The linear and nonlinear Jaynes-Cummings model for the multiphoton transition

With the Jaynes-Cummings model, we have studied the atom and light field quantum entanglement of multiphoton transition, and researched the effect of initial state superposition coefficient $C_{1}$, the transition photon number $N$, the quantum discord $\delta$ and the nonlinear coefficient $\chi$ on the quantum entanglement degrees. We have given the quantum entanglement degrees curves with time evolution, and obtained some results, which should have been used in quantum computing and quantum information.Comment: arXiv admin note: text overlap with arXiv:1404.0821, arXiv:1205.0979 by other author

Dynamic Cytoophidia during Late-Stage Drosophila Oogenesis

CTP synthase (CTPS) catalyzes the final step of de novo synthesis of CTP. CTPS was first discovered to form filamentous structures termed cytoophidia in Drosophila ovarian cells. Subsequent studies have shown that cytoophidia are widely present in cells of three life domains. In the Drosophila ovary model, our previous studies mainly focused on the early and middle stages, with less involvement in the later stages. In this work, we focus on the later stages of female germline cells in Drosophila. We use live-cell imaging to capture the continuous dynamics of cytoophidia in Stages 10–12. We notice the heterogeneity of cytoophidia in the two types of germline cells (nurse cells and oocytes), manifested in significant differences in morphology, distribution, and dynamics. Surprisingly, we also find that neighboring nurse cells in the same egg chamber exhibit multiple dynamic patterns of cytoophidia over time. Although the described dynamics may be influenced by the in vitro incubation conditions, our observation provides an initial understanding of the dynamics of cytoophidia during late-stage Drosophila oogenesis

Improving Anomaly Segmentation with Multi-Granularity Cross-Domain Alignment

Anomaly segmentation plays a crucial role in identifying anomalous objects within images, which facilitates the detection of road anomalies for autonomous driving. Although existing methods have shown impressive results in anomaly segmentation using synthetic training data, the domain discrepancies between synthetic training data and real test data are often neglected. To address this issue, the Multi-Granularity Cross-Domain Alignment (MGCDA) framework is proposed for anomaly segmentation in complex driving environments. It uniquely combines a new Multi-source Domain Adversarial Training (MDAT) module and a novel Cross-domain Anomaly-aware Contrastive Learning (CACL) method to boost the generality of the model, seamlessly integrating multi-domain data at both scene and sample levels. Multi-source domain adversarial loss and a dynamic label smoothing strategy are integrated into the MDAT module to facilitate the acquisition of domain-invariant features at the scene level, through adversarial training across multiple stages. CACL aligns sample-level representations with contrastive loss on cross-domain data, which utilizes an anomaly-aware sampling strategy to efficiently sample hard samples and anchors. The proposed framework has decent properties of parameter-free during the inference stage and is compatible with other anomaly segmentation networks. Experimental conducted on Fishyscapes and RoadAnomaly datasets demonstrate that the proposed framework achieves state-of-the-art performance.Comment: Accepted to ACM Multimedia 202