arXiv subjects

Yue Yang

Publications and source records attributed to Yue Yang.

At least 199 records · Page 11Linked to original sources

arXiv2020

Sex Differences in Severity and Mortality Among Patients With COVID-19: Evidence from Pooled Literature Analysis and Insights from Integrated Bioinformatic Analysis

Objective: To conduct a meta-analysis of current studies that examined sex differences in severity and mortality in patients with COVID-19, and identify potential mechanisms underpinning these differences. Methods: We performed a systematic review to collate data from observational studies examining associations of sex differences with clinical outcomes of COVID-19. PubMed, Web of Science and four preprint servers were searched for relevant studies. Data were extracted and analyzed using meta-analysis where possible, with summary data presented otherwise. Publicly available bulk RNA sequencing (RNA-seq), single-cell RNA sequencing (scRNA-seq), and chromatin immunoprecipitation sequencing (ChIP-seq) data were analyzed to explore the potential mechanisms underlying the observed association. Results: 39 studies met inclusion criteria, representing 77932 patients, of which 41510 (53.3%) were males. Men were at a markedly increased risk of developing severe cases compared with women. Furthermore, the pooled odds ratio (OR) of mortality for male group compared with the female group indicated significant higher mortality rate for male. Data from scRNA-seq suggest that men have a higher amount of ACE2-expressing pulmonary alveolar type II cells than women. Sex-based immunological differences exist. The expression of androgen receptor (AR) is positively correlated with ACE2, and there is evidence that AR may directly regulate the expression of ACE2. Conclusions: This meta-analysis detected an increased severity and mortality rate in the male populations with COVID-19, which might be attributable to the sex-based differences in cellular compositions and immunological microenvironments of the lung. The host cell receptor ACE2 is likely regulated by AR signaling pathway, which is identified as a potential target for prevention and treatment of SARS-Cov-2 infections in men.

q-bio.PE↗

arXiv2020

An Intelligent Quaternion SVDCKF AHRS Estimation with Variable Adaptive Methods in Complex Conditions

Aimed at solving the problem of Attitude and Heading Reference System(AHRS) in the complex and dynamic conditions for small-UAV, An intelligent Singular Value Decomposition Cubature Kalman Filter(SVDCKF) combined with the Variable Adaptive Methods(VAM) is proposed in this paper. Considering the nonlinearity of quaternion AHRS model and non-positive definite of the state covariance matrix, the SVDCKF algorithm is presented with both the SVD and CKF in order to better obtain the filter accuracy and reliability. Additionally, there are the different changes of the values in the accelerometer measurement resulting from the complex flying conditions. Thus, the VAM is designed to deal with three-axis values of the acceleration and tune intelligently the measurement noise matrix Ra. Moreover, the heading measurement from the three-axis values of the magnetometer is calculated according to the whether to use the three-axis values of the acceleration in the special situations. The simulation and experiment results demonstrate that the proposed filter algorithm has the more excellent attitude solution accuracy and robustness than both the Complementary Filter(CF) and the Error State Kalman Filter(ESKF).

cs.RO↗

arXiv2020

An improved nonlinear FastEuler AHRS estimation based on the SVDCKF algorithm

In this paper, we present a Singular Value Decomposition Cubature Kalman Filter(SVDCKF) fusion algorithm based on the improved nonlinear FastEuler Attitude and Heading Reference and System(AHRS) estimation model for small-UAV attitude. The contributions of this work are the derivation of the low-cost IMU/MAG integrated AHRS model combined with the quaternion attitude determination, and use the FastEuler to correct the gyroscope attitude update, which can increase the real-time solution. In addition, the SVDCKF algorithm is fused the various raw sensors data in order to improve the filter accuracy compared with the CKF. The simulation and experiment results demonstrate the proposed algorithm has the more excellent attitude solution accuracy compared with the CKF in the low and high dynamic flight conditions.

cs.RO↗

arXiv2020

An improved FastEuler-DLKF small-UAV AHRS algorithm

The accurate Attitude Heading Reference System(AHRS) is an important apart of the UAV reliable flight system. Aiming at the application scenarios of near ground navigation of small-UAV, this paper establishes a loose couple error model of the gyroscope/accelerometer/magnetometer, and presents an improved FastEuler Double-Layer Kalman Filter algorithm. Using low-cost devices which include MEMS Inertial Measurement Units(IMU) and magnetometers, this paper constructs the AHRS hardware and software systems of UAV, and designs the offline and real-time verification platforms. Moreover, the attitude changes of UAV is analyzed by the simulation and flight test, respectively. In addition, an adaptive factor is used to adjust the measurement noise covariance in order to eliminate the harmful effects of linear acceleration in the accelerometer, which is solved the roll and ptich angle. The experimental comparison with the Complementary Filter shows that the proposed algorithm can provide accurate attitude information when UAV is flying, which improves the accuracy and reliability of attitude solution, and removes the influence the gyro bias for the attitude estimation.

