An echo state network architecture based on quantum logic gate and its optimization

Quantum neural network (QNN) is developed based on two classical theories of quantum computation and artificial neural networks. It has been proved that quantum computing is an important candidate for improving the performance of traditional neural networks. In this work, inspired by the QNN, the quantum computation method is combined with the echo state networks (ESNs), and a hybrid model namely quantum echo state network (QESN) is proposed. Firstly, the input training data is converted to quantum state, and the internal neurons in the dynamic reservoir of ESN are replaced by qubit neurons. Then in order to maintain the stability of QESN, the particle swarm optimization (PSO) is applied to the model for the parameter optimizations. The synthetic time series and real financial application datasets (Standard & Poor's 500 index and foreign exchange) are used for performance evaluations, where the ESN, autoregressive integrated moving average (ARIMAX) are used as the benchmarks. Results show that the proposed PSO-QESN model achieves a good performance for the time series predication tasks and is better than the benchmarking algorithms. Thus, it is feasible to apply quantum computing to the ESN model, which provides a novel method to improve the ESN performance

Influence of the Long-Range Transport of Siberian Biomass Burnings on Air Quality in Northeast China in June 2017

Biomass burning (BB) emits a large volume of trace gases and aerosols into the atmosphere, which can significantly affect the earth’s radiative balance and climate and has negative impacts on air quality and even human health. In late June 2017, an intense BB case, dominated by forest and savanna fires, occurred in Siberia, and it affected the air quality of Northeast China through long-range transport. Here, multisatellite remote-sensing products and ground-based PM2.5 measurements are used to evaluate the influence of the Siberian smoky plume on Northeast China. The results show that the BB was intense at the early stage when the daily fire count and average fire radiative power exceeded 300 and 200 MW, respectively. The maximum daily fire count reached 1350 in Siberia, and the peak value of instantaneous fire radiative power was as high as 3091.5 MW. High concentrations of CO and aerosols were emitted into the atmosphere by the BB in Siberia. The maximum daily mean values of the CO column concentration and aerosol optical depth (AOD) increased by 3 × 1017 molec·cm2 and 0.5 compared with that during the initial BB stage. In addition, the BB released a large number of absorptive aerosols into the atmosphere, and the UV aerosol index (UVAI) increased by five times at the peak of the event in Siberia. Under the appropriate synoptic conditions and, combined with pyroconvection, the smoky plume was lifted into the upper air and transported to Northeast China, affecting the air quality of Northeast China. The daily mean values of CO concentration, AOD, and UVAI in Northeast China increased by 6 × 1017 molec·cm2, 0.5, and 1.4, respectively, after being affected. Moreover, the concentration of the surface PM2.5 in Northeast China approximately doubled after being affected by the plume. The results of this study indicate that the air quality of Northeast China can be significantly affected by Siberian BBs under favorable conditions

Efficient Pipelining Parallel Methods for Image Compositing in Sort-Last Rendering

Abstract. It is well known that image compositing is the bottleneck in Sort-Last rendering. Many methods have been developed to reduce the compositing time. In this paper, we present a series of pipeline methods for image compositing. Our new pipeline methods based on Direct Send and Binary Swap. However, unlike these methods, our methods overlap the rendering time of different frames to achieve high f ps(frames per second) in final display. We analyze the theoretical performance of our methods and take intensive experiments using real data. The results show that our new methods are able to achieve interactive frame rates and scale well with both the size of nodes and screen resolution

Climatological Aspects of Active Fires in Northeastern China and Their Relationship to Land Cover

Biomass burning (BB) is a driving force for heavy haze in northeastern China (NEC) and shows distinct seasonal features. However, little is known about its climatological aspects, which are important for regional BB management and understanding BB effects on climate and environment. Here, the climatological characteristics of active fires and their dependence on land cover in NEC were studied using Moderate Resolution Imaging Spectroradiometer (MODIS) products. Moreover, the influence of meteorological factors on fire activities was explored. The number of fires was found to have increased significantly from 2003 to 2018; and the annual total FRP (FRPtot) showed a generally consistent variation with fire counts. However, the mean fire radiative power for each spot (FRPmean) decreased. Fire activity showed distinctive seasonal variations. Most fires and intense burning events occurred in spring and autumn. Spatially, fires were mainly concentrated in cropland areas in plains, where the frequency of fires increased significantly, especially in spring and autumn. The annual percentage of agricultural fires increased from 34% in 2003 to over 60% after 2008 and the FRPtot of croplands increased from 12% to over 55%. Fires in forests, savannas, and grasslands tended to be associated with higher FRPmean than those in croplands. Analysis indicated that the increasing fire count in NEC is mainly caused by agricultural fires. Although the decreasing FRPmean represents an effective management of BB in recent years, high fire counts and FRPtot in croplands indicate that the crop residue burning cannot be simply banned and a need instead for effective applications. More efforts should be made on clean utilization of straw. The accumulation of dry biomass, high temperature, and low humidity, and weak precipitation are conducive to the fire activities. This study provides a comprehensive analysis of BB in NEC and provides a reference for regional BB management and control

Enabling Electrochemical–Mechanical Robustness of Ultra‐High Ni Cathode via Self‐Supported Primary‐Grain‐Alignment Strategy

Abstract The electrochemical–mechanical degradation of ultrahigh Ni cathode for lithium‐ion batteries is a crucial aspect that limits the cycle life and safety of devices. Herein, the study reports a facile strategy involving rational design of primary grain crystallographic orientation within polycrystalline cathode, which well enhanced its electro‐mechanical strength and Li+ transfer kinetics. Ex situ and in situ experiments/simulations including cross‐sectional particle electron backscatter diffraction (EBSD), single‐particle micro‐compression, thermogravimetric analysis combined with mass spectrometry (TGA‐MS), and finite element modeling reveal that, the primary‐grain‐alignment strategy effectively mitigates the particle pulverization, lattice oxygen release thereby enhances battery cycle life and safety. Besides the preexisting doping and coating methodologies to improve the stability of Ni‐rich cathode, the primary‐grain‐alignment strategy, with no foreign elements or heterophase layers, is unprecedently proposed here. The results shed new light on the study of electrochemical–mechanical strain alleviation for electrode materials