arXiv subjects

Fei Li

Publications and source records attributed to Fei Li.

At least 217 records · Page 12Linked to original sources

arXiv2020

ALMA observations of NGC 6334S $-$ I: Forming massive stars and cluster in subsonic and transonic filamentary clouds

We present Atacama Large Millimeter/submillimeter Array (ALMA) and Karl G. Jansky Very Large Array (JVLA) observations of the massive infrared dark cloud NGC 6334S (also known as IRDC G350.56+0.44), located at the southwestern end of the NGC 6334 molecular cloud complex. The H$^{13}$CO$^{+}$ and the NH$_{2}$D lines covered by the ALMA observations at a $\sim$3$^{\prime\prime}$ angular resolution ($\sim$0.02 pc) reveal that the spatially unresolved non-thermal motions are predominantly subsonic and transonic, a condition analogous to that found in low-mass star-forming molecular clouds. The observed supersonic non-thermal velocity dispersions in massive star forming regions, often reported in the literature, might be significantly biased by poor spatial resolutions that broaden the observed line widths due to unresolved motions within the telescope beam. Our 3~mm continuum image resolves 49 dense cores, whose masses range from 0.17 to 14 $M_{\odot}$. The majority of them are resolved with multiple velocity components. Our analyses of these gas velocity components find an anti-correlation between the gas mass and the virial parameter. This implies that the more massive structures tend to be more gravitationally unstable. Finally, we find that the external pressure in the NGC 6334S cloud is important in confining these dense structures, and may play a role in the formation of dense cores, and subsequently, the embedded young stars.

astro-ph.GA↗

arXiv2020

HCN 3-2 survey towards a sample of local galaxies

We present observations of HCN 3-2 emissions towards 37 local galaxies using 10-m Submillimeter Telescope (SMT). HCN 3-2 emission is detected in 23 galaxies. The correlation of infrared luminosity (LIR) and the luminosity of HCN 3-2 line emission measured in our sample is fitted with a slope of 1.11 and correlation coefficient of 0.91, which follows the linear correlation found in other dense gas tracers in the literatures. Although molecular gas above a certain volume density threshold (i.e., $n_{\rm H_2}\geq$ 10$^4$ cm$^{-3}$) statistically gave similar relation with infrared luminosity, the large scatter of HCN 3-2/HCN1-0 ratios for galaxies with different LIR indicates that dense gas masses estimated from the line luminosities of only one transition of dense gas tracers should be treated with caution for individual galaxies.

astro-ph.GA↗

arXiv2020

Unified model selection approach based on minimum description length principle in Granger causality analysis

Granger causality analysis (GCA) provides a powerful tool for uncovering the patterns of brain connectivity mechanism using neuroimaging techniques. Conventional GCA applies two different mathematical theories in a two-stage scheme: (1) the Bayesian information criterion (BIC) or Akaike information criterion (AIC) for the regression model orders associated with endogenous and exogenous information; (2) F-statistics for determining the causal effects of exogenous variables. While specifying endogenous and exogenous effects are essentially the same model selection problem, this could produce different benchmarks in the two stages and therefore degrade the performance of GCA. In this course, we present a unified model selection approach based on the minimum description length (MDL) principle for GCA in the context of the general regression model paradigm. Compared with conventional methods, our approach emphasize that a single mathematical theory should be held throughout the GCA process. Under this framework, all candidate models within the model space might be compared freely in the context of the code length, without the need for an intermediate model. We illustrate its advantages over conventional two-stage GCA approach in a 3-node network and a 5-node network synthetic experiments. The unified model selection approach is capable of identifying the actual connectivity while avoiding the false influences of noise. More importantly, the proposed approach obtained more consistent results in a challenge fMRI dataset for causality investigation, mental calculation network under visual and auditory stimulus, respectively. The proposed approach has potential to accommodate other Granger causality representations in other function space. The comparison between different GC representations in different function spaces can also be naturally deal with in the framework.

stat.ME↗

arXiv2020

A quasi-static particle-in-cell algorithm based on an azimuthal Fourier decomposition for highly efficient simulations of plasma-based acceleration: QPAD