cs.RO↗

arXiv2020

How to Eliminate Detour Behaviors in E-hailing? Real-time Detecting and Time-dependent Pricing

With the rapid development of information and communication technology (ICT), taxi business becomes a typical electronic commerce mode. However, one traditional problem still exists in taxi service, that greedy taxi drivers may deliberately take unnecessary detours to overcharge passengers. The detection of these fraudulent behaviors is essential to ensure high-quality taxi service. In this paper, we propose a novel framework for detecting and analyzing the detour behaviors both in off-line database and among on-line trips. Applying our framework to real-world taxi data-set, a remarkable performance (AUC surpasses 0.98) has been achieved in off-line classification. Meanwhile, we further extend the off-line methods to on-line detection, a warning mechanism is introduced to remind drivers and an excellent precision (AUC surpasses 0.90) also has arrived in this phases. After conducting extensive experiments to verify the relationships between pricing regulations and detour behaviors, some quantitative pricing suggestions, including rising base fare and reducing distance-based fare rate, are provided to eliminate detour behaviors from the long term.

cs.CY↗

arXiv2019

The Strength of Ramsey's Theorem For Pairs over trees: I. Weak König's Lemma

Let $\mathsf{TT}^2_k$ denote the combinatorial principle stating that every $k$-coloring of pairs of compatible nodes in the full binary tree has a homogeneous solution, i.e. an isomorphic subtree in which all pairs of compatible nodes have the same color. Let $\mathsf{WKL}_0$ be the subsystem of second order arithmetic consisting of the base system $\mathsf{RCA}_0$ together with the principle (called Weak König's Lemma) stating that every infinite subtree of the full binary tree has an infinite path. We show that over $\mathsf{RCA}_0$, $\mathsf{TT}^2_k$ doe not imply $\mathsf{WKL}_0$. This solves the open problem on the relative strength between the two major subsystems of second order arithmetic.

math.LO↗

arXiv2019

Where Pigeonhole Principles meet König Lemmas

We study the pigeonhole principle for $Σ_2$-definable injections with domain twice as large as the codomain, and the weak König lemma for $Δ^0_2$-definable trees in which every level has at least half of the possible nodes. We show that the latter implies the existence of $2$-random reals, and is conservative over the former. We also show that the former is strictly weaker than the usual pigeonhole principle for $Σ_2$-definable injections.

math.LO↗

arXiv2019

Modeling pressure effects on the turbulent burning velocity for lean hydrogen/air premixed combustion

We investigate and model pressure effects on the turbulent burning velocity over a wide range of pressures and turbulence intensities with the direct numerical simulation (DNS) of statistically planar turbulent premixed flames for lean hydrogen/air mixture. DNS results indicate that the stretch factor has an impact on the turbulent burning velocity and flame surface area at elevated pressures. In particular, the enhanced stretch factor at high pressures increases the ratio of turbulent and laminar burning velocities, diminishing the "bending" effect. Based on a good consistency between turbulent and laminar burning velocities with respect to flame stretch, a lookup table formed by laminar flame data is employed to model the stretch factor in turbulent flames at various pressures. A predictive model for the turbulent burning velocity is then developed by combining sub models of the stretch factor and flame surface area. The overall good agreement between model predictions and DNS results demonstrates that the proposed model is able to quantitatively predict the turbulent burning velocity over a wide range of pressures and turbulent intensities in homogeneous isotropic turbulence.

physics.flu-dyn↗

arXiv2019

Modelling of the turbulent burning velocity based on Lagrangian statistics of propagating surfaces

