arXiv subjects

Ping He

Publications and source records attributed to Ping He.

At least 91 records · Page 5Linked to original sources

arXiv2021

The continuous wavelet derived by smoothing function and its application in cosmology

The wavelet analysis technique is a powerful tool and is widely used in broad disciplines of engineering, technology, and sciences. In this work, we present a novel scheme of constructing continuous wavelet functions, in which the wavelet functions are obtained by taking the first derivative of smoothing functions with respect to the scale parameter. Due to this wavelet constructing scheme, the inverse transforms are only one-dimensional integrations with respect to the scale parameter, and hence the continuous wavelet transforms constructed in this way are more ready to use than the usual scheme. We then apply the Gaussian-derived wavelet constructed by our scheme to computations of the density power spectrum for dark matter, the velocity power spectrum and the kinetic energy spectrum for baryonic fluid. These computations exhibit the convenience and strength of the continuous wavelet transforms. The transforms are very easy to perform, and we believe that the simplicity of our wavelet scheme will make continuous wavelet transforms very useful in practice.

astro-ph.IM↗

arXiv2021

Analysis and classification of main risk factors causing stroke in Shanxi Province

In China, stroke is the first leading cause of death in recent years. It is a major cause of long-term physical and cognitive impairment, which bring great pressure on the National Public Health System. Evaluation of the risk of getting stroke is important for the prevention and treatment of stroke in China. A data set with 2000 hospitalized stroke patients in 2018 and 27583 residents during the year 2017 to 2020 is analyzed in this study. Due to data incompleteness, inconsistency, and non-structured formats, missing values in the raw data are filled with -1 as an abnormal class. With the cleaned features, three models on risk levels of getting stroke are built by using machine learning methods. The importance of "8+2" factors from China National Stroke Prevention Project (CSPP) is evaluated via decision tree and random forest models. Except for "8+2" factors the importance of features and SHAP1 values for lifestyle information, demographic information, and medical measurement are evaluated and ranked via a random forest model. Furthermore, a logistic regression model is applied to evaluate the probability of getting stroke for different risk levels. Based on the census data in both communities and hospitals from Shanxi Province, we investigate different risk factors of getting stroke and their ranking with interpretable machine learning models. The results show that Hypertension (Systolic blood pressure, Diastolic blood pressure), Physical Inactivity (Lack of sports), and Overweight (BMI) are ranked as the top three high-risk factors of getting stroke in Shanxi province. The probability of getting stroke for a person can also be predicted via our machine learning model.

cs.LG↗

arXiv2021

Data-Based Optimal Bandwidth for Kernel Density Estimation of Statistical Samples

It is a common practice to evaluate probability density function or matter spatial density function from statistical samples. Kernel density estimation is a frequently used method, but to select an optimal bandwidth of kernel estimation, which is completely based on data samples, is a long-term issue that has not been well settled so far. There exist analytic formulae of optimal kernel bandwidth, but they cannot be applied directly to data samples, since they depend on the unknown underlying density functions from which the samples are drawn. In this work, we devise an approach to pick out the totally data-based optimal bandwidth. First, we derive correction formulae for the analytic formulae of optimal bandwidth to compute the roughness of the sample's density function. Then substitute the correction formulae into the analytic formulae for optimal bandwidth, and through iteration, we obtain the sample's optimal bandwidth. Compared with analytic formulae, our approach gives very good results, with relative differences from the analytic formulae being only 2%-3% for a sample size larger than 10^4. This approach can also be generalized easily to cases of variable kernel estimations.

stat.ME↗

arXiv2020

Turbulence-induced deviation between baryonic field and dark matter field in the spatial distribution of the Universe

The cosmic baryonic fluid at low redshifts is similar to a fully developed turbulence. In this work, we use simulation samples produced by the hybrid cosmological hydrodynamical/N-body code, to investigate on what scale the deviation of spatial distributions between baryons and dark matter is caused by turbulence. For this purpose, we do not include the physical processes such as star formation, supernovae (SNe) and active galactic nucleus (AGN) feedback into our code, so that the effect of turbulence heating for IGM can be exhibited to the most extent. By computing cross-correlation functions $r_m(k)$ for the density field and $r_v(k)$ for the velocity field of both baryons and dark matter, we find that deviations between the two matter components for both density field and velocity field, as expected, are scale-dependent. That is, the deviations are the most significant at small scales and gradually diminish on larger and larger scales. Also, the deviations are time-dependent, i.e. they become larger and larger with increasing cosmic time. The most emphasized result is that the spatial deviations between baryons and dark matter revealed by velocity field are more significant than that by density field. At z = 0, at the 1% level of deviation, the deviation scale is about 3.7 $h^{-1}$Mpc for density field, while as large as 23 $h^{-1}$Mpc for velocity field, a scale that falls within the weakly non-linear regime for the structure formation paradigm. Our results indicate that the effect of turbulence heating is indeed comparable to that of these processes such as SN and AGN feedback.

