Optical properties of ZnO fi lms with nanorod structures

In this paper, ZnO seed layer was prepared on glass substrate by sol-gel method, and ZnO nanorods were grown on the seed layer by hydrothermal method. ZnO fi lms with nanorod structures were obtained. B y changing the concentration of hydrothermal growth, diff erent ZnO fi lms with nanorod structures were obtained, and the structure, morphology, transmittance and light trapping properties of the fi lms were characterized. The optical properties of ZnO fi lms with nanorod structures under diff erent growth conditions were studied in order to improve the light trapping properties of ZnO fi lms while ensuring high transmittance of the fi lms

Dependence of the critical temperature and disorder in holographic superconductors on superfluid density

Recent experiments strongly indicate deep connections between transports of strange metal and high $T_c$ superconductors. For example, the dependence of the zero-temperature phase stiffness on the critical superconducting temperature is generally linear, which is incompatible with the standard Bardeen-Cooper-Schrieffer description. We develop an analytical method for AC conductivity calculation and explore the scaling relations among superconducting critical temperature, superfluid density, and momentum dissipation strength for the Gubser-Rocha model with extensions in the probe limit. In the normal phase, we show that the critical temperature is proportional to the momentum dissipation strength in a certain parameter range, which is universal in holographic models. In the superconducting phase, studying the AC conductivity analytically and numerically, we find linear dependence of zero-temperature superfluid density (phase stiffness) on the critical superconducting temperature, which is consistent with recent experiments of high $T_c$ superconductors. These results further underpin the deep connections between strange metal and high $T_c$ superconductors.Comment: v1: 22 pages, 9 figures, v2: results and discussion improved, references added, 29 pages, 12 figures, v3: discussion improved, 30 pages, 12 figure

Discrete-modulation continuous-variable quantum key distribution with high key rate

Discrete-modulation continuous-variable quantum key distribution has the potential for large-scale deployment in the secure quantum communication networks due to low implementation complexity and compatibility with the current telecom systems. The security proof for four coherent states phase-shift keying (4-PSK) protocol has recently been established by applying numerical methods. However, the achievable key rate is relatively low compared with the optimal Gaussian modulation scheme. To enhance the key rate of discrete-modulation protocol, we first show that 8-PSK increases the key rate by about 60\% in comparison to 4-PSK, whereas the key rate has no significant improvement from 8-PSK to 12-PSK. We then expand the 12-PSK to two-ring constellation structure with four states in the inner ring and eight states in the outer ring, which significantly improves the key rate to be 2.4 times of that of 4-PSK. The key rate of the two-ring constellation structure can reach 70\% of the key rate achieved by Gaussian modulation in long distance transmissions, making this protocol an attractive alternative for high-rate and low-cost application in secure quantum communication networks.Comment: Welcome comment

Efficient Transferability Assessment for Selection of Pre-trained Detectors

Large-scale pre-training followed by downstream fine-tuning is an effective solution for transferring deep-learning-based models. Since finetuning all possible pre-trained models is computational costly, we aim to predict the transferability performance of these pre-trained models in a computational efficient manner. Different from previous work that seek out suitable models for downstream classification and segmentation tasks, this paper studies the efficient transferability assessment of pre-trained object detectors. To this end, we build up a detector transferability benchmark which contains a large and diverse zoo of pre-trained detectors with various architectures, source datasets and training schemes. Given this zoo, we adopt 7 target datasets from 5 diverse domains as the downstream target tasks for evaluation. Further, we propose to assess classification and regression sub-tasks simultaneously in a unified framework. Additionally, we design a complementary metric for evaluating tasks with varying objects. Experimental results demonstrate that our method outperforms other state-of-the-art approaches in assessing transferability under different target domains while efficiently reducing wall-clock time 32$\times$ and requires a mere 5.2\% memory footprint compared to brute-force fine-tuning of all pre-trained detectors.Comment: WACV 202