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Hui Li

Publications and source records attributed to Hui Li.

At least 649 records · Page 36Linked to original sources
arXiv2020

Radiative Hydrodynamic Simulations of the Spectral Characteristics of Solar White-light Flares

As one of the most violent activities in the solar atmosphere, white-light flares (WLFs) are generally known for their enhanced white-light (or continuum) emission, which primarily originates in the solar lower atmosphere. However, we know little about how white-light emission is produced. In this study, we aim to investigate the response of the continua at 3600Å and 4250Å and also the H$α$ and Ly$α$ lines during WLFs modeled with radiative hydrodynamics simulations. We take non-thermal electron beams as the energy source for the WLFs in two different initial atmospheres and vary their parameters. Our results show that the model with non-thermal electron beam heating can clearly show enhancements in the continua at 3600Å and 4250Å as well as in the H$α$ and Ly$α$ lines. A larger electron beam flux, a smaller spectral index, or a penumbral initial atmosphere leads to a stronger emission increase at 3600Å, 4250Å and in the H$α$ line. For the Ly$α$ line, however, it is more preferably enhanced in a quiet-Sun initial atmosphere with a larger spectral index of the electron beam. It is also notable that the continua at 3600Å and 4250Å and the H$α$ line exhibit a dimming at the beginning of the heating and reach their peak emissions later than the peak time of the heating function, while the Ly$α$ line does not show such behaviors. These results can be served as a reference for analyzing future WLF observations.

astro-ph.SR↗
arXiv2020

Magnetic field generation from composition gradients in inertial confinement fusion fuel

Experimental asymmetries in fusion implosions can lead to magnetic field generation in the hot plasma core. For typical parameters, previous studies found that the magnetisation Hall parameter, given by the product of the electron gyro-frequency and Coulomb collision time, can exceed one. This will affect the hydrodynamics through inhibition and deflection of the electron heat flux. The magnetic field source is the collisionless Biermann term, which arises from the Debye shielding potential in electron pressure gradients. We show that there is an additional source term due to the Z dependence of the Coulomb collision operator. If there are ion composition gradients, such as jets of carbon ablator mix entering the hot-spot, this source term can rapidly exceed the Biermann fields. In addition, the Biermann fields are enhanced due to the increased temperature gradients from carbon radiative cooling. With even stronger self-generated fields, heat loss to the carbon regions will be reduced, potentially reducing the negative effect of carbon mix.

physics.plasm-ph↗
arXiv2020

NestFuse: An Infrared and Visible Image Fusion Architecture based on Nest Connection and Spatial/Channel Attention Models

In this paper we propose a novel method for infrared and visible image fusion where we develop nest connection-based network and spatial/channel attention models. The nest connection-based network can preserve significant amounts of information from input data in a multi-scale perspective. The approach comprises three key elements: encoder, fusion strategy and decoder respectively. In our proposed fusion strategy, spatial attention models and channel attention models are developed that describe the importance of each spatial position and of each channel with deep features. Firstly, the source images are fed into the encoder to extract multi-scale deep features. The novel fusion strategy is then developed to fuse these features for each scale. Finally, the fused image is reconstructed by the nest connection-based decoder. Experiments are performed on publicly available datasets. These exhibit that our proposed approach has better fusion performance than other state-of-the-art methods. This claim is justified through both subjective and objective evaluation. The code of our fusion method is available at https://github.com/hli1221/imagefusion-nestfuse

cs.CV↗
arXiv2020

Extensive Study of a Coronal Mass Ejection with UV and WL coronagraphs: the need for multi-wavelength observations

Coronal Mass Ejections (CMEs) often show different features in different band-passes. By combining data in white-light (WL) and ultraviolet (UV) bands, we have applied different techniques to derive plasma temperatures, electron density, internal radial speed, etc, within a fast CME. They serve as extensive tests of the diagnostic capabilities, developed for the observations provided by future multi-channel coronagraphs (such as Solar Orbiter/Metis, ASO-S/LST, PROBA-3/ASPIICS). The involved data include WL images acquired by SOHO/LASCO coronagraphs, and intensities measured by SOHO/UVCS at 2.45 R$_{\odot}$ in the UV (H I Ly$α$ and O VI 1032 {AA} lines) and WL channels. Data from the UVCS WL channel have been employed for the first time to measure the CME position angle with polarization-ratio technique. Plasma electron and effective temperatures of the CME core and void are estimated by combining UV and WL data. Due to the CME expansion and the possible existence of prominence segments, the transit of the CME core results in decreases of the electron temperature down to $10^{5}$ K. The front is observed as a significant dimming in the Ly$α$ intensity, associated with a line broadening due to plasma heating and flows along the line-of-sight. The 2D distribution of plasma speeds within the CME body is reconstructed from LASCO images and employed to constrain the Doppler dimming of Ly$α$ line, and simulate future CME observations by Metis and LST.

