arXiv subjects

Fei Li

Publications and source records attributed to Fei Li.

At least 181 records · Page 10Linked to original sources

arXiv2021

In Situ Generation of High-Energy Spin-Polarized Electrons in a Beam-Driven Plasma Wakefield Accelerator

In situ generation of a high-energy, high-current, spin-polarized electron beam is an outstanding scientific challenge to the development of plasma-based accelerators for high-energy colliders. In this Letter we show how such a spin-polarized relativistic beam can be produced by ionization injection of electrons of certain atoms with a circularly polarized laser field into a beam-driven plasma wakefield accelerator, providing a much desired one-step solution to this challenge. Using time-dependent Schrödinger equation (TDSE) simulations, we show the propensity rule of spin-dependent ionization of xenon atoms can be reversed in the strong-field multi-photon regime compared with the non-adiabatic tunneling regime, leading to high total spin-polarization. Furthermore, three-dimensional particle-in-cell (PIC) simulations are incorporated with TDSE simulations, providing start-to-end simulations of spin-dependent strong-field ionization of xenon atoms and subsequent trapping, acceleration, and preservation of electron spin-polarization in lithium plasma. We show the generation of a high-current (0.8 kA), ultra-low-normalized-emittance (~37 nm), and high-energy (2.7 GeV) electron beam within just 11 cm distance, with up to ~31% net spin polarization. Higher current, energy, and net spin-polarization beams are possible by optimizing this concept, thus solving a long-standing problem facing the development of plasma accelerators.

physics.plasm-ph↗

arXiv2021

Unified Named Entity Recognition as Word-Word Relation Classification

So far, named entity recognition (NER) has been involved with three major types, including flat, overlapped (aka. nested), and discontinuous NER, which have mostly been studied individually. Recently, a growing interest has been built for unified NER, tackling the above three jobs concurrently with one single model. Current best-performing methods mainly include span-based and sequence-to-sequence models, where unfortunately the former merely focus on boundary identification and the latter may suffer from exposure bias. In this work, we present a novel alternative by modeling the unified NER as word-word relation classification, namely W^2NER. The architecture resolves the kernel bottleneck of unified NER by effectively modeling the neighboring relations between entity words with Next-Neighboring-Word (NNW) and Tail-Head-Word-* (THW-*) relations. Based on the W^2NER scheme we develop a neural framework, in which the unified NER is modeled as a 2D grid of word pairs. We then propose multi-granularity 2D convolutions for better refining the grid representations. Finally, a co-predictor is used to sufficiently reason the word-word relations. We perform extensive experiments on 14 widely-used benchmark datasets for flat, overlapped, and discontinuous NER (8 English and 6 Chinese datasets), where our model beats all the current top-performing baselines, pushing the state-of-the-art performances of unified NER.

cs.CL↗

arXiv2021

Mastering the Explicit Opinion-role Interaction: Syntax-aided Neural Transition System for Unified Opinion Role Labeling

Unified opinion role labeling (ORL) aims to detect all possible opinion structures of 'opinion-holder-target' in one shot, given a text. The existing transition-based unified method, unfortunately, is subject to longer opinion terms and fails to solve the term overlap issue. Current top performance has been achieved by employing the span-based graph model, which however still suffers from both high model complexity and insufficient interaction among opinions and roles. In this work, we investigate a novel solution by revisiting the transition architecture, and augmenting it with a pointer network (PointNet). The framework parses out all opinion structures in linear-time complexity, meanwhile breaks through the limitation of any length of terms with PointNet. To achieve the explicit opinion-role interactions, we further propose a unified dependency-opinion graph (UDOG), co-modeling the syntactic dependency structure and the partial opinion-role structure. We then devise a relation-centered graph aggregator (RCGA) to encode the multi-relational UDOG, where the resulting high-order representations are used to promote the predictions in the vanilla transition system. Our model achieves new state-of-the-art results on the MPQA benchmark. Analyses further demonstrate the superiority of our methods on both efficacy and efficiency.

cs.CL↗

arXiv2021

ALMA observations of NGC 6334S. II. Subsonic and Transonic Narrow Filaments in a High-mass Star Formation Cloud

