arXiv subjects

Ming Yu

Publications and source records attributed to Ming Yu.

At least 19 recordsLinked to original sources

arXiv2026

Thermochemical non-equilibrium effects on turbulent boundary layers

This investigation employs direct numerical simulations (DNS) of high-Mach-number turbulent boundary layers under three flow conditions: a low-enthalpy calorically perfect gas, and two high-temperature gas mixtures, one in chemical non-equilibrium state and the other in full thermochemical non-equilibrium state. The influences of the two-temperature model on turbulent statistics and the coupling among turbulence, chemistry, and vibrational energy are examined. It is found that while high-enthalpy effects leave the velocity statistics virtually unchanged, they dramatically modify the near-wall temperature field. A pronounced disparity between the translational-rotational temperature and the vibrational temperature arises in the near-wall region, rendering the conventional generalized Reynolds analogy (GRA) inaccurate for vibrational temperature. To remedy this, a novel composite GRA is proposed that blends a vibrational-temperature-based relation with the standard formulation, and it demonstrates excellent agreement with the DNS data. Thermal non-equilibrium effects also substantially alter near-wall chemical reactions: it suppresses O2 dissociation while promoting NO formation, leading to a corresponding decrease and increase in the mean concentrations of O and NO, respectively. Spectral analyses of the turbulence-chemistry and turbulence-vibrational relaxation interaction terms reveal that temperature fluctuations dominate these flow quantities at energy-containing scales. Integrating the resulting spectral functions, we evaluate subgrid-scale closure terms for large-eddy simulation. At small filtering scales, the magnitude of the cross-correlation term rivals or exceeds that of the temperature fluctuation term, whereas the temperature fluctuation term becomes dominant at larger filter scales.

physics.flu-dyn↗

arXiv2024

Interactions between the near-wall turbulent structures and heavy particles in compressible turbulent boundary layers

In the present study, we conduct direct numerical simulations to investigate the near-wall dynamics of compressible turbulent boundary layers at the free-stream Mach number of 6 laden with heavy particles. By inspecting the instantaneous near-wall flow structures, Reynolds stresses and the impacts of particle forces on solenoidal and dilatational motions, we observed that higher particle mass loadings lead to the less meandering yet almost equally intense velocity streaks, but the weakened wall-normal velocity fluctuations induced by vortices and near-wall dilatational motions organized as travelling wave packets. The strong correlation between the particle force and dilatational velocities indicates that particles are accelerated/decelerated while travelling through these travelling wave packets composed of expansive and compressive events, and in return, leading to the weakened dilatational motions of the fluid during this process. This correlation further supports the elucidation by Yu et al. (J. Fluid. Mech., vol. 984, 2024, pp. A44) that dilatational motions are generated by the vortices that induce strong bursting events, rather than the evolving perturbations beneath the velocity streaks. Nevertheless, the variation of skin friction in the presently considered cases with moderate mass loadings, either increased or decreased by the presence of particles, is found to be primarily attributed to the solenoidal Reynolds shear stress as in incompressible turbulence, suggesting the essentially unaltered nature of wall-bounded turbulence populated by vortical and shear motions instead of gradually switching to the state dominated by dilatational motions.

physics.flu-dyn↗

arXiv2024

AlzhiNet: Traversing from 2DCNN to 3DCNN, Towards Early Detection and Diagnosis of Alzheimer's Disease

Alzheimer's disease (AD) is a progressive neurodegenerative disorder with increasing prevalence among the aging population, necessitating early and accurate diagnosis for effective disease management. In this study, we present a novel hybrid deep learning framework that integrates both 2D Convolutional Neural Networks (2D-CNN) and 3D Convolutional Neural Networks (3D-CNN), along with a custom loss function and volumetric data augmentation, to enhance feature extraction and improve classification performance in AD diagnosis. According to extensive experiments, AlzhiNet outperforms standalone 2D and 3D models, highlighting the importance of combining these complementary representations of data. The depth and quality of 3D volumes derived from the augmented 2D slices also significantly influence the model's performance. The results indicate that carefully selecting weighting factors in hybrid predictions is imperative for achieving optimal results. Our framework has been validated on the Magnetic Resonance Imaging (MRI) from Kaggle and MIRIAD datasets, obtaining accuracies of 98.9% and 99.99%, respectively, with an AUC of 100%. Furthermore, AlzhiNet was studied under a variety of perturbation scenarios on the Alzheimer's Kaggle dataset, including Gaussian noise, brightness, contrast, salt and pepper noise, color jitter, and occlusion. The results obtained show that AlzhiNet is more robust to perturbations than ResNet-18, making it an excellent choice for real-world applications. This approach represents a promising advancement in the early diagnosis and treatment planning for Alzheimer's disease.