We propose a predictive model of the turbulent burning velocity $S_T$ in homogeneous isotropic turbulence (HIT) based on Lagrangian statistics of propagating surfaces. The propagating surfaces with a constant displacement speed are initially arranged on a plane, and they evolve in non-reacting HIT, behaving like the propagation of a planar premixed flame front. The universal constants in the model of $S_T$ characterize the enhancement of area growth of premixed flames by turbulence, and they are determined by Lagrangian statistics of propagating surfaces. The flame area is then modelled by the area of propagating surfaces at a truncation time. This truncation time signals the statistical stationary state of the evolutionary geometry of propagating surfaces, and it is modelled by an explicit expression using limiting conditions of very weak and strong turbulence. Another parameter in the model of $S_T$ characterizes the effect of fuel chemistry on $S_T$, and it is pre-determined by very few available data points of $S_T$ from experiments or direct numerical simulation (DNS) in weak turbulence. The proposed model is validated using three DNS series of turbulent premixed flames with various fuels. The model prediction of $S_T$ generally agrees well with DNS in a wide range of premixed combustion regimes, and it captures the basic trends of $S_T$ in terms of the turbulence intensity, including the linear growth in weak turbulence and the `bending effect' in strong turbulence.

physics.flu-dyn↗

arXiv2019

Bulk Material Based Selective Infrared Emitter for Sub-Ambient Daytime Radiative Cooling

Through passively emitting excess heat to the outer space, radiative cooling has been demonstrated as an efficient way for energy saving applications. Selective surface with unity emittance only within the atmospheric window as well as zero absorption within the solar spectrum is sought to achieve the best sub-ambient radiative cooling performance during the daytime. In this work, we proposed a bulk radiative cooler consisting of a 1-mm-thick lithium fluoride crystal coated with silver backing, which exhibits a solar absorptance of 4.7% and nearly ideal infrared selectivity with high emission exactly within the atmospheric transmission band (i.e., 8-13 um). Excellent daytime cooling performance was demonstrated in an outdoor test with stagnation temperature below the ambient temperature by 5 K under solar irradiance above 900 W/m2 and a net cooling power of about 60 W/m2 when the cooler is in thermal equilibrium with the ambient. As a bulk material with the highest ultraviolet-visible-near infrared transmittance, lithium fluoride crystal has been widely employed as the optical windows and mirrors in various applications. The proposed simple selective infrared emitter based on lithium fluoride would open up an innovative way to radiatively cool optical systems.

physics.app-ph↗

arXiv2019

Chip-based photonic radar for high-resolution imaging

Radar is the only sensor that can realize target imaging at all time and all weather, which would be a key technical enabler for future intelligent society. Poor resolution and large size are two critical issues for radars to gain ground in civil applications. Conventional electronic radars are difficult to address both issues especially in the relatively low-frequency band. In this work, we propose and experimentally demonstrate, for the first time to the best of our knowledge, a chip-based photonic radar based on silicon photonic platform, which can implement high resolution imaging with very small footprint. Both the wideband signal generator and the de-chirp receiver are integrated on the chip. A broadband photonic imaging radar occupying the full Ku band is experimentally established. A high precision range measurement with a resolution of 2.7 cm and an error of less than 2.75 mm is obtained. Inverse synthetic aperture (ISAR) imaging of multiple targets with complex profiles are also implemented.

eess.SP↗

arXiv2019

Analytical Solution for Stochastic Unit Commitment Considering Wind Power Uncertainty with Gaussian Mixture Model

To capture the stochastic characteristics of renewable energy generation output, the chance-constrained unit commitment (CCUC) model is widely used. Conventionally, analytical solution for CCUC is usually based on simplified probability assumption or neglecting some operational constraints, otherwise scenar-io-based methods are used to approximate probability with heavy computation burden. In this paper, Gaussian mixture model (GMM) is employed to characterize the correlation between wind farms and probability distribution of their forecast errors. In our model, chance constraints including reserve sufficiency and branch power flow bounds are ensured to be satisfied with pre-determined probability. To solve this CCUC problem, we propose a Newton method based procedure to acquire the quantiles and transform chance constraints into deterministic constraints. Therefore, the CCUC model is efficiently solved as a mixed-integer quadratic programming problem. Numerical tests are performed on several systems to illustrate efficiency and scalability of the proposed method.

math.OC↗

arXiv2019

Stochastic Real-time Power Dispatch with Large-scale Wind Power Integration and its Analytical solution

Real-time power dispatch (RTD) can coordinate wind farms, automatic generation control (AGC) units and non AGC units. In RTD, the probable wind power forecast errors (WPFE) should be appropriately formulated to ensure system security with high probability and minimize operational cost. Previous studies and our onsite tests show that Cauchy distribution (CD) effectively fits the leptokurtic feature of small timescale WPFE distributions. In this paper, we propose a chance-constrained real time dispatch (CCRTD) model with the WPFE represented by CD. Since the CD is stable and has promising mathematical characteristics, the proposed CCRTD model can be analytically transformed to a convex optimization problem considering the dependence among wind farms outputs. Moreover, the proposed model incorporates an affine control strategy compatible with AGC systems. This strategy makes the CCRTD adaptively take into account both the additional power ramping requirement and power variation on transmission lines caused by WPFE in RTD stage. Numerical test results show that the proposed method is reliable and effective. Meanwhile it is very efficient and suitable for real-time application.