The 3D quasi-static particle-in-cell (PIC) algorithm is a very efficient method for modeling short-pulse laser or relativistic charged particle beam-plasma interactions. In this algorithm, the plasma response to a non-evolving laser or particle beam is calculated using Maxwell's equations based on the quasi-static approximate equations that exclude radiation. The plasma fields are then used to advance the laser or beam forward using a large time step. The algorithm is many orders of magnitude faster than a 3D fully explicit relativistic electromagnetic PIC algorithm. It has been shown to be capable to accurately model the evolution of lasers and particle beams in a variety of scenarios. At the same time, an algorithm in which the fields, currents and Maxwell equations are decomposed into azimuthal harmonics has been shown to reduce the complexity of a 3D explicit PIC algorithm to that of a 2D algorithm when the expansion is truncated while maintaining accuracy for problems with near azimuthal symmetry. This hybrid algorithm uses a PIC description in r-z and a gridless description in $ϕ$. We describe a novel method that combines the quasi-static and hybrid PIC methods. This algorithm expands the fields, charge and current density into azimuthal harmonics. A set of the quasi-static field equations are derived for each harmonic. The complex amplitudes of the fields are then solved using the finite difference method. The beam and plasma particles are advanced in Cartesian coordinates using the total fields. Details on how this algorithm was implemented using a similar workflow to an existing quasi-static code, QuickPIC, are presented. The new code is called QPAD for QuickPIC with Azimuthal Decomposition. Benchmarks and comparisons between a fully 3D explicit PIC code, a full 3D quasi-static code, and the new quasi-static PIC code with azimuthal decomposition are also presented.

physics.comp-ph↗

arXiv2019

Imaging the geometrical structure of complex symmetric molecules by above-threshold ionization spectrum in an IR+XUV laser field

Based on the frequency-domain theory, we put forward a method of imaging the complex symmetrical polyatomic molecules by using IR+XUV two-color laser fields. Although the wave function of a polyatomic molecule, such as SF6, is quite complex in momentum space, it can be simplified by several terms in the region of high value of momentum, where the destructive interference fringes from these terms carry the information of the molecular structure. In this work, we find that, since the XUV laser field may increases the kinetic energy of the photoelectron dramatically in the above-threshold ionization (ATI) process of a molecule in an IR+XUV two-color laser field, the information of the molecular structure can be obtained by analyzing the interference fringes of the ATI spectrum in the high-energy region, where the simplified wave function of the molecule plays a dominant role in this part of the ATI spectrum. This method provides a unique accessible route towards imaging polyatomic molecules from a frequency perspective.

physics.optics↗

arXiv2019

Post-processing techniques of 4D flow MRI: velocity and wall shear stress

As the original velocity field obtained from four-dimensional (4D) flow magnetic resonance imaging (MRI) contains considerable amount of noises and errors, the available Divergence-free smoothing (DFS) method can be used to process the 4D flow MRI data for reducing noises, eliminating errors, fixing missing data and eventually providing the smoothed flow field. However, the traditional DFS does not have the ability to deal with the flow in the near wall region of vessel, especially for satisfying the no-slip boundary condition. In this study, therefore, an improved DFS method with specific near wall treatment is introduced for processing with 4D flow MRI inner flow with curved wall boundary as the blood flows. On the other hand, due to the coarse resolution of 4D flow MRI, velocity gradients in the near wall region are normally underestimated. As a result, a special wall function is required for accurately computing wall shear stress (WSS).

cs.CE↗

arXiv2019

ICD Coding from Clinical Text Using Multi-Filter Residual Convolutional Neural Network

Automated ICD coding, which assigns the International Classification of Disease codes to patient visits, has attracted much research attention since it can save time and labor for billing. The previous state-of-the-art model utilized one convolutional layer to build document representations for predicting ICD codes. However, the lengths and grammar of text fragments, which are closely related to ICD coding, vary a lot in different documents. Therefore, a flat and fixed-length convolutional architecture may not be capable of learning good document representations. In this paper, we proposed a Multi-Filter Residual Convolutional Neural Network (MultiResCNN) for ICD coding. The innovations of our model are two-folds: it utilizes a multi-filter convolutional layer to capture various text patterns with different lengths and a residual convolutional layer to enlarge the receptive field. We evaluated the effectiveness of our model on the widely-used MIMIC dataset. On the full code set of MIMIC-III, our model outperformed the state-of-the-art model in 4 out of 6 evaluation metrics. On the top-50 code set of MIMIC-III and the full code set of MIMIC-II, our model outperformed all the existing and state-of-the-art models in all evaluation metrics. The code is available at https://github.com/foxlf823/Multi-Filter-Residual-Convolutional-Neural-Network.