eess.IV↗

arXiv2024

Modelling aerodynamic forces and torques of spheroid particles in compressible flows

In the present study, we conduct numerical simulations of compressible flows around spheroid particles, for the purpose of refining empirical formulas for drag force, lift force, and pitching torque acting on them. Through an analysis of approximately a thousand numerical simulation cases spanning a wide range of Mach numbers, Reynolds numbers and particle aspect ratios, we first identify the crucial parameters that are strongly correlated with the forces and torques via Spearman correlation analysis, based on which the empirical formulas for the drag force, lift force and pitching torque coefficients are refined. The novel formulas developed for compressible flows exhibit consistency with their incompressible counterparts at low Mach number limits and, moreover, yield accurate predictions with average relative errors of less than 5%. This underscores their robustness and reliability in predicting aerodynamic loads on spheroidal particles under various flow conditions.

physics.flu-dyn↗

arXiv2024

Momentum and kinetic energy transport in supersonic particle-laden turbulent boundary layers

In the present study, we conduct direct numerical simulations of two-way force-coupled particle-laden compressible turbulent boundary layers at the free-stream Mach number of 2.0 for the purpose of examining the effects of particles on the transport of momentum and kinetic energy. By analyzing turbulent databases with various particle Stokes numbers and mass loadings, we observe that the presence of particles suppresses turbulent fluctuations and can even laminarize flow under high mass loading conditions. This is reflected by the wider and more coherent near-wall velocity streaks, reduced Reynolds stresses, and diminished contributions to skin friction and turbulent kinetic energy production. Additionally, the particle feedback force becomes more dominant in turbulent production near the wall and at small scales as mass loadings increase, which is found to be caused by the residual velocity fluctuations from particles swept down from the outer region. Furthermore, we identify that particle dissipation, resulting from the relative velocity between the fluid and particles, accounts for less than 1% of mean kinetic energy viscous dissipation and less than 10% of turbulent kinetic energy dissipation in the case with the highest mass loading. This suggests a modest impact on the internal energy variation of the fluid if two-way heat coupling is introduced. The elevated mean temperature is found in the near-wall region and is ascribed to the influence of the particle feedback force and reduced turbulent diffusion in high mass loading cases.

physics.flu-dyn↗

arXiv2024

Celestial CFT from CHY Formalism: Center Charge and Finite Size Effect

Scattering amplitudes in gauge theories can be calculated either by bulk theories in 4d Minkowski space-time($Mink_4$), or perceived as the correlation functions in celestial CFT(CCFT) living in the celestial sphere at null infinity, where an infinite-dimensional asymptotic symmetry, BMS group, resides. Another well developed method is the CHY formalism, which formulates the scattering amplitude in terms of the correlation functions on a 2d world sheet, on which an ambitwistor string theory is defined. The relationship between CHY theory and CCFT is encoded in scattering equations, which are algebraic equations lacking of analytical solutions in general. So we start from the CHY formalism, take the collinear limit, then find a nice operator formalism for the CCFT. In particular, the center charge $c$ is calculated to be 36 for the CCFT related to the 4d Yang-Mills theory. It then follows that the 4d cosmological constant naturally arises as the finite size effect in 2d CCFT, which is calculated by the method of the $T\bar{T}$ perturbed CCFT.

hep-th↗

arXiv2024

Transport of inertial spherical particles in compressible turbulent boundary layers