astro-ph.CO↗

arXiv2020

Parameterizing the Energy Dissipation Rate in Stably Stratified Flows

We use a database of direct numerical simulations to evaluate parametrizations for energy dissipation rate in stably stratified flows. We show that shear-based formulations are more appropriate for stable boundary layers than commonly used buoyancy-based formulations. As part of the derivations, we explore several length scales of turbulence and investigate their dependence on local stability.

physics.ao-ph↗

arXiv2020

On the Dissipation Rate of Temperature Fluctuations in Stably Stratified Flows

In this study, we explore several integral and outer length scales of turbulence which can be formulated by using the dissipation of temperature fluctuations ($χ$) and other relevant variables. Our analyses directly lead to simple yet non-trivial parameterizations for both $χ$ and the structure parameter of temperature ($C_T^2$). For our purposes, we make use of high-fidelity data from direct numerical simulations of stratified channel flows.

physics.ao-ph↗

arXiv2019

Fine-tuning BERT for Joint Entity and Relation Extraction in Chinese Medical Text

Entity and relation extraction is the necessary step in structuring medical text. However, the feature extraction ability of the bidirectional long short term memory network in the existing model does not achieve the best effect. At the same time, the language model has achieved excellent results in more and more natural language processing tasks. In this paper, we present a focused attention model for the joint entity and relation extraction task. Our model integrates well-known BERT language model into joint learning through dynamic range attention mechanism, thus improving the feature representation ability of shared parameter layer. Experimental results on coronary angiography texts collected from Shuguang Hospital show that the F1-score of named entity recognition and relation classification tasks reach 96.89% and 88.51%, which are better than state-of-the-art methods 1.65% and 1.22%, respectively.

cs.CL↗

arXiv2019

Enriching Medcial Terminology Knowledge Bases via Pre-trained Language Model and Graph Convolutional Network

Enriching existing medical terminology knowledge bases (KBs) is an important and never-ending work for clinical research because new terminology alias may be continually added and standard terminologies may be newly renamed. In this paper, we propose a novel automatic terminology enriching approach to supplement a set of terminologies to KBs. Specifically, terminology and entity characters are first fed into pre-trained language model to obtain semantic embedding. The pre-trained model is used again to initialize the terminology and entity representations, then they are further embedded through graph convolutional network to gain structure embedding. Afterwards, both semantic and structure embeddings are combined to measure the relevancy between the terminology and the entity. Finally, the optimal alignment is achieved based on the order of relevancy between the terminology and all the entities in the KB. Experimental results on clinical indicator terminology KB, collected from 38 top-class hospitals of Shanghai Hospital Development Center, show that our proposed approach outperforms baseline methods and can effectively enrich the KB.

cs.CL↗

arXiv2018

On the Gravitational Instabilities of Protoplanetary Disks

The gravitational instabilities are important to the evolution of the disks and the planet formation in the disks. We calculate the evolution of the disks which form from the collapse of the molecular cloud cores. By changing the properties of the cloud cores and the hydrodynamical viscosity parameters, we explore their effects on the properties of the gravitational instabilities. We find that the disk is unstable when the angular velocity of the molecular cloud core is larger than a critical value. The time duration of the instability increases as the angular velocity of the core increases. The increase of the hydrodynamical viscosity parameter hardly affects the stability of the disk, but decreases the time duration of the critical state of the gravitational instability in the disk. The instability of the disks can happen at very early time of evolution of the disk, which is consistent with the observations.

astro-ph.EP↗

arXiv2018

An attention-based Bi-GRU-CapsNet model for hypernymy detection between compound entities

Named entities are usually composable and extensible. Typical examples are names of symptoms and diseases in medical areas. To distinguish these entities from general entities, we name them \textit{compound entities}. In this paper, we present an attention-based Bi-GRU-CapsNet model to detect hypernymy relationship between compound entities. Our model consists of several important components. To avoid the out-of-vocabulary problem, English words or Chinese characters in compound entities are fed into the bidirectional gated recurrent units. An attention mechanism is designed to focus on the differences between the two compound entities. Since there are some different cases in hypernymy relationship between compound entities, capsule network is finally employed to decide whether the hypernymy relationship exists or not. Experimental results demonstrate

cs.CL↗

arXiv2018

Incorporating Dictionaries into Deep Neural Networks for the Chinese Clinical Named Entity Recognition

