Searcharxiv⌕ Search

arXiv subjects

Dong Li

Publications and source records attributed to Dong Li.

At least 469 records · Page 26Linked to original sources
arXiv2020

An investigation of flare emissions at multiple wavelengths

We report multi-wavelength observations of four solar flares on 2014 July 07. We firstly select these flares according to the soft X-ray (SXR) and extreme ultraviolet (EUV) emissions recorded by the Extreme Ultraviolet Variability Experiment and Geostationary Orbiting Environmental Satellites. Then their locations and geometries are identified from the full-disk images measured by the Atmospheric Imaging Assembly (AIA), and the time delays among the light curves in different channels are identified. The electron number densities are estimated using the Differential Emission Measure method. We find that three of four flares show strong emissions in SXR channels and high temperature (>6 MK) EUV wavelengths during the impulsive phase, i.e., AIA 131 A and 94 A, and then they emit peak radiation subsequently in the middle temperature (~0.6-3 MK) EUV channels. Moreover, they last for a long time and have smaller electron densities, which are probably driven by the interaction of hot diffuse flare loops. Only one flare emits radiation at almost the same time in all the observed wavelengths, lasts for a relatively short time, and has a larger electron density. It is also accompanied by a type III radio burst. The bright emission at the EUV channel could be corresponding to the associated erupting filament.

astro-ph.SR↗
arXiv2020

Evaluating the effect of city lock-down on controlling COVID-19 propagation through deep learning and network science models

The special epistemic characteristics of the COVID-19, such as the long incubation period and the infection through asymptomatic cases, put severe challenge to the containment of its outbreak. By the end of March 2020, China has successfully controlled the within-spreading of COVID-19 at a high cost of locking down most of its major cities, including the epicenter, Wuhan. Since the low accuracy of outbreak data before the mid of Feb. 2020 forms a major technical concern on those studies based on statistic inference from the early outbreak. We apply the supervised learning techniques to identify and train NP-Net-SIR model which turns out robust under poor data quality condition. By the trained model parameters, we analyze the connection between population flow and the cross-regional infection connection strength, based on which a set of counterfactual analysis is carried out to study the necessity of lock-down and substitutability between lock-down and the other containment measures. Our findings support the existence of non-lock-down-typed measures that can reach the same containment consequence as the lock-down, and provide useful guideline for the design of a more flexible containment strategy.

q-bio.PE↗
arXiv2020

Single production of vector-like bottom quark at the LHeC

Existences of vector-like quarks (VLQs) are predicted in many new physics scenarios beyond the Standard Model (SM). We study the possibility of detecting the vector-like bottom quark (VLQ-$B$) being the $SU(2)$ singlet with electric charge $-1/3$ at the Large Hadron Electron Collider (LHeC) in a model-independent framework. The decay properties and single production of VLQ-$B$ at the LHeC are explored. Three types of signatures are investigated. By carrying out a fast simulation for the signals and the corresponding backgrounds, the signal significances are obtained. Our numerical results show that detecting of VLQ-$B$ via the semileptonic channel is better than via the fully hadronic or leptonic channel.

hep-ph↗
arXiv2020

Smart-PGSim: Using Neural Network to Accelerate AC-OPF Power Grid Simulation

The optimal power flow (OPF) problem is one of the most important optimization problems for the operation of the power grid. It calculates the optimum scheduling of the committed generation units. In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution. The high quality of the initial solution and guidance of other outputs generated by the neural network enables faster convergence to the solution without losing optimality of final solution as computed by traditional methods. Smart-PGSim generates a novel multitask-learning neural network model to accelerate the AC-OPF simulation. Smart-PGSim also imposes the physical constraints of the simulation on the neural network automatically. Smart-PGSim brings an average of 49.2% performance improvement (up to 91%), computed over 10,000 problem simulations, with respect to the original AC-OPF implementation, without losing the optimality of the final solution.

eess.SP↗
arXiv2020

Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation

The Eulerian fluid simulation is an important HPC application. The neural network has been applied to accelerate it. The current methods that accelerate the fluid simulation with neural networks lack flexibility and generalization. In this paper, we tackle the above limitation and aim to enhance the applicability of neural networks in the Eulerian fluid simulation. We introduce Smartfluidnet, a framework that automates model generation and application. Given an existing neural network as input, Smartfluidnet generates multiple neural networks before the simulation to meet the execution time and simulation quality requirement. During the simulation, Smartfluidnet dynamically switches the neural networks to make the best efforts to reach the user requirement on simulation quality. Evaluating with 20,480 input problems, we show that Smartfluidnet achieves 1.46x and 590x speedup comparing with a state-of-the-art neural network model and the original fluid simulation respectively on an NVIDIA Titan X Pascal GPU, while providing better simulation quality than the state-of-the-art model.