In the present study, we perform direct numerical simulations of compressible turbulent boundary layers at the free stream Mach number of 2 ~ 6 laden with dilute phase of spherical particles to investigate the Mach number effects on particle transport and dynamics. Most of the phenomena observed and well-recognized for inertia particles in incompressible wall-bounded turbulent flows, such as the near-wall preferential accumulation and clustering beneath the low-speed streaks, the flatter mean velocity profiles and the trend variation of the particle velocity fluctuations, are identified in the compressible turbulent boundary layer as well. However, we find that the compressibility effects are significant for large inertia particles. As the Mach number increases, the near-wall accumulation and the small-scale clustering are alleviated, which is probably caused by the variation of the fluid density and viscosity that are crucial to particle dynamics. This can be affected by the fact that the forces acting on the particles with the viscous Stokes number greater than 500 are modulated by the comparatively high particle Mach numbers in the near-wall region. This is also the reason for the abatement of the streamwise particle velocity fluctuation intensities with the Mach numbers.

physics.flu-dyn↗

arXiv2023

DMseg: a Python algorithm for de novo detection of differentially or variably methylated regions

Detecting and assessing statistical significance of differentially methylated regions (DMRs) is a fundamental task in methylome association studies. While the average differential methylation in different phenotype groups has been the inferential focus, methylation changes in chromosomal regions may also present as differential variability, i.e., variably methylated regions (VMRs). Testing statistical significance of regional differential methylation is a challenging problem, and existing algorithms do not provide accurate type I error control for genome-wide DMR or VMR analysis. No algorithm has been publicly available for detecting VMRs. We propose DMseg, a Python algorithm with efficient DMR/VMR detection and significance assessment for array-based methylome data, and compare its performance to Bumphunter, a popular existing algorithm. Operationally, DMseg searches for DMRs or VMRs within CpG clusters that are adaptively determined by both gap distance and correlation between contiguous CpG sites in a microarray. Levene test was implemented for assessing differential variability of individual CpGs. A likelihood ratio statistic is proposed to test for a constant difference within CpGs in a DMR or VMR to summarize the evidence of regional difference. Using a stratified permutation scheme and pooling null distributions of LRTs from clusters with similar numbers of CpGs, DMseg provides accurate control of the type I error rate. In simulation experiments, DMseg shows superior power than Bumphunter to detect DMRs. Application to methylome data of Barrett's esophagus and esophageal adenocarcinoma reveals a number of DMRs and VMRs of biological interest.

stat.ME↗

arXiv2021

Information Prebuilt Recurrent Reconstruction Network for Video Super-Resolution

The video super-resolution (VSR) method based on the recurrent convolutional network has strong temporal modeling capability for video sequences. However, the temporal receptive field of different recurrent units in the unidirectional recurrent network is unbalanced. Earlier reconstruction frames receive less spatio-temporal information, resulting in fuzziness or artifacts. Although the bidirectional recurrent network can alleviate this problem, it requires more memory space and fails to perform many tasks with low latency requirements. To solve the above problems, we propose an end-to-end information prebuilt recurrent reconstruction network (IPRRN), consisting of an information prebuilt network (IPNet) and a recurrent reconstruction network (RRNet). By integrating sufficient information from the front of the video to build the hidden state needed for the initially recurrent unit to help restore the earlier frames, the information prebuilt network balances the input information difference at different time steps. In addition, we demonstrate an efficient recurrent reconstruction network, which outperforms the existing unidirectional recurrent schemes in all aspects. Many experiments have verified the effectiveness of the network we propose, which can effectively achieve better quantitative and qualitative evaluation performance compared to the existing state-of-the-art methods.

eess.IV↗

arXiv2019

Constrained High Dimensional Statistical Inference

In typical high dimensional statistical inference problems, confidence intervals and hypothesis tests are performed for a low dimensional subset of model parameters under the assumption that the parameters of interest are unconstrained. However, in many problems, there are natural constraints on model parameters and one is interested in whether the parameters are on the boundary of the constraint or not. e.g. non-negativity constraints for transmission rates in network diffusion. In this paper, we provide algorithms to solve this problem of hypothesis testing in high-dimensional statistical models under constrained parameter space. We show that following our testing procedure we are able to get asymptotic designed Type I error under the null. Numerical experiments demonstrate that our algorithm has greater power than the standard algorithms where the constraints are ignored. We demonstrate the effectiveness of our algorithms on two real datasets where we have {\emph{intrinsic}} constraint on the parameters.

