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Rong Ma

Publications and source records attributed to Rong Ma.

At least 55 records · Page 3Linked to original sources
arXiv2022

Deterministic distribution of orbital angular momentum multiplexed continuous-variable entanglement and quantum steering

Orbital angular momentum (OAM) multiplexing provides an efficient method to improve data-carrying capacity in various quantum communication protocols. It is a precondition to distribute OAM multiplexed quantum resources in quantum channels for implementing quantum communication. However, quantum steering of OAM multiplexed optical fields and the effect of channel noise on OAM multiplexed quantum resources remain unclear. Here, we generate OAM multiplexed continuous-variable (CV) entangled states and distribute them in lossy or noisy channels. We show that the decoherence property of entanglement and quantum steering of the OAM multiplexed states carrying topological charges $l=1$ and $l=2$ are the same as that of the Gaussian mode with $l=0$ in lossy and noisy channels. The sudden death of entanglement and quantum steering of high-order OAM multiplexed states is observed in the presence of excess noise. Our results demonstrate the feasibility to realize high data-carrying capacity quantum information processing by utilizing OAM multiplexed CV entangled states.

quant-ph↗
arXiv2022

CFP-SLAM: A Real-time Visual SLAM Based on Coarse-to-Fine Probability in Dynamic Environments

The dynamic factors in the environment will lead to the decline of camera localization accuracy due to the violation of the static environment assumption of SLAM algorithm. Recently, some related works generally use the combination of semantic constraints and geometric constraints to deal with dynamic objects, but problems can still be raised, such as poor real-time performance, easy to treat people as rigid bodies, and poor performance in low dynamic scenes. In this paper, a dynamic scene-oriented visual SLAM algorithm based on object detection and coarse-to-fine static probability named CFP-SLAM is proposed. The algorithm combines semantic constraints and geometric constraints to calculate the static probability of objects, keypoints and map points, and takes them as weights to participate in camera pose estimation. Extensive evaluations show that our approach can achieve almost the best results in high dynamic and low dynamic scenarios compared to the state-of-the-art dynamic SLAM methods, and shows quite high real-time ability.

cs.RO↗
arXiv2021

On the primes in floor function sets

Let [t] be the integral part of the real number t and let 1 P be the characteristic function of the primes. Denote by $π$ G (x) the number of primes in the floor function set G(x) := {[ x n ] : 1 n x} and by S 1 P (x) the number of primes in the sequence {[ x n ]} n 1. Very recently, Heyman proves

math.NT↗
arXiv2021

Global stability analysis and direct numerical simulation of boundary layers with an isolated roughness element

Global stability analysis and direct numerical simulation (DNS) are performed to study boundary layer flows with an isolated roughness element. Wall-attached cuboids with aspect ratios $η=1$ and $η=0.5$ are investigated for fixed ratio of roughness height to displacement boundary layer thickness $h/δ^*=2.86$. Global stability analysis is able to capture the frequency of the primary vortical structures. For $η=1$, only varicose instability is seen. For the thinner roughness element ($η=0.5$), the varicose instability dominates the sinuous instability, and the sinuous instability becomes more pronounced as $Re_h$ increases, due to increased spanwise shear in the near-wake region. The unstable modes mainly extract energy from the central streak, although the lateral streaks also contribute. The DNS results show that different instability features lead to different behavior and development of vortical structures in the nonlinear transition process. For $η=1$, the varicose mode is associated with the shedding of hairpin vortices. As $Re_h$ increases, the breakdown of hairpin vortices occurs closer to the roughness and sinuous breakdown behavior promoting transition to turbulence is seen in the farther wake. A fully-developed turbulent flow is established in both the inner and outer layers farther downstream when $Re_h$ is sufficiently high. For $η=0.5$, the sinuous wiggling of hairpin vortices is prominent at higher $Re_h$, leading to stronger interactions in the near wake, as a result of combined varicose and sinuous instabilities. A sinuous mode captured by dynamic mode decomposition (DMD) analysis, and associated with the `wiggling' of streaks persists far downstream.

physics.flu-dyn↗
arXiv2021

On the new identities of Dirichlet $L$-functions

Let $q\ge3$ be an integer, $χ$ be a Dirichlet character modulo $q$, and $L(s,χ)$ denote the Dirichlet $L$-functions corresponding to $χ$. In this paper, we show some special function series, and give some new identities for the Dirichlet $L$-functions involving Gauss sums. Specially, we give specific identities for $L(2,χ)$.

math.NT↗
arXiv2021

On the difference between a D. H. Lehmer number and its inverse over short interval