We present a study of narrow filaments toward a massive infrared dark cloud, NGC 6334S, using the Atacama Large Millimeter/submillimeter Array (ALMA). Thirteen gas filaments are identified using the H$^{13}$CO$^{+}$ line, while a single continuum filament is revealed by the continuum emission. The filaments present a compact radial distribution with a median filament width of $\sim$0.04 pc narrower than the previously proposed `quasi-universal' 0.1~pc filament width. The higher spatial resolution observations and higher-density gas tracer tend to identify even narrower and lower mass filaments. The filament widths are roughly twice the size of embedded cores. The gas filaments are largely supported by thermal motions. The nonthermal motions are predominantly subsonic and transonic in both identified gas filaments and embedded cores, which may imply that stars are likely born in environments of low turbulence. A fraction of embedded objects show a narrower velocity dispersion compared with their corresponding natal filaments, which may indicate that the turbulent dissipation is taking place in these embedded cores. The physical properties (mass, mass per unit length, gas kinematics, and width) of gas filaments are analogous to those of narrow filaments found in low- to high-mass star-forming regions. The more evolved sources are found to be farther away from the filaments, a situation that may have resulted from the relative motions between the YSOs and their natal filaments.

astro-ph.GA↗

arXiv2021

Strain Tunable Intrinsic Ferromagnetic in 2D Square CrBr$_2$

Two-dimensional (2D) intrinsic magnetic materials with high Curie temperature (Tc) coexisting with 100% spin-polarization are highly desirable for realizing promising spintronic devices. In the present work, the intrinsic magnetism of monolayer square CrBr2 is predicted by using first-principles calculations. The monolayer CrBr2 is an intrinsic ferromagnetic (FM) half-metal with the half-metallic gap of 1.58 eV. Monte Carlo simulations based on the Heisenberg model estimates Tc as 212 K. Furthermore, the large compressive strain makes CrBr2 undergo ferromagnetic-antiferromagnetic phase transition, when the biaxial tensile strain larger than 9.3% leads to the emergence of semiconducting electronic structures. Our results show that the intrinsic half-metal with a high Tc and controllable magnetic properties endow monolayer square CrBr2 a potential material for spintronic applications.

cond-mat.mtrl-sci↗

arXiv2021

Unified generation and fast emission of arbitrary single-photon multimode $W$ states

We propose a unified and deterministic scheme to generate arbitrary single-photon multimode $W$ states in circuit QED. A three-level system (qutrit) is driven by a pump-laser pulse and coupled to $N$ spatially separated resonators. The coupling strength for each spatial mode $g_i$ totally decide the generated single-photon N-mode $W$ state $\vert W_N \rangle=\frac{1}{A}\sum_{i=1}^N g_i|0_1 0_2 \cdots 1_i 0_{i+1}\cdots 0_N\rangle$, so arbitrary $\vert W_N \rangle$ can be generated just by tuning $g_i$. We could not only generate $W$ states inside resonators but also release them into transmission lines on demand. The time and fidelity for generating (or emitting) $\vert W_N \rangle$ can both be the same for arbitrary $N$. Remarkably, $\vert W_N\rangle$ can be emitted with probability reaching $98.9\%$ in $20-50$ ns depending on parameters, comparable to the recently reported fastest two-qubit gate ($30-45$ ns). Finally, the time evolution process is convenient to control since only the pump pulse is time-dependent.

quant-ph↗

arXiv2021

Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging

Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extract efficient feature embedding. We have optimized the architecture of two different networks for CT images: (i) a novel conditional variational autoencoder (CVAE) with a specific architecture that integrates the class labels inside the encoder layers and uses side information with shared attention layers for the encoder, which make the most of the contextual clues for representation learning, and (ii) a downstream convolutional neural network for supervised classification using the encoder structure of the CVAE. With the explainable classification results, the proposed diagnosis system is very effective for COVID-19 classification. Based on the promising results obtained qualitatively and quantitatively, we envisage a wide deployment of our developed technique in large-scale clinical studies.Code is available at https://git.etrovub.be/AVSP/ct-based-covid-19-diagnostic-tool.git.

eess.IV↗

arXiv2021

Stopping Power Enhancement From Discrete Particle-Wake Correlations in High Energy Density Plasmas