cs.LG↗
arXiv2020

Stability analysis for the Implicit-Explicit discretization of the Cahn-Hilliard equation

Implicit-Explicit methods have been widely used for the efficient numerical simulation of phase field problems such as the Cahn-Hilliard equation or thin film type equations. Due to the lack of maximum principle and stiffness caused by the effect of small dissipation coefficient, most existing theoretical analysis relies on adding additional stabilization terms, mollifying the nonlinearity or introducing auxiliary variables which implicitly either changes the structure of the problem or trades accuracy for stability in a subtle way. In this work we introduce a robust theoretical framework to analyze directly the stability of the standard implicit-explicit approach without stabilization or any other modification. We take the Cahn-Hilliard equation as a model case and prove energy stability under natural time step constraints which are optimal with respect to energy scaling. These settle several questions which have been open since the work of Chen and Shen \cite{CS98}.

math.AP↗
arXiv2020

Testing error distribution by kernelized Stein discrepancy in multivariate time series models

Knowing the error distribution is important in many multivariate time series applications. To alleviate the risk of error distribution mis-specification, testing methodologies are needed to detect whether the chosen error distribution is correct. However, the majority of the existing tests only deal with the multivariate normal distribution for some special multivariate time series models, and they thus can not be used to testing for the often observed heavy-tailed and skewed error distributions in applications. In this paper, we construct a new consistent test for general multivariate time series models, based on the kernelized Stein discrepancy. To account for the estimation uncertainty and unobserved initial values, a bootstrap method is provided to calculate the critical values. Our new test is easy-to-implement for a large scope of multivariate error distributions, and its importance is illustrated by simulated and real data.

econ.EM↗
arXiv2020

Preflare very long-periodic pulsations observed in Halpha emission before the onset of a solar flare

Very long-periodic pulsations during preflare phases (preflare-VLPs) have been detected in the full-disk solar soft X-Ray (SXR) flux (see Tan et al. 2016). They may be regarded as precursors to solar flares and may help us better understand the trigger mechanism of solar flares. In this letter, we report a preflare-VLP event before the onset of an M1.1 circular-ribbon flare on 2015 October 16. It was simultaneously observed in Halpha, SXR, and extreme ultraviolet (EUV) wavelengths, which were recorded by the NVST, GOES, EVE, and AIA respectively. The preflare-VLP is identified as the repeat and quasi-periodic pulses in light curves during preflare phase, which might be modulated by LRC-circuit oscillation in the current-carrying plasma loop. The quasi-periodicity can be determined from the Fourier power spectrum with Markov chain Monte Carlo (MCMC)-based Bayesian (e.g., Liang et al. 2020), such as ~9.3 minutes. We present the first report of a preflare-VLP event in the local Halpha line and EUV wavelength, which could be considered a precursor of a solar flare. This finding should therefore prove useful for the prediction of solar flares, especially for powerful flares.

astro-ph.SR↗
arXiv2020

An ultra-long and quite thin coronal loop without significant expansion

Context. Coronal loops are the basic building blocks of the solar corona, which are related to the mass supply and heating of solar plasmas in the corona. However, their fundamental magnetic structures are still not well understood. Most coronal loops do not expand significantly, whereas the diverging magnetic field would have an expansion factor of about 5-10 over one pressure scale height. Aims. In this study, we investigate a unique coronal loop with a roughly constant cross section, it is ultra long and quite thin. A coronal loop model with magnetic helicity is presented to explain the small expansion of the loop width. Methods. This coronal loop was predominantly detectable in the 171 A channel of the Atmospheric Imaging Assembly (AIA). Then, the local magnetic field line was extrapolated by a Potential-Field-Source-Surface model. Finally, the differential emission measure analysis made from six AIA bandpasses was applied to obtain the thermal properties of this loop. Results. This coronal loop has a projected length of roughly 130 Mm, a width of about 1.5 +(-) 0.5 Mm and a lifetime of around 90 minutes. It follows an open magnetic field line. The cross section expanded very little (i.e., 1.5-2.0) along the loop length during its whole lifetime. This loop has a nearly constant temperature at about 0.7 +(-) 0.2 MK, whereas its density exhibits the typical structure of a stratified atmosphere. Conclusions. We use a thin twisted flux tube theory to construct a model for this non-expanding loop, and find that indeed with sufficient twist a coronal loop can attain equilibrium. However, we can not rule out other possibilities such as footpoint heating by small-scale reconnection, elevated scale height by a steady flow along the loop etc.

