recommendation model is the temporal domain shift, which causes the mismatch between the training and testing data distribution and hence domain generalization error. To overcome, we propose to learn a meta future gradient generator that forecasts the gradient information of the future data distribution for training so that the recommendation model can be trained as if we were able to look ahead at the future of its deployment. Compared with Batch Update, a widely used paradigm, our theory suggests that the proposed algorithm achieves smaller temporal domain generalization error measured by a gradient variation term in a local regret. We demonstrate the empirical advantage by comparing with various representative baselines

Multi-configurational nature of electron correlation within nitrogen vacancy centers in diamond

Diamond is a solid-state platform to develop quantum technologies, but it has been a long-standing problem that the current understanding of quantum states in diamond is mostly limited to single-electron pictures. Here, we combine the full configuration interaction quantum Monte Carlo method and the density-matrix functional embedding theory, to achieve unprecedented accuracy in describing the many-body quantum states of nitrogen vacancy (NV) centers in diamond. More than 30 electrons and 130 molecular orbitals are correlated, which reveals the multi-configurational wavefunction of the many-body quantum states in diamond. The multi-configurational description explains puzzling experimental measurements in intersystem crossing and charge state transition in NV centers in diamond. The calculations not only reproduce the available experimental measurements of the energy gaps between quantum states but also provide new benchmarks for states that are still subject to considerable uncertainty. This study highlights the importance of multi-configurational wavefunction in the many-body quantum states in solids

Association between investigator-measured body-mass index and colorectal adenoma: a systematic review and meta-analysis of 168,201 subjects

The objective of this meta-analysis is to evaluate the odds of colorectal adenoma (CRA) in colorectal cancer screening participants with different body mass index (BMI) levels, and examine if this association was different according to gender and ethnicity. The EMBASE and MEDLINE were searched to enroll high quality observational studies that examined the association between investigator-measured BMI and colonoscopy-diagnosed CRA. Data were independently extracted by two reviewers. A random-effects meta-analysis was conducted to estimate the summary odds ratio (SOR) for the association between BMI and CRA. The Cochran’s Q statistic and I2 analyses were used to assess the heterogeneity. A total of 17 studies (168,201 subjects) were included. When compared with subjects having BMI &lt; 25, individuals with BMI 25–30 had significantly higher risk of CRA (SOR 1.44, 95% CI 1.30–1.61; I2 = 43.0%). Subjects with BMI ≥ 30 had similarly higher risk of CRA (SOR 1.42, 95% CI 1.24–1.63; I2 = 18.5%). The heterogeneity was mild to moderate among studies. The associations were significantly higher than estimates by previous meta-analyses. There was no publication bias detected (Egger’s regression test, p = 0.584). Subgroup analysis showed that the magnitude of association was significantly higher in female than male subjects (SOR 1.43, 95% CI 1.30–1.58 vs. SOR 1.16, 95% CI 1.07–1.24; different among different ethnic groups (SOR 1.72, 1.44 and 0.88 in White, Asians and Africans, respectively) being insignificant in Africans; and no difference exists among different study designs. In summary, the risk conferred by BMI for CRA was significantly higher than that reported previously. These findings bear implications in CRA risk estimation

Discovery of Diverse Rodent and Bat Pestiviruses With Distinct Genomic and Phylogenetic Characteristics in Several Chinese Provinces

Bats and rodents are widely distributed worldwide and can be native or intermediate reservoirs of many important zoonotic viruses. Pestiviruses are a group of virus species of the genus Pestivirus under the family Flaviviridae that can infect a wide variety of artiodactylous hosts, including swine and ruminants. Two classic types of pestiviruses, bovine viral diarrhea virus and classical swine fever virus, are important causative agents of mild-to-severe disease in bovine and swine hosts, respectively, and cause tremendous economic losses in these industries. Recent reports revealed that bats and rodents could also act as natural hosts of pestiviruses and an atypical porcine pestivirus, which cause disease in piglets, showed a close genetic relationship with a specific bat pestivirus, RaPestV-1. This study aimed to describe the detection and characterization of novel pestiviruses from bats and rodents in different locations by analyzing the available bat and rodent virome data from throughout China. Two bat pestivirus species and four rodent pestivirus species that are distinct from other known viruses were identified and sequenced. These viruses were identified from two bat species and four rodent species in different Chinese provinces. There were two distinct lineages present in these viruses, that differ from artiodactylous pestivirus. These findings expand our understanding of the genetic diversity of pestiviruses in bats and rodents and suggest the presence of a diverse set of pestiviruses in non-artiodactylous hosts. This study may provide new insight for the prevention of future viral disease outbreaks originating from bats and rodents

ST Segment Extraction from Exercise ECG Signal Based on EMD and Wavelet Transform

Myocardial ischemia is always characterized by the changes in ST complex. But ischemia is not obvious at rest. Only in the state of exercise, abnormal ST will appear. The signal of ST is susceptible to noise interference which causes the inaccuracy of the ST segment detection. Combining the advantages of empirical mode decomposition (EMD), the paper proposes a modified threshold method to filter a serious of noise from exercise ECG. Extracted from the ECG feature, it includes ST segment detection, with wavelet transform. In the end, the method is tested with synthetic exercise data and real exercise ECG data. The results of ST segment detection are accurate and this method can be applied in practical exercise