Three-dimensional (3D) simulations of electron beams propagating in high energy density (HED) plasmas using the quasi-static Particle-in-Cell (PIC) code QuickPIC demonstrate a significant increase in stopping power when beam electrons mutually interact via their wakes. Each beam electron excites a plasma wave wake of wavelength $\sim2πc/ω_{pe}$, where $c$ is the speed of light and $ω_{pe}$ is the background plasma frequency. We show that a discrete collection of electrons undergoes a beam-plasma like instability caused by mutual particle-wake interactions that causes electrons to bunch in the beam, even for beam densities $n_b$ for which fluid theory breaks down. This bunching enhances the beam's stopping power, which we call "correlated stopping," and the effect increases with the "correlation number" $N_b \equiv n_b (c/ω_{pe})^3$. For example, a beam of monoenergetic 9.7 MeV electrons with $N_b=1/8$, in a cold background plasma with $n_e=10^{26}$ cm$^{-3}$ (450 g cm$^{-3}$ DT), has a stopping power of $2.28\pm0.04$ times the single-electron value, which increases to $1220\pm5$ for $N_b=64$. The beam also experiences transverse filamentation, which eventually limits the stopping enhancement.

physics.plasm-ph↗

arXiv2021

A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition

Research on overlapped and discontinuous named entity recognition (NER) has received increasing attention. The majority of previous work focuses on either overlapped or discontinuous entities. In this paper, we propose a novel span-based model that can recognize both overlapped and discontinuous entities jointly. The model includes two major steps. First, entity fragments are recognized by traversing over all possible text spans, thus, overlapped entities can be recognized. Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession. In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities. As a whole, our model can be regarded as a relation extraction paradigm essentially. Experimental results on multiple benchmark datasets (i.e., CLEF, GENIA and ACE05) show that our model is highly competitive for overlapped and discontinuous NER.

cs.CL↗

arXiv2021

A Low-mass Cold and Quiescent Core Population in a Massive Star Protocluster

Pre-stellar cores represent the initial conditions of star formation. Although these initial conditions in nearby low-mass star-forming regions have been investigated in detail, such initial conditions remain vastly unexplored for massive star-forming regions. We report the detection of a cluster of low-mass starless and pre-stellar core candidates in a massive star protocluster forming cloud, NGC6334S. With the ALMA observations at a $\sim$0.02 pc spatial resolution, we identified 17 low-mass starless core candidates that do not show any evidence of protostellar activity. These candidates present small velocity dispersions, high fractional abundances of NH$_{2}$D, high NH$_{3}$ deuterium fractionations, and are completely dark in the infrared wavelengths from 3.6 up to 70~$μ$m. Turbulence is significantly dissipated and the gas kinematics are dominated by thermal motions toward these candidates. Nine out of the 17 cores are gravitationally bound, and therefore are identified as pre-stellar core candidates. The embedded cores of NGC6334S show a wide diversity in masses and evolutionary stages.

astro-ph.GA↗

arXiv2021

Stochastic Model Predictive Control for Linear Systems with Unbounded Additive Uncertainties

This paper presents two stochastic model predictive control methods for linear time-invariant systems subject to unbounded additive uncertainties. The new methods are developed by formulating the chance constraints into deterministic form, which are treated in analogy with robust constraints, by using the probabilistic reachable set. Firstly, the probabilistically resolvable time-varying tube-based stochastic model predictive control algorithm is designed by employing the time-varying probabilistic reachable sets as the tubes. Secondly, by utilizing the probabilistic positively invariant set, the probabilistically resolvable constant tube-based stochastic model predictive control algorithm is developed by employing the constantly tightened constraints in the entire prediction horizons. In addition, to enhance the feasibility of the algorithms, the soft constraints are imposed to the state initializations. The algorithm feasibility and closed-loop stability results are provided. The efficacy of the approaches are demonstrated by means of numerical simulations.

eess.SY↗

arXiv2021

Accurately simulating nine-dimensional phase space of relativistic particles in strong fields