astro-ph.SR↗
arXiv2020

Magnetization around mix jets entering inertial confinement fusion fuel

Engineering features are known to cause jets of ablator material to enter the fuel hot-spot in inertial confinement fusion implosions. The Biermann battery mechanism wraps them in self-generated magnetic field. We show that higher-Z jets have an additional thermoelectric magnetic source term that is not present for hydrogen jets, verified here through a kinetic simulation. It has similar magnitude to the Biermann term. We then include this in an extended magneto-hydrodynamics approach to post-process an xRAGE radiation-hydrodynamic implosion simulation. The simulation includes an accurate model for the capsule fill tube, producing a dense carbon jet that becomes wrapped in a 4000T magnetic field. A simple spherical carbon mix model shows that this insulates the electron heat conduction enough to cause contraction of the jet to an optically thick equilibrium. The denser magnetized jet hydrodynamics could change its core penetration and therefore the final mix mass, which is known to be well correlated with fusion yield degradation. Fully exploring this will require self-consistent magneto-hydrodynamic simulations. Experimental signatures of this self-magnetization may emerge in the high energy neutron spectrum.

physics.plasm-ph↗
arXiv2020

Exploring the acceleration mechanisms for particle injection and power-law formation during trans-relativistic magnetic reconnection

Magnetic reconnection in the relativistic and trans-relativistic regimes is able to accelerate particles to hard power law energy spectra $f \propto γ^{-p}$ (approaching $p=1$). The underlying acceleration mechanism that determines the spectral shape is currently a topic of intense investigation. By means of fully kinetic plasma simulations, we carry out a study of particle acceleration during magnetic reconnection in the trans-relativistic regime of a proton-electron plasma. While earlier work in this parameter regime has focused on the effects of electric field parallel to the local magnetic field on the particle injection (from thermal energy to the lower energy bound of the power-law spectrum), here we examine the roles of both parallel and perpendicular electric fields to gain a more complete understanding on the injection process and further development of a power-law spectrum. We show that the parallel electric field does contribute significantly to particle injection, and is more important in the initial phase of magnetic reconnection. However, as the simulation proceeds, the acceleration by the perpendicular electric field becomes more important for particle injection and completely dominates the acceleration responsible for the high-energy power-law spectrum. This holds robustly, in particular for longer reconnection times and larger systems, i.e. in simulations that are more indicative of the processes in astrophysical sources.

astro-ph.HE↗
arXiv2020

Recent Progress on Particle Acceleration and Reconnection Physics during Magnetic Reconnectionin the Magnetically-dominated Relativistic Regime

Magnetic reconnection in strongly magnetized astrophysical plasma environments is believed to be the primary process for fast energy release and particle energization. Currently there is strong interest in relativistic magnetic reconnection, in that it may provide a new explanation for high-energy particle acceleration and radiation in strongly magnetized astrophysical systems. We review recent advances in particle acceleration and reconnection physics in the magnetically-dominated regime. More discussion is focused on the physics of particle acceleration, power-law formation as well as the reconnection rate problem. In addition, we provide an outlook for studying reconnection acceleration mechanisms and kinetic physics in the next step.

astro-ph.HE↗
arXiv2020

Non-adiabatic quantum interference effects and chaoticity in the ultracold Li + LiNa $\to$ Li$_2$ + Na reaction

Electronically non-adiabatic effects play an important role in many chemical reactions. How these effects manifest in cold and ultracold chemistry remain largely unexplored. Here, through first principles non-adiabatic quantum dynamics calculations of the Li + LiNa $\to$ Li$_2$ + Na chemical reaction, it is shown that non-adiabatic dynamics induces quantum interference effects that dramatically alter the ultracold rotationally resolved reaction rate coefficients. The interference effect arises from a conical intersection between the ground and an excited electronic state that is energetically accessible even for ultracold collisions. These unique interference effects might be exploited for quantum control applications as a quantum molecular switch. A statistical analysis of rotational populations of the Li$_2$ product reveals a Poisson distribution implying an underlying classically chaotic dynamics. The Poisson distribution is robust and amenable to experimental verification and appears to be a universal property of ultracold reactions involving alkali metal dimers.

physics.chem-ph↗
arXiv2020

Effects of Conical Intersections on Hyperfine Quenching of Hydroxyl OH in collision with an ultracold Sr atom