Clinical Named Entity Recognition (CNER) aims to identify and classify clinical terms such as diseases, symptoms, treatments, exams, and body parts in electronic health records, which is a fundamental and crucial task for clinical and translational research. In recent years, deep neural networks have achieved significant success in named entity recognition and many other Natural Language Processing (NLP) tasks. Most of these algorithms are trained end to end, and can automatically learn features from large scale labeled datasets. However, these data-driven methods typically lack the capability of processing rare or unseen entities. Previous statistical methods and feature engineering practice have demonstrated that human knowledge can provide valuable information for handling rare and unseen cases. In this paper, we address the problem by incorporating dictionaries into deep neural networks for the Chinese CNER task. Two different architectures that extend the Bi-directional Long Short-Term Memory (Bi-LSTM) neural network and five different feature representation schemes are proposed to handle the task. Computational results on the CCKS-2017 Task 2 benchmark dataset show that the proposed method achieves the highly competitive performance compared with the state-of-the-art deep learning methods.

cs.CL↗

arXiv2017

Constraining the optical depth of galaxies and velocity bias with cross-correlation between kinetic Sunyaev-Zeldovich effect and peculiar velocity field

We calculate the cross-correlation function $\langle (ΔT/T)(\mathbf{v}\cdot \mathbf{n}/σ_{v}) \rangle$ between the kinetic Sunyaev-Zeldovich (kSZ) effect and the reconstructed peculiar velocity field using linear perturbation theory, to constrain the optical depth $τ$ and peculiar velocity bias of central galaxies with Planck data. We vary the optical depth $τ$ and the velocity bias function $b_{v}(k)=1+b(k/k_{0})^{n}$, and fit the model to the data, with and without varying the calibration parameter $y_{0}$ that controls the vertical shift of the correlation function. By constructing a likelihood function and constraining $τ$, $b$ and $n$ parameters, we find that the quadratic power-law model of velocity bias $b_{v}(k)=1+b(k/k_{0})^{2}$ provides the best-fit to the data. The best-fit values are $τ=(1.18 \pm 0.24) \times 10^{-4}$, $b=-0.84^{+0.16}_{-0.20}$ and $y_{0}=(12.39^{+3.65}_{-3.66})\times 10^{-9}$ ($68\%$ confidence level). The probability of $b>0$ is only $3.12 \times 10^{-8}$ for the parameter $b$, which clearly suggests a detection of scale-dependent velocity bias. The fitting results indicate that the large-scale ($k \leq 0.1\,h\,{\rm Mpc}^{-1}$) velocity bias is unity, while on small scales the bias tends to become negative. The value of $τ$ is consistent with the stellar mass--halo mass and optical depth relation proposed in the previous literatures, and the negative velocity bias on small scales is consistent with the peak background-split theory. Our method provides a direct tool to study the gaseous and kinematic properties of galaxies.

astro-ph.CO↗

arXiv2015

Constraining cosmology with pairwise velocity estimator

In this paper, we develop a full statistical method for the pairwise velocity estimator previously proposed, and apply Cosmicflows-2 catalogue to this method to constrain cosmology. We first calculate the covariance matrix for line-of-sight velocities for a given catalogue, and then simulate the mock full-sky surveys from it, and then calculate the variance for the pairwise velocity field. By applying the $8315$ independent galaxy samples and compressed $5224$ group samples from Cosmicflows-2 catalogue to this statistical method, we find that the joint constraint on $Ω^{0.6}_{\rm m}h$ and $σ_{8}$ is completely consistent with the WMAP 9-year and Planck 2015 best-fitting cosmology. Currently, there is no evidence for the modified gravity models or any dynamic dark energy models from this practice, and the error-bars need to be reduced in order to provide any concrete evidence against/to support $Λ$CDM cosmology.

astro-ph.CO↗

arXiv2014

Statistical computation of Boltzmann entropy and estimation of the optimal probability density function from statistical sample

In this work, we investigate the statistical computation of the Boltzmann entropy of statistical samples. For this purpose, we use both histogram and kernel function to estimate the probability density function of statistical samples. We find that, due to coarse-graining, the entropy is a monotonic increasing function of the bin width for histogram or bandwidth for kernel estimation, which seems to be difficult to select an optimal bin width/bandwidth for computing the entropy. Fortunately, we notice that there exists a minimum of the first derivative of entropy for both histogram and kernel estimation, and this minimum point of the first derivative asymptotically points to the optimal bin width or bandwidth. We have verified these findings by large amounts of numerical experiments. Hence, we suggest that the minimum of the first derivative of entropy be used as a selector for the optimal bin width or bandwidth of density estimation. Moreover, the optimal bandwidth selected by the minimum of the first derivative of entropy is purely data-based, independent of the unknown underlying probability density distribution, which is obviously superior to the existing estimators. Our results are not restricted to one-dimensional, but can also be extended to multivariate cases. It should be emphasized, however, that we do not provide a robust mathematical proof of these findings, and we leave these issues with those who are interested in them.