Next-generation high-power lasers that can be focused to intensities exceeding 10^23 W/cm^2 are enabling new physics and applications. The physics of how these lasers interact with matter is highly nonlinear, relativistic, and can involve lowest-order quantum effects. The current tool of choice for modeling these interactions is the particle-in-cell (PIC) method. In strong fields, the motion of charged particles and their spin is affected by radiation reaction. Standard PIC codes usually use Boris or its variants to advance the particles, which requires very small time steps in the strong-field regime to obtain accurate results. In addition, some problems require tracking the spin of particles, which creates a 9D particle phase space (x, u, s). Therefore, numerical algorithms that enable high-fidelity modeling of the 9D phase space in the strong-field regime are desired. We present a new 9D phase space particle pusher based on analytical solutions to the position, momentum and spin advance from the Lorentz force, together with the semi-classical form of RR in the Landau-Lifshitz equation and spin evolution given by the Bargmann-Michel-Telegdi equation. These analytical solutions are obtained by assuming a locally uniform and constant electromagnetic field during a time step. The solutions provide the 9D phase space advance in terms of a particle's proper time, and a mapping is used to determine the proper time step for each particle from the simulation time step. Due to the analytical integration, the constraint on the time step needed to resolve trajectories in ultra-high fields can be greatly reduced. We present single-particle simulations and full PIC simulations to show that the proposed particle pusher can greatly improve the accuracy of particle trajectories in 9D phase space for given laser fields. A discussion on the numerical efficiency of the proposed pusher is also provided.

physics.comp-ph↗

arXiv2021

A multi-sheath model for highly nonlinear plasma wakefields

An improved description for nonlinear plasma wakefields with phase velocities near the speed of light is presented and compared against fully kinetic particle-in-cell simulations. These wakefields are excited by intense particle beams or lasers pushing plasma electrons radially outward, creating an ion bubble surrounded by a sheath of electrons characterized by the source term $S \equiv -\frac{1}{en_p}(ρ-J_z/c)$ where $ρ$ and $J_z$ are the charge and axial current densities. Previously, the sheath source term was described phenomenologically with a positive-definite function, resulting in a positive definite wake potential. In reality, the wake potential is negative at the rear of the ion column which is important for self-injection and accurate beam loading models. To account for this, we introduce a multi-sheath model in which the source term, $S$, of the plasma wake can be negative in regions outside the ion bubble. Using this model, we obtain a new expression for the wake potential and a modified differential equation for the bubble radius. Numerical results obtained from these equations are validated against particle-in-cell simulations for unloaded and loaded wakes. The new model provides accurate predictions of the shape and duration of trailing bunch current profiles that flatten plasma wakefields. It is also used to design a trailing bunch for a desired longitudinally varying loaded wakefield. We present beam loading results for laser wakefields and discuss how the model can be improved for laser drivers in future work. Finally, we discuss differences between the predictions of the multi- and single-sheath models for beam loading.

physics.plasm-ph↗

arXiv2021

Open-set Intersection Intention Prediction for Autonomous Driving

Intention prediction is a crucial task for Autonomous Driving (AD). Due to the variety of size and layout of intersections, it is challenging to predict intention of human driver at different intersections, especially unseen and irregular intersections. In this paper, we formulate the prediction of intention at intersections as an open-set prediction problem that requires context specific matching of the target vehicle state and the diverse intersection configurations that are in principle unbounded. We capture map-centric features that correspond to intersection structures under a spatial-temporal graph representation, and use two MAAMs (mutually auxiliary attention module) that cover respectively lane-level and exitlevel intentions to predict a target that best matches intersection elements in map-centric feature space. Under our model, attention scores estimate the probability distribution of the openset intentions that are contextually defined by the structure of the current intersection. The proposed model is trained and evaluated on simulated dataset. Furthermore, the model, trained on simulated dataset and without any fine tuning, is directly validated on in-house real-world dataset collected at 98 realworld intersections and exhibits satisfactory performance,demonstrating the practical viability of our approach.

cs.RO↗

arXiv2021

Multiple HC$_3$N line observations towards 19 Galactic massive star-forming regions

We performed observations of the HC$_3$N (24-23, 17-16, 11-10, 8-7) lines towards a sample consisting of 19 Galactic massive star-forming regions with the Arizona Radio Observatory 12-m and Caltech Submillimeter Observatory 10.4-m telescopes. HC$_3$N (24-23, 17-16, 11-10, 8-7) lines were detected in sources except for W44, where only HC$_3$N (17-16, 11-10) were detected. Twelve of the nineteen sources showed probable line wing features. The excitation temperatures were estimated from the line ratio of HC$_3$N (24-23) to HC$_3$N (17-16) for 18 sources and are in the range 23-57 K. The line widths of higher-$J$ transitions are larger than lower-$J$ ones for most sources. This indicates that the inner dense warm regions have more violent turbulence or other motions (such as rotation) than outer regions in these sources. A possible cutoff tendency was found around $L_{\rm IR}$ $\sim$ 10$^{6}$ $L_\odot$ in the relation between $L_{\rm IR}$ and full width at half maximum line widths.