stat.ME↗

arXiv2019

Convergent Policy Optimization for Safe Reinforcement Learning

We study the safe reinforcement learning problem with nonlinear function approximation, where policy optimization is formulated as a constrained optimization problem with both the objective and the constraint being nonconvex functions. For such a problem, we construct a sequence of surrogate convex constrained optimization problems by replacing the nonconvex functions locally with convex quadratic functions obtained from policy gradient estimators. We prove that the solutions to these surrogate problems converge to a stationary point of the original nonconvex problem. Furthermore, to extend our theoretical results, we apply our algorithm to examples of optimal control and multi-agent reinforcement learning with safety constraints.

cs.LG↗

arXiv2019

Simultaneous Inference for Pairwise Graphical Models with Generalized Score Matching

Probabilistic graphical models provide a flexible yet parsimonious framework for modeling dependencies among nodes in networks. There is a vast literature on parameter estimation and consistent model selection for graphical models. However, in many of the applications, scientists are also interested in quantifying the uncertainty associated with the estimated parameters and selected models, which current literature has not addressed thoroughly. In this paper, we propose a novel estimator for statistical inference on edge parameters in pairwise graphical models based on generalized Hyv\"arinen scoring rule. Hyv\"arinen scoring rule is especially useful in cases where the normalizing constant cannot be obtained efficiently in a closed form, which is a common problem for graphical models, including Ising models and truncated Gaussian graphical models. Our estimator allows us to perform statistical inference for general graphical models whereas the existing works mostly focus on statistical inference for Gaussian graphical models where finding normalizing constant is computationally tractable. Under mild conditions that are typically assumed in the literature for consistent estimation, we prove that our proposed estimator is $\sqrt{n}$-consistent and asymptotically normal, which allows us to construct confidence intervals and build hypothesis tests for edge parameters. Moreover, we show how our proposed method can be applied to test hypotheses that involve a large number of model parameters simultaneously. We illustrate validity of our estimator through extensive simulation studies on a diverse collection of data-generating processes.

stat.ME↗

arXiv2018

An Improved Deep Belief Network Model for Road Safety Analyses

Crash prediction is a critical component of road safety analyses. A widely adopted approach to crash prediction is application of regression based techniques. The underlying calibration process is often time-consuming, requiring significant domain knowledge and expertise and cannot be easily automated. This paper introduces a new machine learning (ML) based approach as an alternative to the traditional techniques. The proposed ML model is called regularized deep belief network, which is a deep neural network with two training steps: it is first trained using an unsupervised learning algorithm and then fine-tuned by initializing a Bayesian neural network with the trained weights from the first step. The resulting model is expected to have improved prediction power and reduced need for the time-consuming human intervention. In this paper, we attempt to demonstrate the potential of this new model for crash prediction through two case studies including a collision data set from 800 km stretch of Highway 401 and other highways in Ontario, Canada. Our intention is to show the performance of this ML approach in comparison to various traditional models including negative binomial (NB) model, kernel regression (KR), and Bayesian neural network (Bayesian NN). We also attempt to address other related issues such as effect of training data size and training parameters.

cs.LG↗

arXiv2018

Provable Gaussian Embedding with One Observation

The success of machine learning methods heavily relies on having an appropriate representation for data at hand. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about data. However, recently there has been a surge in approaches that learn how to encode the data automatically in a low dimensional space. Exponential family embedding provides a probabilistic framework for learning low-dimensional representation for various types of high-dimensional data. Though successful in practice, theoretical underpinnings for exponential family embeddings have not been established. In this paper, we study the Gaussian embedding model and develop the first theoretical results for exponential family embedding models. First, we show that, under mild condition, the embedding structure can be learned from one observation by leveraging the parameter sharing between different contexts even though the data are dependent with each other. Second, we study properties of two algorithms used for learning the embedding structure and establish convergence results for each of them. The first algorithm is based on a convex relaxation, while the other solved the non-convex formulation of the problem directly. Experiments demonstrate the effectiveness of our approach.