cs.CL↗

arXiv2019

Mapping Observations of complex organic molecules around Sagittarius B2 with ARO 12m telescope

We performed high-sensitivity mapping observations of several complex organic molecules around Sagittarius B2 with ARO 12m telescope at 3-mm wavelength. Based on their spatial distribution, molecules can be classified as either "extended" that detected not only in Sgr B2(N) and Sgr B2(M), or "compact" that only detected toward or near to Sgr B2(N) and Sgr B2(M). The "extended" molecules including glycolaldehyde (CH2OHCHO), methyl formate (CH3OCHO), formic acid (t-HCOOH), ethanol (C2H5OH) and methyl amine (CH3NH2), while the "compact" molecules including dimethyl ether (CH3OCH3), ethyl cyanide (C2H5CN), and amino acetonitrile (H2NCH2CN). These "compact" molecules are likely produced under strong UV radiation, while "extended" molecules are likely formed under low-temperature, via gas-phase or grain surface reactions. The spatial distribution of "warm" CH2OHCHO at 89 GHz differ from the spatial distribution of "cold" CH2OHCHO observed at 13 GHz. We found evidence for an overabundance of CH2OHCHO compared to that expected from the gas-phase model, which indicates that grain-surface reactions are necessary to explain the origin of CH2OHCHO in Sagittarius B2. Grain-surface reactions are also needed to explain the correlation between the abundances of "cold" CH2OHCHO and C2H5OH. These results demonstrate the importance of grain-surface chemistry in the production of complex organic molecules.

astro-ph.SR↗

arXiv2019

The evolution of Information entropy components in Relativistic Heavy-Ion Collisions

Shannon information entropy provides an effective tool to study the evolution process in relativistic heavy-ion collisions. The time evolution process of thermodynamic entropy $S_{\rm thermal}$, multiple entropy $S_{\rm mul}$, and configuration entropy $S_{\rm conf}$ at RHIC is studied using the AMPT model to generate central Au-Au collisions. By superimposing the three kinds of information entropy, we can get a more complete information entropy of the system to describe the physical information of the relativistic heavy ion collision. The results show that the four stages of the time evolution process of the system entropy $S$ seem to correspond to the four physical processes in the relativistic heavy ion collision, indicating that the total entropy of the system can reflect the physical information in the relativistic heavy ion collision more accurately.

hep-ph↗

arXiv2019

On numerical errors to the fields surrounding a relativistically moving particle in PIC codes

The particle-in-cell (PIC) method is widely used to model the self-consistent interaction between discrete particles and electromagnetic fields. It has been successfully applied to problems across plasma physics including plasma based acceleration, inertial confinement fusion, magnetically confined fusion, space physics, astrophysics, high energy density plasmas. In many cases the physics involves how relativistic particles are generated and interact with plasmas. However, when relativistic particles stream across the grid both in vacuum and in plasma there are many numerical issues that may arise which can lead to incorrect physics. We present a detailed analysis of how discretized Maxwell solvers used in PIC codes can lead to numerical errors to the fields that surround particles that move at relativistic speeds across the grid. Expressions for the axial electric field as integrals in k space are presented. Two types of errors to these expressions are identified. The first arises from errors to the numerator of the integrand and leads to unphysical fields that are antisymmetric about the particle. The second arises from errors to the denominator of the integrand and lead to Cerenkov like radiation in "vacuum". These fields are not anti-symmetric, extend behind the particle, and cause the particle to accelerate or decelerate depending on the solver and parameters. The unphysical fields are studied in detail for two representative solvers - the Yee solver and the FFT based solver. A solution for eliminating these unphysical fields by modifying the k operator in the axial direction is also presented. Using a customized finite difference solver, this solution was successfully implemented into OSIRIS. Results from the customized solver are also presented. This solution will be useful for a beam of particles that all move in one direction with a small angular divergence.

physics.plasm-ph↗

arXiv2019

Optimal Delivery with Budget Constraint in E-Commerce Advertising

Online advertising in E-commerce platforms provides sellers an opportunity to achieve potential audiences with different target goals. Ad serving systems (like display and search advertising systems) that assign ads to pages should satisfy objectives such as plenty of audience for branding advertisers, clicks or conversions for performance-based advertisers, at the same time try to maximize overall revenue of the platform. In this paper, we propose an approach based on linear programming subjects to constraints in order to optimize the revenue and improve different performance goals simultaneously. We have validated our algorithm by implementing an offline simulation system in Alibaba E-commerce platform and running the auctions from online requests which takes system performance, ranking and pricing schemas into account. We have also compared our algorithm with related work, and the results show that our algorithm can effectively improve campaign performance and revenue of the platform.