astro-ph.GA↗

arXiv2021

Dense gas in local galaxies revealed by multiple tracers

We present 3 mm and 2 mm band simultaneously spectroscopic observations of HCN 1-0, HCO$^{+}$ 1-0, HNC 1-0, and CS 3-2 with the IRAM 30 meter telescope, toward a sample of 70 sources as nearby galaxies with infrared luminosities ranging from several 10$^{5}L_{\odot}$ to more than 10$^{12}L_{\odot}$. After combining HCN 1-0, HCO$^{+}$ 1-0 and HNC 1-0 data from literature with our detections, relations between luminosities of dense gas tracers (HCN 1-0, HCO$^{+}$ 1-0 and HNC 1-0) and infrared luminosities are derived, with tight linear correlations for all tracers. Luminosities of CS 3-2 with only our observations also show tight linear correlation with infrared luminosities. No systematic difference is found for tracing dense molecular gas among these tracers. Star formation efficiencies for dense gas with different tracers also do not show any trend along different infrared luminosities. Our study also shows that HCN/HCO$^{+}$ line ratio might not be a good indicator to diagnose obscured AGN in galaxies.

astro-ph.GA↗

arXiv2021

Pedestrian Motion State Estimation From 2D Pose

Traffic violation and the flexible and changeable nature of pedestrians make it more difficult to predict pedestrian behavior or intention, which might be a potential safety hazard on the road. Pedestrian motion state (such as walking and standing) directly affects or reflects its intention. In combination with pedestrian motion state and other influencing factors, pedestrian intention can be predicted to avoid unnecessary accidents. In this paper, pedestrian is treated as non-rigid object, which can be represented by a set of two-dimensional key points, and the movement of key point relative to the torso is introduced as micro motion. Static and dynamic micro motion features, such as position, angle and distance, and their differential calculations in time domain, are used to describe its motion pattern. Gated recurrent neural network based seq2seq model is used to learn the dependence of motion state transition on previous information, finally the pedestrian motion state is estimated via a softmax classifier. The proposed method only needs the previous hidden state of GRU and current feature to evaluate the probability of current motion state, and it is computation efficient to deploy on vehicles. This paper verifies the proposed algorithm on the JAAD public dataset, and the accuracy is improved by 11.6% compared with the existing method.

cs.CV↗

arXiv2020

Probing the CGM of Low-redshift Dwarf Galaxies Using FIRE Simulations

Observations of UV metal absorption lines have provided insight into the structure and composition of the circumgalactic medium (CGM) around galaxies. We compare these observations with the low-redshift ($z \leq 0.3$) CGM around dwarf galaxies in high-resolution cosmological zoom-in runs in the FIRE-2 simulation suite. We select simulated galaxies that match the halo mass, stellar mass, and redshift of the observed samples. We produce absorption measurements using Trident for UV transitions of C IV, O VI, Mg II and Si III. The FIRE equivalent width (EW) distributions and covering fractions for the C IV ion are broadly consistent with observations inside $0.5 R_{vir}$, but are under-predicted for O VI, Mg II, and Si III. The absorption strengths of the ions in the CGM are moderately correlated with the masses and star formation activity of the galaxies. The correlation strengths increase with the ionization potential of the ions. The structure and composition of the gas from the simulations exhibit three zones around dwarf galaxies characterized by distinct ion column densities: the disky ISM, the inner CGM (the wind-dominated regime), and the outer CGM (the IGM accretion-dominated regime). We find that the outer CGM in the simulations is nearly but not quite supported by thermal pressure, so it is not in hydrostatic equilibrium (HSE), resulting in halo-scale bulk inflow and outflow motions. The net gas inflow rates are comparable to the SFR of the galaxy, but the bulk inflow and outflow rates are greater by an order of magnitude, with velocities comparable to the virial velocity of the halo. These roughly virial velocities (${\sim} 100 km s^{-1}$) produce large EWs in the simulations. This supports a picture for dwarf galaxies in which the dynamics of the CGM at large scales are coupled to the small-scale star formation activity near the centre of their halos.

astro-ph.GA↗