Let $q>2$ be an odd integer. For each integer $x$ with $0<x<q$ and $(q,x)= 1$, we know that there exists one and only one $\bar{x}$ with $0<\bar{x}<q$ such that $x\bar{x}\equiv1(\bmod q)$. A Lehmer number is defined to be any integer $a$ with $2\dagger(a+\bar{a})$. For any nonnegative integer $k$, Let $$ M(x,q,k)=\displaystyle\mathop {\displaystyle\mathop{\sum{'}}_{a=1}^{q} \displaystyle\mathop{\sum{'}}_{b\leq xq}}_{\mbox{$\tiny\begin{array}{c} 2|a+b+1\\ ab\equiv1(\bmod q)\end{array}$}}(a-b)^{2k}.$$ The main purpose of this paper is to study the properties of $M(x,q,k)$, and give a sharp asymptotic formula, by using estimates of Kloosterman's sums and properties of trigonometric sums.

math.NT↗
arXiv2020

Global and Simultaneous Hypothesis Testing for High-Dimensional Logistic Regression Models

High-dimensional logistic regression is widely used in analyzing data with binary outcomes. In this paper, global testing and large-scale multiple testing for the regression coefficients are considered in both single- and two-regression settings. A test statistic for testing the global null hypothesis is constructed using a generalized low-dimensional projection for bias correction and its asymptotic null distribution is derived. A lower bound for the global testing is established, which shows that the proposed test is asymptotically minimax optimal over some sparsity range. For testing the individual coefficients simultaneously, multiple testing procedures are proposed and shown to control the false discovery rate (FDR) and falsely discovered variables (FDV) asymptotically. Simulation studies are carried out to examine the numerical performance of the proposed tests and their superiority over existing methods. The testing procedures are also illustrated by analyzing a data set of a metabolomics study that investigates the association between fecal metabolites and pediatric Crohn's disease and the effects of treatment on such associations.

stat.ME↗
arXiv2020

Optimal Structured Principal Subspace Estimation: Metric Entropy and Minimax Rates

Driven by a wide range of applications, many principal subspace estimation problems have been studied individually under different structural constraints. This paper presents a unified framework for the statistical analysis of a general structured principal subspace estimation problem which includes as special cases non-negative PCA/SVD, sparse PCA/SVD, subspace constrained PCA/SVD, and spectral clustering. General minimax lower and upper bounds are established to characterize the interplay between the information-geometric complexity of the structural set for the principal subspaces, the signal-to-noise ratio (SNR), and the dimensionality. The results yield interesting phase transition phenomena concerning the rates of convergence as a function of the SNRs and the fundamental limit for consistent estimation. Applying the general results to the specific settings yields the minimax rates of convergence for those problems, including the previous unknown optimal rates for non-negative PCA/SVD, sparse SVD and subspace constrained PCA/SVD.

math.ST↗
arXiv2020

Estimation, Confidence Intervals, and Large-Scale Hypotheses Testing for High-Dimensional Mixed Linear Regression

This paper studies the high-dimensional mixed linear regression (MLR) where the output variable comes from one of the two linear regression models with an unknown mixing proportion and an unknown covariance structure of the random covariates. Building upon a high-dimensional EM algorithm, we propose an iterative procedure for estimating the two regression vectors and establish their rates of convergence. Based on the iterative estimators, we further construct debiased estimators and establish their asymptotic normality. For individual coordinates, confidence intervals centered at the debiased estimators are constructed. Furthermore, a large-scale multiple testing procedure is proposed for testing the regression coefficients and is shown to control the false discovery rate (FDR) asymptotically. Simulation studies are carried out to examine the numerical performance of the proposed methods and their superiority over existing methods. The proposed methods are further illustrated through an analysis of a dataset of multiplex image cytometry, which investigates the interaction networks among the cellular phenotypes that include the expression levels of 20 epitopes or combinations of markers.

stat.ME↗
arXiv2020

Optimal Estimation of Simultaneous Signals Using Absolute Inner Product with Applications to Integrative Genomics