cs.LG↗

arXiv2019

REFUGE Challenge: A Unified Framework for Evaluating Automated Methods for Glaucoma Assessment from Fundus Photographs

Glaucoma is one of the leading causes of irreversible but preventable blindness in working age populations. Color fundus photography (CFP) is the most cost-effective imaging modality to screen for retinal disorders. However, its application to glaucoma has been limited to the computation of a few related biomarkers such as the vertical cup-to-disc ratio. Deep learning approaches, although widely applied for medical image analysis, have not been extensively used for glaucoma assessment due to the limited size of the available data sets. Furthermore, the lack of a standardize benchmark strategy makes difficult to compare existing methods in a uniform way. In order to overcome these issues we set up the Retinal Fundus Glaucoma Challenge, REFUGE (\url{https://refuge.grand-challenge.org}), held in conjunction with MICCAI 2018. The challenge consisted of two primary tasks, namely optic disc/cup segmentation and glaucoma classification. As part of REFUGE, we have publicly released a data set of 1200 fundus images with ground truth segmentations and clinical glaucoma labels, currently the largest existing one. We have also built an evaluation framework to ease and ensure fairness in the comparison of different models, encouraging the development of novel techniques in the field. 12 teams qualified and participated in the online challenge. This paper summarizes their methods and analyzes their corresponding results. In particular, we observed that two of the top-ranked teams outperformed two human experts in the glaucoma classification task. Furthermore, the segmentation results were in general consistent with the ground truth annotations, with complementary outcomes that can be further exploited by ensembling the results.

cs.CV↗

arXiv2019

Eliminating Shadow Artifacts via Generative Inpainting Networks to Quantify Vascular Changes of the Choroid

Shadow artifacts from retinal vessels hinder the development of quantitative choroidal biomarkers in Optical Coherence Tomography (OCT), which limits the clinical applications of the choroidal OCT to the measures of layer thickness. In this paper, we present a novel framework that could eliminate the retinal shadows and realize the quantification of choroidal vasculature. Different from existing methods, we convert the shadow elimination into an object removal task, which can be handled by image inpainting techniques. We adopt and finetune a two-stage generative inpainting network which connects the edges of the vessels ahead of refilling the shadow-contaminated areas. It shows surpassing performance in repairing the choroidal vasculature compared with traditional inpainting algorithms. We further verify the feasibility of the proposed frame using a prospective observational study, which detects the choroidal vascular changes related to the Intra-Ocular Pressure (IOP) elevation of 34 healthy volunteers. As the average IOP increases from $15.84\pm1.99$ mmHg to $34.48\pm5.35$ mmHg, we achieve the decreases of the choroidal vessel density and flow index from $0.491\pm0.020$ to $0.463\pm0.019$ and from $0.336\pm0.025$ to $0.300\pm0.019$, respectively.

physics.med-ph↗

arXiv2019

Generating high quality ultra-relativistic electron beams using an evolving electron beam driver

A new method of controllable injection to generate high quality electron bunches in the nonlinear blowout regime driven by electron beams is proposed and demonstrated using particle-in-cell simulations. Injection is facilitated by decreasing the wake phase velocity through varying the spot size of the drive beam and can be tuned through the Courant-Snyder (CS) parameters. Two regimes are examined. In the first, the spot size is focused according to the vacuum CS beta function, while in the second, it is focused by the plasma ion column. The effects of the driver intensity and vacuum CS parameters on the wake velocity and injected beam parameters are examined via theory and simulations. For plasma densities of $\sim 10^{19} ~\text{cm}^{-3}$, particle-in-cell (PIC) simulations demonstrate that peak normalized brightnesses $\gtrsim 10^{20}~\text{A}/\text{m}^2/\text{rad}^2$ can be obtained with projected energy spreads of $\lesssim 1\%$ within the middle section of the injected beam, and with normalized slice emittances as low as $\sim 10 ~\text{nm}$.

