arXiv subjects

Jun Yan

Publications and source records attributed to Jun Yan.

At least 217 records · Page 12Linked to original sources

arXiv2021

Assortativity measures for weighted and directed networks

Assortativity measures the tendency of a vertex in a network being connected by other vertexes with respect to some vertex-specific features. Classical assortativity coefficients are defined for unweighted and undirected networks with respect to vertex degree. We propose a class of assortativity coefficients that capture the assortative characteristics and structure of weighted and directed networks more precisely. The vertex-to-vertex strength correlation is used as an example, but the proposed measure can be applied to any pair of vertex-specific features. The effectiveness of the proposed measure is assessed through extensive simulations based on prevalent random network models in comparison with existing assortativity measures. In application World Input-Ouput Networks,the new measures reveal interesting insights that would not be obtained by using existing ones. An implementation is publicly available in a R package "wdnet".

stat.AP↗

arXiv2021

Extended calculations of energy levels, radiative properties, and lifetimes for P-like Ge XVIII

Using the multiconfiguration Dirac-Hartree-Fock (MCDHF) and the relativistic configuration interaction (RCI) methods, a consistent set of transition energies and radiative transition data for the lowest 150 states of the $3s^2 3p^3$, $3s 3p^4$, $3s^2 3p^2 3d$, $3s 3p^3 3d$, $3p^5$, and $3s^2 3p 3d^2$ configurations in P-like Ge XVIII is provided. To assess the accuracy of the MCDHF transition energies, we have also performed calculations using the many-body perturbation theory (MBPT). Comparisons are made between the present MCDHF and MBPT data sets, as well as with other available experimental and theoretical values. The resulting accurate and consistent MCDHF data set will be useful for line identification and modeling purposes. These data can be considered as a benchmark for other calculations.

physics.atom-ph↗

arXiv2020

Probing new physics with multi-vacua quantum tunnelings beyond standard model through gravitational waves

We report on a novel phenomenon of particle cosmology, which features specific cosmological phase transitions via quantum tunnelings through multiple vacua. The latter is inspired by the axiverse ideas and enables us to probe the associated new physics models through a potential observation of specific patterns in the stochastic gravitational waves background. Multiple vacua may induce the nucleation of co-existing bubbles over the phase transition epoch, hence enhancing the overall process of bubbles' nucleation. Our detailed analysis of semi-analytical and numerical solutions to the bounce equations of the path integral in three vacua case has enabled us to determine the existence of three instanton solutions. This new mechanism of cosmological phase transitions clearly predicts a possibly sizeable new source of gravitational waves, with its energy spectrum being featured with particular patterns, which could be probed by the future gravitational wave interferometers.

astro-ph.CO↗

arXiv2020

A Bayesian marked spatial point processes model for basketball shot chart

The success rate of a basketball shot may be higher at locations where a player makes more shots. For a marked spatial point process, this means that the mark and the intensity are associated. We propose a Bayesian joint model for the mark and the intensity of marked point processes, where the intensity is incorporated in the mark model as a covariate. Inferences are done with a Markov chain Monte Carlo algorithm. Two Bayesian model comparison criteria, the Deviance Information Criterion and the Logarithm of the Pseudo-Marginal Likelihood, were used to assess the model. The performances of the proposed methods were examined in extensive simulation studies. The proposed methods were applied to the shot charts of four players (Curry, Harden, Durant, and James) in the 2017--2018 regular season of the National Basketball Association to analyze their shot intensity in the field and the field goal percentage in detail. Application to the top 50 most frequent shooters in the season suggests that the field goal percentage and the shot intensity are positively associated for a majority of the players. The fitted parameters were used as inputs in a secondary analysis to cluster the players into different groups.

stat.AP↗

arXiv2020

Regularized Fingerprinting in Detection and Attribution of Climate Change with Weight Matrix Optimizing the Efficiency in Scaling Factor Estimation