Integrating the summary statistics from genome-wide association study (\textsc{gwas}) and expression quantitative trait loci (e\textsc{qtl}) data provides a powerful way of identifying the genes whose expression levels are potentially associated with complex diseases. A parameter called $T$-score that quantifies the genetic overlap between a gene and the disease phenotype based on the summary statistics is introduced based on the mean values of two Gaussian sequences. Specifically, given two independent samples $\mathbf{x}_n\sim N(θ, Σ_1)$ and $\mathbf{y}_n\sim N(μ, Σ_2)$, the $T$-score is defined as $\sum_{i=1}^n |θ_iμ_i|$, a non-smooth functional, which characterizes the amount of shared signals between two absolute normal mean vectors $|θ|$ and $|μ|$. Using approximation theory, estimators are constructed and shown to be minimax rate-optimal and adaptive over various parameter spaces. Simulation studies demonstrate the superiority of the proposed estimators over existing methods. The method is applied to an integrative analysis of heart failure genomics datasets and we identify several genes and biological pathways that are potentially causal to human heart failure.

stat.ME↗
arXiv2020

Optimal Estimation of Bacterial Growth Rates Based on Permuted Monotone Matrix

Motivated by the problem of estimating the bacterial growth rates for genome assemblies from shotgun metagenomic data, we consider the permuted monotone matrix model $Y=ΘΠ+Z$, where $Y\in \mathbb{R}^{n\times p}$ is observed, $Θ\in \mathbb{R}^{n\times p}$ is an unknown approximately rank-one signal matrix with monotone rows, $Π\in \mathbb{R}^{p\times p}$ is an unknown permutation matrix, and $Z\in \mathbb{R}^{n\times p}$ is the noise matrix. This paper studies the estimation of the extreme values associated to the signal matrix $Θ$, including its first and last columns, as well as their difference. Treating these estimation problems as compound decision problems, minimax rate-optimal estimators are constructed using the spectral column sorting method. Numerical experiments through simulated and synthetic microbiome metagenomic data are presented, showing the superiority of the proposed methods over the alternatives. The methods are illustrated by comparing the growth rates of gut bacteria between inflammatory bowel disease patients and normal controls.

math.ST↗
arXiv2020

Optimal Permutation Recovery in Permuted Monotone Matrix Model

Motivated by recent research on quantifying bacterial growth dynamics based on genome assemblies, we consider a permuted monotone matrix model $Y=ΘΠ+Z$, where the rows represent different samples, the columns represent contigs in genome assemblies and the elements represent log-read counts after preprocessing steps and Guanine-Cytosine (GC) adjustment. In this model, $Θ$ is an unknown mean matrix with monotone entries for each row, $Π$ is a permutation matrix that permutes the columns of $Θ$, and $Z$ is a noise matrix. This paper studies the problem of estimation/recovery of $Π$ given the observed noisy matrix $Y$. We propose an estimator based on the best linear projection, which is shown to be minimax rate-optimal for both exact recovery, as measured by the 0-1 loss, and partial recovery, as quantified by the normalized Kendall's tau distance. Simulation studies demonstrate the superior empirical performance of the proposed estimator over alternative methods. We demonstrate the methods using a synthetic metagenomics dataset of 45 closely related bacterial species and a real metagenomic dataset to compare the bacterial growth dynamics between the responders and the non-responders of the IBD patients after 8 weeks of treatment.

math.ST↗
arXiv2020

The Asymptotic Distribution of Modularity in Weighted Signed Networks

Modularity is a popular metric for quantifying the degree of community structure within a network. The distribution of the largest eigenvalue of a network's edge weight or adjacency matrix is well studied and is frequently used as a substitute for modularity when performing statistical inference. However, we show that the largest eigenvalue and modularity are asymptotically uncorrelated, which suggests the need for inference directly on modularity itself when the network size is large. To this end, we derive the asymptotic distributions of modularity in the case where the network's edge weight matrix belongs to the Gaussian Orthogonal Ensemble, and study the statistical power of the corresponding test for community structure under some alternative model. We empirically explore universality extensions of the limiting distribution and demonstrate the accuracy of these asymptotic distributions through type I error simulations. We also compare the empirical powers of the modularity based tests with some existing methods. Our method is then used to test for the presence of community structure in two real data applications.

stat.ME↗
arXiv2020

MODMA dataset: a Multi-modal Open Dataset for Mental-disorder Analysis

According to the World Health Organization, the number of mental disorder patients, especially depression patients, has grown rapidly and become a leading contributor to the global burden of disease. However, the present common practice of depression diagnosis is based on interviews and clinical scales carried out by doctors, which is not only labor-consuming but also time-consuming. One important reason is due to the lack of physiological indicators for mental disorders. With the rising of tools such as data mining and artificial intelligence, using physiological data to explore new possible physiological indicators of mental disorder and creating new applications for mental disorder diagnosis has become a new research hot topic. However, good quality physiological data for mental disorder patients are hard to acquire. We present a multi-modal open dataset for mental-disorder analysis. The dataset includes EEG and audio data from clinically depressed patients and matching normal controls. All our patients were carefully diagnosed and selected by professional psychiatrists in hospitals. The EEG dataset includes not only data collected using traditional 128-electrodes mounted elastic cap, but also a novel wearable 3-electrode EEG collector for pervasive applications. The 128-electrodes EEG signals of 53 subjects were recorded as both in resting state and under stimulation; the 3-electrode EEG signals of 55 subjects were recorded in resting state; the audio data of 52 subjects were recorded during interviewing, reading, and picture description. We encourage other researchers in the field to use it for testing their methods of mental-disorder analysis.