stat.ML↗

arXiv2018

Learning Influence-Receptivity Network Structure with Guarantee

Traditional works on community detection from observations of information cascade assume that a single adjacency matrix parametrizes all the observed cascades. However, in reality the connection structure usually does not stay the same across cascades. For example, different people have different topics of interest, therefore the connection structure depends on the information/topic content of the cascade. In this paper we consider the case where we observe a sequence of noisy adjacency matrices triggered by information/event with different topic distributions. We propose a novel latent model using the intuition that a connection is more likely to exist between two nodes if they are interested in similar topics, which are common with the information/event. Specifically, we endow each node with two node-topic vectors: an influence vector that measures how influential/authoritative they are on each topic; and a receptivity vector that measures how receptive/susceptible they are to each topic. We show how these two node-topic structures can be estimated from observed adjacency matrices with theoretical guarantee on estimation error, in cases where the topic distributions of the information/event are known, as well as when they are unknown. Experiments on synthetic and real data demonstrate the effectiveness of our model and superior performance compared to state-of-the-art methods.

stat.ML↗

arXiv2018

Simultaneous Parameter Learning and Bi-Clustering for Multi-Response Models

We consider multi-response and multitask regression models, where the parameter matrix to be estimated is expected to have an unknown grouping structure. The groupings can be along tasks, or features, or both, the last one indicating a bi-cluster or "checkerboard" structure. Discovering this grouping structure along with parameter inference makes sense in several applications, such as multi-response Genome-Wide Association Studies. This additional structure can not only can be leveraged for more accurate parameter estimation, but it also provides valuable information on the underlying data mechanisms (e.g. relationships among genotypes and phenotypes in GWAS). In this paper, we propose two formulations to simultaneously learn the parameter matrix and its group structures, based on convex regularization penalties. We present optimization approaches to solve the resulting problems and provide numerical convergence guarantees. Our approaches are validated on extensive simulations and real datasets concerning phenotypes and genotypes of plant varieties.

stat.ML↗

arXiv2018

Recovery of simultaneous low rank and two-way sparse coefficient matrices, a nonconvex approach

We study the problem of recovery of matrices that are simultaneously low rank and row and/or column sparse. Such matrices appear in recent applications in cognitive neuroscience, imaging, computer vision, macroeconomics, and genetics. We propose a GDT (Gradient Descent with hard Thresholding) algorithm to efficiently recover matrices with such structure, by minimizing a bi-convex function over a nonconvex set of constraints. We show linear convergence of the iterates obtained by GDT to a region within statistical error of an optimal solution. As an application of our method, we consider multi-task learning problems and show that the statistical error rate obtained by GDT is near optimal compared to minimax rate. Experiments demonstrate competitive performance and much faster running speed compared to existing methods, on both simulations and real data sets.

stat.ML↗

arXiv2017

Multitask Learning using Task Clustering with Applications to Predictive Modeling and GWAS of Plant Varieties

Inferring predictive maps between multiple input and multiple output variables or tasks has innumerable applications in data science. Multi-task learning attempts to learn the maps to several output tasks simultaneously with information sharing between them. We propose a novel multi-task learning framework for sparse linear regression, where a full task hierarchy is automatically inferred from the data, with the assumption that the task parameters follow a hierarchical tree structure. The leaves of the tree are the parameters for individual tasks, and the root is the global model that approximates all the tasks. We apply the proposed approach to develop and evaluate: (a) predictive models of plant traits using large-scale and automated remote sensing data, and (b) GWAS methodologies mapping such derived phenotypes in lieu of hand-measured traits. We demonstrate the superior performance of our approach compared to other methods, as well as the usefulness of discovering hierarchical groupings between tasks. Our results suggest that richer genetic mapping can indeed be obtained from the remote sensing data. In addition, our discovered groupings reveal interesting insights from a plant science perspective.

stat.ML↗