The optimal fingerprinting method for detection and attribution of climate change is based on a multiple regression where each covariate has measurement error whose covariance matrix is the same as that of the regression error up to a known scale. Inferences about the regression coefficients are critical not only for making statements about detection and attribution but also for quantifying the uncertainty in important outcomes derived from detection and attribution analyses. When there is no errors-in-variables (EIV), the optimal weight matrix in estimating the regression coefficients is the precision matrix of the regression error which, in practice, is never known and has to be estimated from climate model simulations. We construct a weight matrix by inverting a nonlinear shrinkage estimate of the error covariance matrix that minimizes loss functions directly targeting the uncertainty of the resulting regression coefficient estimator. The resulting estimator of the regression coefficients is asymptotically optimal as the sample size of the climate model simulations and the matrix dimension go to infinity together with a limiting ratio. When EIVs are present, the estimator of the regression coefficients based on the proposed weight matrix is asymptotically more efficient than that based on the inverse of the existing linear shrinkage estimator of the error covariance matrix. The performance of the method is confirmed in finite sample simulation studies mimicking realistic situations in terms of the length of the confidence intervals and empirical coverage rates for the regression coefficients. An application to detection and attribution analyses of the mean temperature at different spatial scales illustrates the utility of the method.

stat.ME↗

arXiv2020

Bayesian Nonparametric Estimation for Point Processes with Spatial Homogeneity: A Spatial Analysis of NBA Shot Locations

Basketball shot location data provide valuable summary information regarding players to coaches, sports analysts, fans, statisticians, as well as players themselves. Represented by spatial points, such data are naturally analyzed with spatial point process models. We present a novel nonparametric Bayesian method for learning the underlying intensity surface built upon a combination of Dirichlet process and Markov random field. Our method has the advantage of effectively encouraging local spatial homogeneity when estimating a globally heterogeneous intensity surface. Posterior inferences are performed with an efficient Markov chain Monte Carlo (MCMC) algorithm. Simulation studies show that the inferences are accurate and that the method is superior compared to the competing methods. Application to the shot location data of $20$ representative NBA players in the 2017-2018 regular season offers interesting insights about the shooting patterns of these players. A comparison against the competing method shows that the proposed method can effectively incorporate spatial contiguity into the estimation of intensity surfaces.

stat.ME↗

arXiv2020

An Online Updating Approach for Testing the Proportional Hazards Assumption with Streams of Survival Data

The Cox model, which remains as the first choice in analyzing time-to-event data even for large datasets, relies on the proportional hazards (PH) assumption. When survival data arrive sequentially in chunks, a fast and minimally storage intensive approach to test the PH assumption is desirable. We propose an online updating approach that updates the standard test statistic as each new block of data becomes available, and greatly lightens the computational burden. Under the null hypothesis of PH, the proposed statistic is shown to have the same asymptotic distribution as the standard version computed on the entire data stream with the data blocks pooled into one dataset. In simulation studies, the test and its variant based on most recent data blocks maintain their sizes when the PH assumption holds and have substantial power to detect different violations of the PH assumption. We also show in simulation that our approach can be used successfully with "big data" that exceed a single computer's computational resources. The approach is illustrated with the survival analysis of patients with lymphoma cancer from the Surveillance, Epidemiology, and End Results Program. The proposed test promptly identified deviation from the PH assumption that was not captured by the test based on the entire data.

stat.ME↗

arXiv2020

A Method to Learn Embedding of a Probabilistic Medical Knowledge Graph: Algorithm Development

This paper proposes an algorithm named as PrTransH to learn embedding vectors from real world EMR data based medical knowledge. The unique challenge in embedding medical knowledge graph from real world EMR data is that the uncertainty of knowledge triplets blurs the border between "correct triplet" and "wrong triplet", changing the fundamental assumption of many existing algorithms. To address the challenge, some enhancements are made to existing TransH algorithm, including: 1) involve probability of medical knowledge triplet into training objective; 2) replace the margin-based ranking loss with unified loss calculation considering both valid and corrupted triplets; 3) augment training data set with medical background knowledge. Verifications on real world EMR data based medical knowledge graph prove that PrTransH outperforms TransH in link prediction task. To the best of our survey, this paper is the first one to learn and verify knowledge embedding on probabilistic knowledge graphs.

cs.AI↗

arXiv2020

Long-lived populations of momentum- and spin-indirect excitons in monolayer WSe$_2$