physics.plasm-ph↗

arXiv2019

An inexact proximal augmented Lagrangian framework with arbitrary linearly convergent inner solver for composite convex optimization

We propose an inexact proximal augmented Lagrangian framework with explicit inner problem termination rule for composite convex optimization problems. We consider arbitrary linearly convergent inner solver including in particular stochastic algorithms, making the resulting framework more scalable facing the ever-increasing problem dimension. Each subproblem is solved inexactly with an explicit and self-adaptive stopping criterion, without requiring to set an a priori target accuracy. When the primal and dual domain are bounded, our method achieves $O(1/\sqrtε)$ and $O(1/ε)$ complexity bound in terms of number of inner solver iterations, respectively for the strongly convex and non-strongly convex case. Without the boundedness assumption, only logarithm terms need to be added and the above two complexity bounds increase respectively to $\tilde O(1/\sqrtε)$ and $\tilde O(1/ε)$, which hold both for obtaining $ε$-optimal and $ε$-KKT solution. Within the general framework that we propose, we also obtain $\tilde O(1/ε)$ and $\tilde O(1/{ε^2})$ complexity bounds under relative smoothness assumption on the differentiable component of the objective function. We show through theoretical analysis as well as numerical experiments the computational speedup possibly achieved by the use of randomized inner solvers for large-scale problems.

math.OC↗

arXiv2019

Formation of Massive Protostellar Clusters -- Observations of Massive 70 $μ$m Dark Molecular Clouds

We present Submillimeter Array (SMA) observations of seven massive molecular clumps which are dark in the far-infrared for wavelengths up to 70 $μ$m. Our 1.3 mm continuum images reveal 44 dense cores, with gas masses ranging from 1.4 to 77.1 M$_{\odot}$. Twenty-nine dense cores have masses greater than 8 M$_{\odot}$ and the other fifteen dense cores have masses between 1.4 and 7.5 M$_{\odot}$. Assuming the core density follows a power-law in radius $ρ\propto r^{-b}$, the index $b$ is found to be between 0.6 and 2.1 with a mean value of 1.3. The virial analysis reveals that the dense cores are not in virial equilibrium. CO outflow emission was detected toward 6 out of 7 molecular clumps and associated with 17 dense cores. For five of these cores, CO emissions appear to have line-wings at velocities of greater than 30 km s$^{-1}$ with respect to the source systemic velocity, which indicates that most of the clumps harbor protostars and thus are not quiescent in star formation. The estimated outflow timescale increase with core mass, which likely indicates that massive cores have longer accretion timescale than that of the less massive ones. The fragmentation analysis shows that the mass of low-mass and massive cores are roughly consistent with thermal and turbulent Jeans masses, respectively.

astro-ph.GA↗

arXiv2019

Generating Classical Chinese Poems from Vernacular Chinese

Classical Chinese poetry is a jewel in the treasure house of Chinese culture. Previous poem generation models only allow users to employ keywords to interfere the meaning of generated poems, leaving the dominion of generation to the model. In this paper, we propose a novel task of generating classical Chinese poems from vernacular, which allows users to have more control over the semantic of generated poems. We adapt the approach of unsupervised machine translation (UMT) to our task. We use segmentation-based padding and reinforcement learning to address under-translation and over-translation respectively. According to experiments, our approach significantly improve the perplexity and BLEU compared with typical UMT models. Furthermore, we explored guidelines on how to write the input vernacular to generate better poems. Human evaluation showed our approach can generate high-quality poems which are comparable to amateur poems.

cs.CL↗

arXiv2019

Learning to Find Correlated Features by Maximizing Information Flow in Convolutional Neural Networks

Training convolutional neural networks for image classification tasks usually causes information loss. Although most of the time the information lost is redundant with respect to the target task, there are still cases where discriminative information is also discarded. For example, if the samples that belong to the same category have multiple correlated features, the model may only learn a subset of the features and ignore the rest. This may not be a problem unless the classification in the test set highly depends on the ignored features. We argue that the discard of the correlated discriminative information is partially caused by the fact that the minimization of the classification loss doesn't ensure to learn the overall discriminative information but only the most discriminative information. To address this problem, we propose an information flow maximization (IFM) loss as a regularization term to find the discriminative correlated features. With less information loss the classifier can make predictions based on more informative features. We validate our method on the shiftedMNIST dataset and show the effectiveness of IFM loss in learning representative and discriminative features.

cs.CV↗