stat.ME↗

arXiv2012

Second-order solutions of the equilibrium statistical mechanics for self-gravitating systems

In a previous study, we formulated a framework of the entropy-based equilibrium statistical mechanics for self-gravitating systems. This theory is based on the Boltzmann-Gibbs entropy and includes the generalized virial equations as additional constraints. With the truncated distribution function to the lowest order, we derived a set of second-order equations for the equilibrium states of the system. In this work, the numerical solutions of these equations are investigated. It is found that there are three types of solutions for these equations. Both the isothermal and divergent solutions are thermally unstable and have unconfined density profiles with infinite mass, energy and spatial extent. The convergent solutions, however, seem to be reasonable. Although the results cannot match the simulation data well, because of the truncations of the distribution function and its moment equations, these lowest-order convergent solutions show that the density profiles of the system are confined, the velocity dispersions are variable functions of the radius, and the velocity distributions are also anisotropic in different directions. The convergent solutions also indicate that the statistical equilibrium of self-gravitating systems is by no means the thermodynamic equilibrium. These solutions are just the lowest-order approximation, but they have already manifested the qualitative success of our theory. We expect that higher-order solutions of our statistical-mechanical theory will give much better agreement with the simulation results concerning dark matter haloes.

astro-ph.CO↗

arXiv2011

Equilibrium statistical mechanics for self-gravitating systems: local ergodicity and extended Boltzmann-Gibbs/White-Narayan statistics

The long-standing puzzle surrounding the statistical mechanics of self-gravitating systems has not yet been solved successfully. We formulate a systematic theoretical framework of entropy-based statistical mechanics for spherically symmetric collisionless self-gravitating systems. We use an approach that is very different from that of the conventional statistical mechanics of short-range interaction systems. We demonstrate that the equilibrium states of self-gravitating systems consist of both mechanical and statistical equilibria, with the former characterized by a series of velocity-moment equations and the latter by statistical equilibrium equations, which should be derived from the entropy principle. The velocity-moment equations of all orders are derived from the steady-state collisionless Boltzmann equation. We point out that the ergodicity is invalid for the whole self-gravitating systems, but it can be re-established locally. Based on the local ergodicity, using Fermi-Dirac-like statistics, with the nondegenerate condition and the spatial independence of the local microstates, we rederive the Boltzmann-Gibbs entropy. This is consistent with the validity of the collisionless Boltzmann equation, and should be the correct entropy form for collisionless self-gravitating systems. Apart from the usual constraints of mass and energy conservation, we demonstrate that the series of moment or virialization equations must be included as additional constraints on the entropy functional when performing the variational calculus; this is an extension to the original prescription by White & Narayan. Any possible velocity distribution can be produced by the statistical-mechanical approach that we have developed with the extended Boltzmann-Gibbs/White-Narayan statistics. Finally, we discuss the questions of negative specific heat and ensemble inequivalence for self-gravitating systems.

astro-ph.CO↗

arXiv2011

High-energy high-luminosity electron-ion collider eRHIC

In this paper, we describe a future electron-ion collider (EIC), based on the existing Relativistic Heavy Ion Collider (RHIC) hadron facility, with two intersecting superconducting rings, each 3.8 km in circumference. A new ERL accelerator, which provide 5-30 GeV electron beam, will ensure 10^33 to 10^34 cm^-2 s^-1 level luminosity.

physics.acc-ph↗

arXiv2011

Fluid-like entropy and equilibrium statistical mechanics of self-gravitating systems

The statistical mechanics of self-gravitating systems has not been well understood, and still remains an open question so far. In a previous study by Kang & He, we showed that the fluid approximation may give a clue to further investigate this problem. In fact, there are indeed many dynamical similarities between self-gravitating and fluid systems. Based on a fluid-like entropy, that work explained successfully the outer density profiles of dark matter halos, but there left some drawbacks with the calculation concerning extremizing process of the entropy. In the current paper, with the improved extremizing calculation -- including an additional differential constraint of dynamical equilibrium and without any other assumptions, we confirm that statistical-mechanical methods can give a density profile with finite mass and finite energy. Moreover, this density profile is also consistent with the observational surface brightness of the elliptical galaxy NGC 3379. In our methods, the density profile is derived from the equation of state, which is obtained from entropy principle but does not correspond to the maximum entropy of the system. Finally, we suggest an alternative entropy form, a hybrid of Boltzmann-Gibbs and Tsallis entropy, whose global maximum may give rise to this equation of state.

astro-ph.CO↗