Monolayer transition metal dichalcogenides are a promising platform to investigate many-body interactions of excitonic complexes. In monolayer tungsten diselenide, the ground-state exciton is dark (spin-indirect), and the valley degeneracy allows low-energy dark momentum-indirect excitons to form. Interactions between the dark exciton species and the optically accessible bright exciton (X) are likely to play significant roles in determining the optical properties of X at high power, as well as limiting the ultimate exciton densities that can be achieved, yet so far little is known about these interactions. Here, we demonstrate long-lived dense populations of momentum-indirect intervalley ($X_K$) and spin-indirect intravalley (D) dark excitons by time-resolved photoluminescence measurements (Tr-PL). Our results uncover an efficient inter-state conversion between X to D excitons through the spin-flip process and the one between D and $X_K$ excitons mediated by the exchange interaction (D + D to $X_K$ + $X_K$). Moreover, we observe a persistent redshift of the X exciton due to strong excitonic screening by $X_K$ exciton with a response time in the timescale of sub-ns, revealing a non-trivial inter-state exciton-exciton interaction. Our results provide a new insight into the interaction between bright and dark excitons, and point to a possibility to employ dark excitons for investigating exciton condensation and the valleytronics.

cond-mat.mes-hall↗

arXiv2020

Scalable Multi-Hop Relational Reasoning for Knowledge-Aware Question Answering

Existing work on augmenting question answering (QA) models with external knowledge (e.g., knowledge graphs) either struggle to model multi-hop relations efficiently, or lack transparency into the model's prediction rationale. In this paper, we propose a novel knowledge-aware approach that equips pre-trained language models (PTLMs) with a multi-hop relational reasoning module, named multi-hop graph relation network (MHGRN). It performs multi-hop, multi-relational reasoning over subgraphs extracted from external knowledge graphs. The proposed reasoning module unifies path-based reasoning methods and graph neural networks to achieve better interpretability and scalability. We also empirically show its effectiveness and scalability on CommonsenseQA and OpenbookQA datasets, and interpret its behaviors with case studies.

cs.CL↗

arXiv2020

Moving-Resting Process with Measurement Error in Animal Movement Modeling

Statistical modeling of animal movement is of critical importance. The continuous trajectory of an animal's movements is only observed at discrete, often irregularly spaced time points. Most existing models cannot handle the unequal sampling interval naturally and/or do not allow inactivity periods such as resting or sleeping. The recently proposed moving-resting (MR) model is a Brownian motion governed by a telegraph process, which allows periods of inactivity in one state of the telegraph process. The MR model shows promise in modeling the movements of predators with long inactive periods such as many felids, but the lack of accommodation of measurement errors seriously prohibits its application in practice. Here we incorporate measurement errors in the MR model and derive basic properties of the model. Inferences are based on a composite likelihood using the Markov property of the chain composed by every other observed increments. The performance of the method is validated in finite sample simulation studies. Application to the movement data of a mountain lion in Wyoming illustrates the utility of the method.

stat.ME↗

arXiv2020

Remarks on the distribution of colors in Gallai colorings

A Gallai coloring of a complete graph $K_n$ is an edge coloring without triangles colored with three different colors. A sequence $e_1\ge \dots \ge e_k$ of positive integers is an $(n,k)$-sequence if $\sum_{i=1}^k e_i=\binom{n}{2}$. An $(n,k)$-sequence is a G-sequence if there is a Gallai coloring of $K_n$ with $k$ colors such that there are $e_i$ edges of color $i$ for all $i,1\le i \le k$. Gyárfás, Pálvölgyi, Patkós and Wales proved that for any integer $k\ge 3$ there exists an integer $g(k)$ such that every $(n,k)$-sequence is a G-sequence if and only if $n\ge g(k)$. They showed that $g(3)=5, g(4)=8$ and $2k-2\le g(k)\le 8k^2+1$. We show that $g(5)=10$ and give almost matching lower and upper bounds for $g(k)$ by showing that with suitable constants $α,β>0$, $\frac{αk^{1.5}}{\ln k}\le g(k) \le βk^{1.5}$ for all sufficiently large $k$.

math.CO↗

arXiv2020

Global dynamics of contact Hamiltonian systems (I): monotone systems

This article is devoted to a description of the dynamics of the phase flow of monotone contact Hamiltonian systems. Particular attention is paid to locating the maximal attractor (or repeller), which could be seen as the union of compact invariant sets, and investigating its topological and dynamical properties. This is based on an analysis from the viewpoint of gradient-like systems.