The effect of conical intersections (CIs) on electronic relaxation, transitions from excited states to ground states, is well studied, but their influence on hyperfine quenching in a reactant molecule is not known. Here, we report on ultracold collision dynamics of the hydroxyl free-radical OH with Sr atoms leading to quenching of OH hyperfine states. Our quantum-mechanical calculations of this process reveal that quenching is efficient due to anomalous molecular dynamics in the vicinity of the conical intersection at collinear geometry. We observe wide scattering resonance features in both elastic and inelastic rate coefficients at collision energies below k x 10 mK. They are identified as either p- or d-wave shape resonances. We also describe the electronic potentials relevant for these non-reactive collisions, their diabatization procedure, as well as the non-adiabatic coupling between the diabatic potentials near the CIs.

physics.chem-ph↗
arXiv2020

Machine-learning-based methods for output only structural modal identification

In this study, we propose a machine-learning-based approach to identify the modal parameters of the output-only data for structural health monitoring (SHM) that makes full use of the characteristic of independence of modal responses and the principle of machine learning. By taking advantage of the independence feature of each mode, we use the principle of unsupervised learning, making the training process of the deep neural network becomes the process of modal separation. A self-coding deep neural network is designed to identify the structural modal parameters from the vibration data of structures. The mixture signals, that is, the structural response data, are used as the input of the neural network. Then we use a complex loss function to restrict the training process of the neural network, making the output of the third layer the modal responses we want, and the weights of the last two layers are mode shapes. The deep neural network is essentially a nonlinear objective function optimization problem. A novel loss function is proposed to constrain the independent feature with consideration of uncorrelation and non-Gaussianity to restrict the designed neural network to obtain the structural modal parameters. A numerical example of a simple structure and an example of actual SHM data from a cable-stayed bridge are presented to illustrate the modal parameter identification ability of the proposed approach. The results show the approach's good capability in blindly extracting modal information from system responses.

cs.LG↗
arXiv2020

A Robust Attentional Framework for License Plate Recognition in the Wild

Recognizing car license plates in natural scene images is an important yet still challenging task in realistic applications. Many existing approaches perform well for license plates collected under constrained conditions, eg, shooting in frontal and horizontal view-angles and under good lighting conditions. However, their performance drops significantly in an unconstrained environment that features rotation, distortion, occlusion, blurring, shading or extreme dark or bright conditions. In this work, we propose a robust framework for license plate recognition in the wild. It is composed of a tailored CycleGAN model for license plate image generation and an elaborate designed image-to-sequence network for plate recognition. On one hand, the CycleGAN based plate generation engine alleviates the exhausting human annotation work. Massive amount of training data can be obtained with a more balanced character distribution and various shooting conditions, which helps to boost the recognition accuracy to a large extent. On the other hand, the 2D attentional based license plate recognizer with an Xception-based CNN encoder is capable of recognizing license plates with different patterns under various scenarios accurately and robustly. Without using any heuristics rule or post-processing, our method achieves the state-of-the-art performance on four public datasets, which demonstrates the generality and robustness of our framework. Moreover, we released a new license plate dataset, named "CLPD", with 1200 images from all 31 provinces in mainland China. The dataset can be available from: https://github.com/wangpengnorman/CLPD_dataset.

cs.CV↗
arXiv2020

Outlier Detection Using a Novel method: Quantum Clustering

We propose a new assumption in outlier detection: Normal data instances are commonly located in the area that there is hardly any fluctuation on data density, while outliers are often appeared in the area that there is violent fluctuation on data density. And based on this hypothesis, we apply a novel density-based approach to unsupervised outlier detection. This approach, called Quantum Clustering (QC), deals with unlabeled data processing and constructs a potential function to find the centroids of clusters and the outliers. The experiments show that the potential function could clearly find the hidden outliers in data points effectively. Besides, by using QC, we could find more subtle outliers by adjusting the parameter $σ$. Moreover, our approach is also evaluated on two datasets (Air Quality Detection and Darwin Correspondence Project) from two different research areas, and the results show the wide applicability of our method.

cs.LG↗
arXiv2020

Mitigating Query-Flooding Parameter Duplication Attack on Regression Models with High-Dimensional Gaussian Mechanism