cs.DL↗
arXiv2019

On the mean value of the generalized Dirichlet L-functions with the weight of the Gauss Sums

Let $q\ge3$ be an integer, $χ$ denote a Dirichlet character modulo $q$, for any real number $a\ge 0$, we define the generalized Dirichlet $L$-functions $$ L(s,χ,a)=\sum_{n=1}^{\infty}\frac{χ(n)}{(n+a)^s}, $$ where $s=σ+it$ with $σ>1$ and $t$ both real. It can be extended to all $s$ by analytic continuation. For any integer $m$, the famous Gauss sum $G(m,χ)$ is defined as follows: $$G(m,χ)=\sum_{a=1}^{q}χ(a)e\left(\frac{am}{q}\right), $$ where $e(y)=e^{2πiy}$. The main purpose of this paper is to use the analytic method to study the mean value properties of the generalized Dirichlet $L$-functions with the weight of the Gauss Sums, and obtain a sharp asymptotic formula.

math.NT↗
arXiv2019

Water harvesting from Soils by Solar-to-Heat Induced Evaporation and Capillary Water Migration

Fresh water scarcity is one of the critical challenges for global sustainable development. Several novel water resources such as passive seawater solar desalination and atmospheric water harvesting have made some progress in recent years. However, no investigation has referred to harvesting water from shallow subsurface soils, which are potential huge water reservoirs. Here, we introduce a method of solar-driven water harvesting from soils, which can provide cheap fresh water in impoverished, arid and decentralized areas. The concentrated solar energy is used to heat the soils to evaporate the soil moisture. Then vapors flow to the condenser through tubes and condense as freshwater. Sustainable water harvesting is realized by water migration due to capillary pumping effect within soils. In the laboratory condition, an experimental setup is designed and its water-harvesting ability from soils is investigated. The maximum water mass harvesting rate was 99.8 g h-1. In about 12 h, the total harvesting water could be as high as about 900 ml. The whole process is solar-driven and spontaneous without other mechanical or electrical ancillaries. The water harvesting rate under one sun energy flux (1 kW m-2) is estimated to be about 360 g h-1 with a 1 m2 solar concentrator. Our proposal provides a potential onsite and sustainable fresh water supply solution to deal with the water scarcity problem.

physics.app-ph↗
arXiv2019

Quantum beam splitter for orbital angular momentum of light: quantum correlation by four-wave mixing operated in a nonamplifying regime

Nondegenerate four-wave mixing (FWM) process based on a double-$Λ$ scheme in hot alkali metal vapor is a versatile tool in quantum state engineering, quantum imaging, and quantum precision measurements. In this Letter, we investigate the generation of quantum correlated twin beams which carry nonzero orbital angular momentums (OAMs) based on the FWM process in hot cesium vapor. The amplified probe beam and the newly generated conjugate beam in the FWM process have the same and opposite topological charge as the seed beam, respectively. We also explore the FWM process operated in a nonamplifying regime where quantum correlated twin beams carrying OAMs can still be generated. In this regime, the FWM process plays the role of quantum beam splitter for the OAM of light, that is, a device that can split a coherent light beam carrying OAM into quantum-correlated twin beams carrying OAMs. More generally, our setup can be used as a quantum beam splitter of images.

quant-ph↗
arXiv2019

On asymptotic properties of the generalized Dirichlet $L$-functions

Let $q\ge3$ be an integer, $χ$ denote a Dirichlet character modulo $q$, for any real number $a\ge 0$, we define the generalized Dirichlet $L$-functions $$ L(s,χ,a)=\sum_{n=1}^{\infty}\frac{χ(n)}{(n+a)^s}, $$ where $s=σ+it$ with $σ>1$ and $t$ both real. It can be extended to all $s$ by analytic continuation. In this paper, we study the mean value properties of the generalized Dirichlet $L$-functions, and obtain several sharp asymptotic formulae by using analytic method.

math.NT↗