math.DS↗

arXiv2020

Orbital angular momentum beam generation using a free-electron laser oscillator

With wavelength tunability, free-electron lasers (FELs) are well-suited for generating orbital angular momentum (OAM) beams in a wide photon energy range. We report the first experimental demonstration of OAM beam generation using an oscillator FEL. Lasing around 458 nm, we have produced the four lowest orders of coherently mixed OAM beams with good beam quality, excellent stability, and substantial intracavity power. We have also developed a pulsed mode operation of the OAM beam with a highly reproducible temporal structure for a range of modulation frequencies from 1 to 30 Hz. This development can be extended to short wavelengths, for example to x-rays using a future x-ray FEL oscillator. The operation of such an OAM FEL also paves the way for the generation of OAM gamma-ray beams via Compton scattering.

physics.acc-ph↗

arXiv2020

Multivariate Distributionally Robust Convex Regression under Absolute Error Loss

This paper proposes a novel non-parametric multidimensional convex regression estimator which is designed to be robust to adversarial perturbations in the empirical measure. We minimize over convex functions the maximum (over Wasserstein perturbations of the empirical measure) of the absolute regression errors. The inner maximization is solved in closed form resulting in a regularization penalty involves the norm of the gradient. We show consistency of our estimator and a rate of convergence of order $ \widetilde{O}\left( n^{-1/d}\right) $, matching the bounds of alternative estimators based on square-loss minimization. Contrary to all of the existing results, our convergence rates hold without imposing compactness on the underlying domain and with no a priori bounds on the underlying convex function or its gradient norm.

math.ST↗

arXiv2020

Up- and Down-Conversion between Intra- and Inter-Valley Excitons in Waveguide Coupled Monolayer WSe2

The presence of two spin-split valleys in monolayer (1L) transition metal dichalcogenide (TMD) semiconductors supports versatile exciton species classified by their spin and valley quantum numbers. While the spin-0 intravalley exciton, known as the "bright" exciton, is readily observable, other types of excitons, such as the spin-1 intravalley (spin-dark) and spin-0 intervalley (momentum-dark) excitons, are more difficult to access. Here we develop a waveguide coupled 1L tungsten diselenide (WSe2) device to probe these exciton species. In particular, TM coupling to the atomic layer's out-of-plane dipole moments enabled us to not only efficiently collect, but also resonantly populate the spin-1 dark excitons, promising for developing devices with long valley lifetimes. Our work reveals several upconversion processes that bring out an intricate coupling network linking spin-0 and spin-1 intra- and inter-valley excitons, demonstrating that intervalley scattering and spin-flip are very common processes in the atomic layer. These experimental results deepen our understanding of tungsten diselenide exciton physics and illustrate that planar photonic devices are capable of harnessing versatile exciton species in TMD semiconductors.

cond-mat.mes-hall↗

arXiv2020

Weak KAM solutions of Hamilton-Jacobi equations with decreasing dependence on unknown functions

We consider the Hamilton-Jacobi equation \[{H}(x,u,Du)=0,\quad x\in M, \] where $M$ is a connected, closed and smooth Riemannian manifold, ${H}(x,u,p)$ satisfies Tonelli conditions with respect to $p$ and certain decreasing condition with respect to $u$. Based on a dynamical approach developed in \cite{WWY,WWY1,WWY2}, we obtain a series of properties for weak KAM solutions (equivalently, viscosity solutions) of the stationary equation and the long time behavior of viscosity solutions of the evolutionary equation on the Cauchy problem \begin{equation*} \begin{cases} w_t+{H}(x,w,w_x)=0,\quad (x,t)\in M\times (0,+\infty),\\ w(x,0)=φ(x), \quad x\in M. \end{cases} \end{equation*}

math.DS↗

arXiv2020

Ground and Excited Exciton Polarons in Monolayer MoSe2

Monolayer transition metal dichalcogenide semiconductors, with versatile experimentally accessible exciton species, offer an interesting platform for investigating the interaction between excitons and a Fermi sea of charges. Using hexagonal boron nitride encapsulated monolayer MoSe2, we study the impact of charge density tuning on the ground and excited Rydberg states in the atomic layer. Consistent excitonpolaron behavior is revealed in both photoluminescence and reflection spectra of the A exciton 1s (A:1s) Rydberg state, in contrast to previous studies. The A:2s Rydberg state provides an opportunity to understand such interactions with greatly reduced exciton binding energy. We found that the impact of the Fermi sea becomes much more dramatic. With a photoluminescence upconversion technique, we further verify the 2s polaron-like behavior for the repulsive branch of B:2s exciton whose energy is well above the bare bandgap. Our studies show that the polaron-like interaction features are quite generic and highly robust, offering key insights into the dressed manybody state in a Fermi sea.

cond-mat.mes-hall↗