Public intelligent services enabled by machine learning algorithms are vulnerable to model extraction attacks that can steal confidential information of the learning models through public queries. Differential privacy (DP) has been considered a promising technique to mitigate this attack. However, we find that the vulnerability persists when regression models are being protected by current DP solutions. We show that the adversary can launch a query-flooding parameter duplication (QPD) attack to infer the model information by repeated queries. To defend against the QPD attack on logistic and linear regression models, we propose a novel High-Dimensional Gaussian (HDG) mechanism to prevent unauthorized information disclosure without interrupting the intended services. In contrast to prior work, the proposed HDG mechanism will dynamically generate the privacy budget and random noise for different queries and their results to enhance the obfuscation. Besides, for the first time, HDG enables an optimal privacy budget allocation that automatically determines the minimum amount of noise to be added per user-desired privacy level on each dimension. We comprehensively evaluate the performance of HDG using real-world datasets and shows that HDG effectively mitigates the QPD attack while satisfying the privacy requirements. We also prepare to open-source the relevant codes to the community for further research.

cs.CR↗
arXiv2020

Effects of initial density profiles on massive star cluster formation in giant molecular clouds

We perform a suite of hydrodynamic simulations to investigate how initial density profiles of giant molecular clouds (GMCs) affect their subsequent evolution. We find that the star formation duration and integrated star formation efficiency of the whole clouds are not sensitive to the choice of different profiles but are mainly controlled by the interplay between gravitational collapse and stellar feedback. Despite this similarity, GMCs with different profiles show dramatically different modes of star formation. For shallower profiles, GMCs first fragment into many self-gravitation cores and form sub-clusters that distributed throughout the entire clouds. These sub-clusters are later assembled ``hierarchically'' to central clusters. In contrast, for steeper profiles, a massive cluster is quickly formed at the center of the cloud and then gradually grows its mass via gas accretion. Consequently, central clusters that emerged from clouds with shallower profiles are less massive and show less rotation than those with the steeper profiles. This is because 1) a significant fraction of mass and angular momentum in shallower profiles is stored in the orbital motion of the sub-clusters that are not able to merge into the central clusters 2) frequent hierarchical mergers in the shallower profiles lead to further losses of mass and angular momentum via violent relaxation and tidal disruption. Encouragingly, the degree of cluster rotations in steeper profiles is consistent with recent observations of young and intermediate-age clusters. We speculate that rotating globular clusters are likely formed via an ``accretion'' mode from centrally-concentrated clouds in the early Universe.

astro-ph.GA↗
arXiv2020

The Preservation of Super Earths and the Emergence of Gas Giants after Their Progenitor Cores have Entered the Pebble Isolation Phase

The omnipresence of super-Earths suggests that they are able to be retained in natal disks around low-mass stars, whereas exoplanets' mass distribution indicates that some cores have transformed into gas giants through runaway gas accretion at 1AU from solar-type stars. In this paper, we show that transition to runaway gas accretion by cores may be self-impeded by an increase of the grain opacity in their envelope after they have acquired sufficient mass (typically 10Mearth) to enter a pebble-isolation phase. The accumulation of mm-m size pebbles in their migration barriers enhances their local fragmentation rates. The freshly produced sub-mm grains pass through the barrier, elevate the effective dust opacity and reduce the radiative flux in the cores envelope. These effects alone are adequate to suppress the transition to runaway accretion and preserve super-Earths in the stellar proximity (0.1 AU), albeit entropy advection between the envelope and the disk can further reduce the accretion rate. At intermediate distance (1AU) from their host stars, the escalation in the dust opacity dominates over entropy advection in stalling the transition to runaway accretion for marginally pebble-isolated cores. Beyond a few AU, the transformation of more massive cores to gas giants is reachable before severe depletion of disk gas. This requirement can be satisfied either in extended disks with large scale height via orderly accretion of migrating pebbles or through the mergers of oligarchic protoplanetary embryos, and can account for the correlated occurrence of long-period gas giants and close-in super-Earths.

astro-ph.EP↗
arXiv2020

Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction

A learning-based 3D reconstruction method for long-span bridges is proposed in this paper. 3D reconstruction generates a 3D computer model of a real object or scene from images, it involves many stages and open problems. Existing point-based methods focus on generating 3D point clouds and their reconstructed polygonal mesh or fitting-based geometrical models in urban scenes civil structures reconstruction within Manhattan world constrains and have made great achievements. Difficulties arise when an attempt is made to transfer these systems to structures with complex topology and part relations like steel trusses and long-span bridges, this could be attributed to point clouds are often unevenly distributed with noise and suffer from occlusions and incompletion, recovering a satisfactory 3D model from these highly unstructured point clouds in a bottom-up pattern while preserving the geometrical and topological properties makes enormous challenge to existing algorithms. Considering the prior human knowledge that these structures are in conformity to regular spatial layouts in terms of components, a learning-based topology-aware 3D reconstruction method which can obtain high-level structural graph layouts and low-level 3D shapes from images is proposed in this paper. We demonstrate the feasibility of this method by testing on two real long-span steel truss cable-stayed bridges.

cs.CV↗