astro-ph.SR↗
arXiv2020

Asymptotic Behaviour of Time Stepping Methods for Phase Field Models

Adaptive time stepping methods for metastable dynamics of the Allen Cahn and Cahn Hilliard equations are investigated in the spatially continuous, semi-discrete setting. We analyse the performance of a number of first and second order methods, formally predicting step sizes required to satisfy specified local truncation error $σ$ in the limit of small order parameter $ε\rightarrow 0$ during meta-stable dynamics. The formal predictions are made under stability assumptions that include the preservation of the asymptotic structure of the diffuse interface, a concept we call profile fidelity. In this setting, definite statements about the relative behaviour of time stepping methods can be made. Some methods, including all so-called energy stable methods but also some fully implicit methods, require asymptotically more time steps than others.The formal analysis is confirmed in computational studies. We observe that some provably energy stable methods popular in the literature perform worse than some more standard schemes. We show further that when Backward Euler is applied to meta-stable Allen Cahn dynamics, the energy decay and profile fidelity properties for these discretizations are preserved for much larger time steps than previous analysis would suggest. The results are established asymptotically for general interfaces, with a rigorous proof for radial interfaces. It is shown analytically and computationally that for most reaction terms, Eyre type time stepping performs asymptotically worse due to loss of profile fidelity.

math.NA↗
arXiv2020

Anomalies of weight-based coherence measure and mixed maximally coherent states

As an analogy of best separable approximation (BSA) in the framework of entanglement theory, here we concentrate on the notion of best incoherent approximation, with application to characterizing and quantifying quantum coherence. From both analytical and numerical perspectives, we have demonstrated that the weight-based coherence measure displays some unusual properties, in sharp contrast to other popular coherence quantifiers. First, by deriving a closed formula for qubit states, we have showed the weight-based coherence measure exhibits a rich (geometrical) structure even in this simplest case. Second, we have identified the existence of mixed maximally coherent states (MMCS) with respect to this coherence measure and discussed the characteristic feature of MMCS in high-dimensional Hilbert spaces. Especially, we present several important families of MMCS by gaining insights from the numerical simulations. Moreover, it is pointed out that some considerations in this work can be generalized to general convex resource theories and a numerical method of improving the computational efficiency for finding the BSA is also discussed.

quant-ph↗
arXiv2020

Large-scale Real-time Personalized Similar Product Recommendations

Similar product recommendation is one of the most common scenes in e-commerce. Many recommendation algorithms such as item-to-item Collaborative Filtering are working on measuring item similarities. In this paper, we introduce our real-time personalized algorithm to model product similarity and real-time user interests. We also introduce several other baseline algorithms including an image-similarity-based method, item-to-item collaborative filtering, and item2vec, and compare them on our large-scale real-world e-commerce dataset. The algorithms which achieve good offline results are also tested on the online e-commerce website. Our personalized method achieves a 10% improvement on the add-cart number in the real-world e-commerce scenario.

cs.IR↗
arXiv2020

Long Short-Term Relation Networks for Video Action Detection

It has been well recognized that modeling human-object or object-object relations would be helpful for detection task. Nevertheless, the problem is not trivial especially when exploring the interactions between human actor, object and scene (collectively as human-context) to boost video action detectors. The difficulty originates from the aspect that reliable relations in a video should depend on not only short-term human-context relation in the present clip but also the temporal dynamics distilled over a long-range span of the video. This motivates us to capture both short-term and long-term relations in a video. In this paper, we present a new Long Short-Term Relation Networks, dubbed as LSTR, that novelly aggregates and propagates relation to augment features for video action detection. Technically, Region Proposal Networks (RPN) is remoulded to first produce 3D bounding boxes, i.e., tubelets, in each video clip. LSTR then models short-term human-context interactions within each clip through spatio-temporal attention mechanism and reasons long-term temporal dynamics across video clips via Graph Convolutional Networks (GCN) in a cascaded manner. Extensive experiments are conducted on four benchmark datasets, and superior results are reported when comparing to state-of-the-art methods.

cs.CV↗
arXiv2020

Observations of a quasi-periodic pulsation in the coronal loop and microwave flux during a solar preflare phase

