Coherent manipulation of spin wave vector for polarization of photons in an atomic ensemble

We experimentally demonstrate the manipulation of two-orthogonal components of a spin wave in an atomic ensemble. Based on Raman two-photon transition and Larmor spin precession induced by magnetic field pulses, the coherent rotations between the two components of the spin wave is controllably achieved. Successively, the two manipulated spin-wave components are mapped into two orthogonal polarized optical emissions, respectively. By measuring Ramsey fringes of the retrieved optical signals, the \pi/2-pulse fidelity of ~96% is obtained. The presented manipulation scheme can be used to build an arbitrary rotation for qubit operations in quantum information processing based on atomic ensembles

Scenario Generation for Cooling, Heating, and Power Loads Using Generative Moment Matching Networks

Scenario generations of cooling, heating, and power loads are of great significance for the economic operation and stability analysis of integrated energy systems. In this paper, a novel deep generative network is proposed to model cooling, heating, and power load curves based on a generative moment matching networks (GMMN) where an auto-encoder transforms high-dimensional load curves into low-dimensional latent variables and the maximum mean discrepancy represents the similarity metrics between the generated samples and the real samples. After training the model, the new scenarios are generated by feeding Gaussian noises to the scenario generator of the GMMN. Unlike the explicit density models, the proposed GMMN does not need to artificially assume the probability distribution of the load curves, which leads to stronger universality. The simulation results show that the GMMN not only fits the probability distribution of multi-class load curves well, but also accurately captures the shape (e.g., large peaks, fast ramps, and fluctuation), frequency-domain characteristics, and temporal-spatial correlations of cooling, heating, and power loads. Furthermore, the energy consumption of generated samples closely resembles that of real samples.Comment: This paper has been accepted by CSEE Journal of Power and Energy System

A Review of Graph Neural Networks and Their Applications in Power Systems

Deep neural networks have revolutionized many machine learning tasks in power systems, ranging from pattern recognition to signal processing. The data in these tasks is typically represented in Euclidean domains. Nevertheless, there is an increasing number of applications in power systems, where data are collected from non-Euclidean domains and represented as graph-structured data with high dimensional features and interdependency among nodes. The complexity of graph-structured data has brought significant challenges to the existing deep neural networks defined in Euclidean domains. Recently, many publications generalizing deep neural networks for graph-structured data in power systems have emerged. In this paper, a comprehensive overview of graph neural networks (GNNs) in power systems is proposed. Specifically, several classical paradigms of GNNs structures (e.g., graph convolutional networks) are summarized, and key applications in power systems, such as fault scenario application, time series prediction, power flow calculation, and data generation are reviewed in detail. Furthermore, main issues and some research trends about the applications of GNNs in power systems are discussed

Quantum Interference of Stored Coherent Spin-wave Excitations in a Two-channel Memory

Quantum memories are essential elements in long-distance quantum networks and quantum computation. Significant advances have been achieved in demonstrating relative long-lived single-channel memory at single-photon level in cold atomic media. However, the qubit memory corresponding to store two-channel spin-wave excitations (SWEs) still faces challenges, including the limitations resulting from Larmor procession, fluctuating ambient magnetic field, and manipulation/measurement of the relative phase between the two channels. Here, we demonstrate a two-channel memory scheme in an ideal tripod atomic system, in which the total readout signal exhibits either constructive or destructive interference when the two-channel SWEs are retrieved by two reading beams with a controllable relative phase. Experimental result indicates quantum coherence between the stored SWEs. Based on such phase-sensitive storage/retrieval scheme, measurements of the relative phase between the two SWEs and Rabi oscillation, as well as elimination of the collapse and revival of the readout signal, are experimentally demonstrated

Epidemiological and virological characteristics of pandemic influenza A (H1N1) 2009 in school outbreaks in China

Background: During the 2009 pandemic influenza H1N1 (2009) virus (pH1N1) outbreak, school students were at an increased risk of infection by the pH1N1 virus. However, the estimation of the attack rate showed significant variability. Methods: Two school outbreaks were investigated in this study. A questionnaire was designed to collect information by interview. Throat samples were collected from all the subjects in this study 6 times and sero samples 3 times to confirm the infection and to determine viral shedding. Data analysis was performed using the software STATA 9.0. Findings: The attack rate of the pH1N1 outbreak was 58.3% for the primary school, and 52.9% for the middle school. The asymptomatic infection rates of the two schools were 35.8% and 37.6% respectively. Peak virus shedding occurred on the day of ARI symptoms onset, followed by a steady decrease over subsequent days (p = 0.026). No difference was found either in viral shedding or HI titer between the symptomatic and the asymptomatic infectious groups. Conclusions: School children were found to be at a high risk of infection by the novel virus. This may be because of a heightened risk of transmission owing to increased mixing at boarding school, or a lack of immunity owing to socioeconomic status. We conclude that asymptomatically infectious cases may play an important role in transmission of the pH1N1 virus

The Emerging of Hydrovoltaic Materials as a Future Technology: A Case Study for China

Water contains tremendous energy in various forms, but very little of this energy has yet been harvested. Nanostructured materials can generate electricity by water-nanomaterial interaction, a phenomenon referred to as hydrovoltaic effect, which potentially extends the technical capability of water energy harvesting. In this chapter, starting by describing the fundamental principle of hydrovoltaic effect, including water-carbon interactions and fundamental mechanisms of harvesting water energy with nanostructured materials, experimental advances in generating electricity from water flows, waves, natural evaporation, and moisture are then reviewed. We further discuss potential applications of hydrovoltaic technologies, analyze main challenges in improving the energy conversion efficiency and scaling up the output power, and suggest prospects for developments of the emerging technology, especially in China