math.OC↗

arXiv2019

Variational approximations using Fisher divergence

Modern applications of Bayesian inference involve models that are sufficiently complex that the corresponding posterior distributions are intractable and must be approximated. The most common approximation is based on Markov chain Monte Carlo, but these can be expensive when the data set is large and/or the model is complex, so more efficient variational approximations have recently received considerable attention. The traditional variational methods, that seek to minimize the Kullback--Leibler divergence between the posterior and a relatively simple parametric family, provide accurate and efficient estimation of the posterior mean, but often does not capture other moments, and have limitations in terms of the models to which they can be applied. Here we propose the construction of variational approximations based on minimizing the Fisher divergence, and develop an efficient computational algorithm that can be applied to a wide range of models without conjugacy or potentially unrealistic mean-field assumptions. We demonstrate the superior performance of the proposed method for the benchmark case of logistic regression.

stat.ML↗

arXiv2018

A causal inference framework for cancer cluster investigations using publicly available data

Often, a community becomes alarmed when high rates of cancer are noticed, and residents suspect that the cancer cases could be caused by a known source of hazard. In response, the CDC recommends that departments of health perform a standardized incidence ratio (SIR) analysis to determine whether the observed cancer incidence is higher than expected. This approach has several limitations that are well documented in the literature. In this paper we propose a novel causal inference approach to cancer cluster investigations, rooted in the potential outcomes framework. Assuming that a source of hazard representing a potential cause of increased cancer rates in the community is identified a priori, we introduce a new estimand called the causal SIR (cSIR). The cSIR is a ratio defined as the expected cancer incidence in the exposed population divided by the expected cancer incidence under the (counterfactual) scenario of no exposure. To estimate the cSIR we need to overcome two main challenges: 1) identify unexposed populations that are as similar as possible to the exposed one to inform estimation under the counterfactual scenario of no exposure, and 2) make inference on cancer incidence in these unexposed populations using publicly available data that are often available at a much higher level of spatial aggregation than what is desired. We overcome the first challenge by relying on matching. We overcome the second challenge by developing a Bayesian hierarchical model that borrows information from other sources to impute cancer incidence at the desired finer level of spatial aggregation. We apply our proposed approach to determine whether trichloroethylene vapor exposure has caused increased cancer incidence in Endicott, NY.

stat.ME↗

arXiv2018

A Fast Method for Array Response Adjustment with Phase-Only Constraint

In this paper, we propose a fast method for array response adjustment with phase-only constraint. This method can precisely and rapidly adjust the array response of a given point by only varying the entry phases of a pre-assigned weight vector. We show that phase-only array response adjustment can be formulated as a polygon construction problem, which can be solved by edge rotation in complex plain. Unlike the existing approaches, the proposed algorithm provides an analytical solution and guarantees a precise phase-only adjustment without pattern distortion. Moreover, the proposed method is suitable for an arbitrarily given weight vector and has a low computational complexity. Representative examples are presented to demonstrate the effectiveness of the proposed algorithm.

eess.SP↗

arXiv2018

On The Computability of Perfect Subsets of Sets with Positive Measure

A set $X \subseteq 2^ω$ with positive measure contains a perfect subset. We study such perfect subsets from the viewpoint of computability and prove that these sets can have weak computational strength. Then we connect the existence of perfect subsets of sets with positive measure with reverse mathematics.

math.LO↗

arXiv2018

Bayesian inference in high-dimensional linear models using an empirical correlation-adaptive prior

In the context of a high-dimensional linear regression model, we propose the use of an empirical correlation-adaptive prior that makes use of information in the observed predictor variable matrix to adaptively address high collinearity, determining if parameters associated with correlated predictors should be shrunk together or kept apart. Under suitable conditions, we prove that this empirical Bayes posterior concentrates around the true sparse parameter at the optimal rate asymptotically. A simplified version of a shotgun stochastic search algorithm is employed to implement the variable selection procedure, and we show, via simulation experiments across different settings and a real-data application, the favorable performance of the proposed method compared to existing methods.

stat.ME↗