We report a quasi-periodic pulsation (QPP) event simultaneously detected from the spatial displacements of coronal loop at both EUV images and microwave emission during the preflare phase of a C1.1 flare on 2016 March 23. Using the motion magnification technique, a low-amplitude transverse oscillation with the growing period is discovered in a diffuse coronal loop in Atmospheric Imaging Assembly (AIA) image sequences at wavelength of 171 A, and the initial oscillation period is estimated to be ~397 s with a slow growth rate of 0.045. At the same time, a QPP with growing periods from roughly 300 s to nearly 500 s is discovered in the microwave flux in the same active region. Based on the imaging observations measured at EUV wavelengths by the AIA and at microwave 17 GHz by Nobeyama Radioheliograph, the diffuse coronal loop and the microwave radiation source are found to be connected through a hot loop seen in AIA images at wavelength of 94 A. The growing period of the QPP should be related to the modulation of LRC-circuit oscillating process in a current-carrying plasma loop. The existence of electric currents may imply the non-potentialities in the source region during the preflare phase.

astro-ph.SR↗
arXiv2020

Feedback Graph Convolutional Network for Skeleton-based Action Recognition

Skeleton-based action recognition has attracted considerable attention in computer vision since skeleton data is more robust to the dynamic circumstance and complicated background than other modalities. Recently, many researchers have used the Graph Convolutional Network (GCN) to model spatial-temporal features of skeleton sequences by an end-to-end optimization. However, conventional GCNs are feedforward networks which are impossible for low-level layers to access semantic information in the high-level layers. In this paper, we propose a novel network, named Feedback Graph Convolutional Network (FGCN). This is the first work that introduces the feedback mechanism into GCNs and action recognition. Compared with conventional GCNs, FGCN has the following advantages: (1) a multi-stage temporal sampling strategy is designed to extract spatial-temporal features for action recognition in a coarse-to-fine progressive process; (2) A dense connections based Feedback Graph Convolutional Block (FGCB) is proposed to introduce feedback connections into the GCNs. It transmits the high-level semantic features to the low-level layers and flows temporal information stage by stage to progressively model global spatial-temporal features for action recognition; (3) The FGCN model provides early predictions. In the early stages, the model receives partial information about actions. Naturally, its predictions are relatively coarse. The coarse predictions are treated as the prior to guide the feature learning of later stages for a accurate prediction. Extensive experiments on the datasets, NTU-RGB+D, NTU-RGB+D120 and Northwestern-UCLA, demonstrate that the proposed FGCN is effective for action recognition. It achieves the state-of-the-art performance on the three datasets.

cs.CV↗
arXiv2020

Deep Phase Shifter for Quantitative Phase Imaging

A single intensity-only holographic interferogram can records the full amplitude and phase information of optical field. However, current digital holography technologies cannot recover the lossless phase information from a single interferogram. In this paper, we provide an entirely new approach for the full-field quantitative phase imaging technology. We demonstrate that deep learning can be used to replace the entitative phase shifter, and quantitative phase imaging can obtain quantitative phase from a single interferogram in in-line holography. A deep-phase-shift network (DPS-net) is reported, which can be trained with simulation training data. The trained DPS-net can be used to generate multiple interferograms with arbitrary phase shift from a single interferogram as an artificial intelligence phase shifter. The ability and the accuracy of generating arbitrary phase shifts are verified, and the performance of the proposed method is also verified by the experimental interferogram. The results demonstrate that the proposed method can provide a full digital phase shifter with high-accuracy for the technology of dynamic quantitative phase measurement.

eess.IV↗
arXiv2020

Quasi-periodic pulsation detected in Lyman-alpha emission during solar flares

We investigated the quasi-periodic pulsation (QPP) in Lyman-alpha, X-ray and extreme-ultraviolet (EUV) emissions during two solar flares, i.e., an X-class (SOL2012-01-27T) and a C-class (SOL2016-02-08T). The full-disk Lyman-alpha and X-Ray flux during these solar flares were recorded by the EUV Sensor and X-Ray Sensor on board the Geostationary Operational Environmental Satellite. The °are regions were located from the EUV images measured by the Atmospheric Imaging Assembly. The QPP could be identified as a series of regular and periodic peaks in the light curves, and its quasi-periodicity was determined from the global wavelet and Fourier power spectra. A quasi-periodicity at about 3 minutes is detected during the impulsive phase of the X-class flare, which could be explained as the acoustic wave in the chromosphere (e.g., Milligan et al. 2017). Interestingly, a quasi-periodicity at roughly 1 minute is discovered during the entire evolutionary phases of solar flares, including the precursor, impulsive, and gradual phases. This is the first report of 1-minute QPP in the Lyman-alpha emission during solar flares, in particular during the flare precursor. It may be interpreted as a self-oscillatory regime of the magnetic reconnection, such as magnetic dripping.

astro-ph.SR↗