Solar flare hard X-ray spikes observed by RHESSI: a statistical study

Context. Hard X-ray (HXR) spikes refer to fine time structures on timescales of seconds to milliseconds in high-energy HXR emission profiles during solar flare eruptions. Aims. We present a preliminary statistical investigation of temporal and spectral properties of HXR spikes. Methods. Using a three-sigma spike selection rule, we detected 184 spikes in 94 out of 322 flares with significant counts at given photon energies, which were detected from demodulated HXR light curves obtained by the Reuven Ramaty High Energy Solar Spectroscopic Imager (RHESSI). About one fifth of these spikes are also detected at photon energies higher than 100 keV. Results. The statistical properties of the spikes are as follows. (1) HXR spikes are produced in both impulsive flares and long-duration flares with nearly the same occurrence rates. Ninety percent of the spikes occur during the rise phase of the flares, and about 70% occur around the peak times of the flares. (2) The time durations of the spikes vary from 0.2 to 2 s, with the mean being 1.0 s, which is not dependent on photon energies. The spikes exhibit symmetric time profiles with no significant difference between rise and decay times. (3) Among the most energetic spikes, nearly all of them have harder count spectra than their underlying slow-varying components. There is also a weak indication that spikes exhibiting time lags in high-energy emissions tend to have harder spectra than spikes with time lags in low-energy emissions.Comment: 16 pages, 13 figure

Design and finite element mode analysis of noncircular gear

The noncircular gear transmission is an important branch of the gear transmission, it is characterized by its compact structure, good dynamic equilibration and other advantages, and can be used in the automobile, engineering machine, ship, machine tool, aviation and spaceflight field etc. Studying on the dynamics feature of noncircular gear transmission can improve the ability to carry loads of, reduce the vibration and noise of, increase the life of the noncircular gear transmission machine, provides guidance for the design of the noncircular gear, and has significant theories and practical meanings. In this paper, the gear transmission technique is used to studied the design method of the noncircular gear, which contains distribution of teeth on the pitch curve, designs of the tooth tip curve and the tooth root curve, design of the tooth profile curve, the gear system dynamics principle is introduced to establish dynamics model for the noncircular gear; basic theory of finite element and mode analysis method are applied, finite element model for the noncircular gear is established, natural vibration characteristic of the noncircular gear is studied. And the oval gear is taken as an example, the mathematics software MathCAD, the 3D modeling software UG and the finite element software ABAQUS are used to realize precise 3D model of the oval gear. The finite element method is used, the natural vibration characteristic of the oval gear is studied, the main vibration types and natural frequencies of the oval gear and that of the equivalent cylindrical gears are analyzed and compared, the conclusions received reflect the dynamics performance of the oval gear, and solid foundation is laid for dynamics research and engineering application of the oval gear transmission

Single transverse-spin asymmetry in Drell-Yan lepton angular distribution

We calculate a single transverse-spin asymmetry for the Drell-Yan lepton-pair's angular distribution in perturbative QCD. At leading order in the strong coupling constant, the asymmetry is expressed in terms of a twist-3 quark-gluon correlation function T_F^{(V)}(x_1,x_2). In our calculation, the same result was obtained in both light-cone and covariant gauge in QCD, while keeping explicit electromagnetic current conservation for the virtual photon that decays into the lepton pair. We also present a numerical estimate of the asymmetry and compare the result to an existing other prediction.Comment: 15 pages, Revtex, 5 Postscript figures, uses aps.sty, epsfig.st

Spin-current injection and detection in strongly correlated organic conductor

Spin-current injection into an organic semiconductor $\rm{\kappa\text{-}(BEDT\text{-}TTF)_2Cu[N(CN)_2]Br}$ film induced by the spin pumping from an yttrium iron garnet (YIG) film. When magnetization dynamics in the YIG film is excited by ferromagnetic or spin-wave resonance, a voltage signal was found to appear in the $\rm{\kappa\text{-}(BEDT\text{-}TTF)_2Cu[N(CN)_2]Br}$ film. Magnetic-field-angle dependence measurements indicate that the voltage signal is governed by the inverse spin Hall effect in $\rm{\kappa\text{-}(BEDT\text{-}TTF)_2Cu[N(CN)_2]Br}$. We found that the voltage signal in the $\rm{\kappa\text{-}(BEDT\text{-}TTF)_2Cu[N(CN)_2]Br}$/YIG system is critically suppressed around 80 K, around which magnetic and/or glass transitions occur, implying that the efficiency of the spin-current injection is suppressed by fluctuations which critically enhanced near the transitions

Modeling Long- and Short-Term Temporal Patterns with Deep Neural Networks

Multivariate time series forecasting is an important machine learning problem across many domains, including predictions of solar plant energy output, electricity consumption, and traffic jam situation. Temporal data arise in these real-world applications often involves a mixture of long-term and short-term patterns, for which traditional approaches such as Autoregressive models and Gaussian Process may fail. In this paper, we proposed a novel deep learning framework, namely Long- and Short-term Time-series network (LSTNet), to address this open challenge. LSTNet uses the Convolution Neural Network (CNN) and the Recurrent Neural Network (RNN) to extract short-term local dependency patterns among variables and to discover long-term patterns for time series trends. Furthermore, we leverage traditional autoregressive model to tackle the scale insensitive problem of the neural network model. In our evaluation on real-world data with complex mixtures of repetitive patterns, LSTNet achieved significant performance improvements over that of several state-of-the-art baseline methods. All the data and experiment codes are available online.Comment: Accepted by SIGIR 201

The S=1/2 chain in a staggered field: High-energy bound-spinon state and the effects of a discrete lattice

We report an experimental and theoretical study of the antiferromagnetic S=1/2 chain subject to uniform and staggered fields. Using inelastic neutron scattering, we observe a novel bound-spinon state at high energies in the linear chain compound CuCl2 * 2((CD3)2SO). The excitation is explained with a mean-field theory of interacting S=1/2 fermions and arises from the opening of a gap at the Fermi surface due to confining spinon interactions. The mean-field model also describes the wave-vector dependence of the bound-spinon states, particularly in regions where effects of the discrete lattice are important. We calculate the dynamic structure factor using exact diagonalization of finite length chains, obtaining excellent agreement with the experiments.Comment: 16 pages, 7 figures, accepted by Phys. Rev.

Deep learning for cardiac image segmentation: A review

Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research