arXiv subjects

Ying Yang

Publications and source records attributed to Ying Yang.

At least 73 records · Page 4Linked to original sources

arXiv2024

Testing the cosmic distance duality relation with Type Ia supernova and transverse BAO measurements

In this work, we test the cosmic distance duality relation (CDDR) by comparing the angular diameter distance (ADD) derived from the transverse Baryon Acoustic Oscillations (BAO) data with the luminosity distance (LD) from the Pantheon type Ia supernova (SNIa) sample. The binning method and Gaussian process are employed to match ADD data with LD data at the same redshift. First, we use nonparametric and parametric methods to investigate the impact of the specific prior values of the absolute magnitude $M_{\rm B}$ from SNIa observations and the sound horizon scale $r_{\rm s}$ from transverse BAO measurements on the CDDR tests. The results obtained from the parametric and non-parametric methods indicate that specific prior values of $M_{\rm B}$ and $r_{\rm s}$ lead to significant biases on the CDDR test. Then, to avoid these biases, we propose a method independent of $M_{\rm B}$ and $r_{\rm s}$ to test CDDR by considering the fiducial value of $κ\equiv10^{M_{\rm B} \over 5}r_{\rm s}$ as a nuisance parameter and then marginalizing its influence with a flat prior in the analysis. No violation of the CDDR is found, and the transverse BAO measurement can be used as a powerful tool to verify the validity of CDDR in the cosmological-model-independent method.

astro-ph.CO↗

arXiv2024

MX precipitate behavior in an irradiated advanced Fe-9Cr steel: Self-ion irradiation effects on phase stability

Reduced activation ferritic/martensitic (RAFM) steels are the leading candidate structural materials for first-wall and blanket components in fusion reactors. This work is the first in a series to provide a systematic roadmap of MX precipitate stability in RAFM steels under various ion irradiation conditions. Here, the MX-TiC precipitate behavior in an advanced Fe-9Cr RAFM steel is assessed under self-ion irradiation to damage levels ranging from 1 to 100 displacements per atom (dpa) at temperatures ranging from 300-600°C to isolate the effects of temperature and damage level on precipitate stability. The pre-existing MX-TiC precipitates are shown to exhibit temperature-dominated responses, including coarsening above 400°C at damage levels of 15 dpa, while damage levels studied at 50 dpa and higher showed dissolution across all temperature ranges studied. The effects of ballistic dissolution and diffusion on precipitate behavior are outlined as a function of precipitate characteristics (number density, size, and 25 volume fraction) and irradiation parameters with the use of the recoil resolution model of precipitate stability. This work provides critical insights into MX-TiC stability to high dose in-order to further optimize advanced steels with improved radiation resistance.

cond-mat.mtrl-sci↗

arXiv2024

Limit Results for Estimation of Connectivity Matrix in Multi-layer Stochastic Block Models

Multi-layer networks arise naturally in various domains including biology, finance and sociology, among others. The multi-layer stochastic block model (multi-layer SBM) is commonly used for community detection in the multi-layer networks. Most of current literature focuses on statistical consistency of community detection methods under multi-layer SBMs. However, the asymptotic distributional properties are also indispensable which play an important role in statistical inference. In this work, we aim to study the estimation and asymptotic properties of the layer-wise scaled connectivity matrices in the multi-layer SBMs. We develop a novel and efficient method to estimate the scaled connectivity matrices. Under the multi-layer SBM and its variant multi-layer degree-corrected SBM, we establish the asymptotic normality of the estimated matrices under mild conditions, which can be used for interval estimation and hypothesis testing. Simulations show the superior performance of proposed method over existing methods in two considered statistical inference tasks. We also apply the method to a real dataset and obtain interpretable results.

math.ST↗

arXiv2024

Spectral co-Clustering in Multi-layer Directed Networks

Modern network analysis often involves multi-layer network data in which the nodes are aligned, and the edges on each layer represent one of the multiple relations among the nodes. Current literature on multi-layer network data is mostly limited to undirected relations. However, direct relations are more common and may introduce extra information. This study focuses on community detection (or clustering) in multi-layer directed networks. To take into account the asymmetry, a novel spectral-co-clustering-based algorithm is developed to detect co-clusters, which capture the sending patterns and receiving patterns of nodes, respectively. Specifically, the eigendecomposition of the debiased sum of Gram matrices over the layer-wise adjacency matrices is computed, followed by the k-means, where the sum of Gram matrices is used to avoid possible cancellation of clusters caused by direct summation. Theoretical analysis of the algorithm under the multi-layer stochastic co-block model is provided, where the common assumption that the cluster number is coupled with the rank of the model is relaxed. After a systematic analysis of the eigenvectors of the population version algorithm, the misclassification rates are derived, which show that multi-layers would bring benefits to the clustering performance. The experimental results of simulated data corroborate the theoretical predictions, and the analysis of a real-world trade network dataset provides interpretable results.

math.ST↗

arXiv2024

Adaptive Bayesian Multivariate Spline Knot Inference with Prior Specifications on Model Complexity

In multivariate spline regression, the number and locations of knots influence the performance and interpretability significantly. However, due to non-differentiability and varying dimensions, there is no desirable frequentist method to make inference on knots. In this article, we propose a fully Bayesian approach for knot inference in multivariate spline regression. The existing Bayesian method often uses BIC to calculate the posterior, but BIC is too liberal and it will heavily overestimate the knot number when the candidate model space is large. We specify a new prior on the knot number to take into account the complexity of the model space and derive an analytic formula in the normal model. In the non-normal cases, we utilize the extended Bayesian information criterion to approximate the posterior density. The samples are simulated in the space with differing dimensions via reversible jump Markov chain Monte Carlo. We apply the proposed method in knot inference and manifold denoising. Experiments demonstrate the splendid capability of the algorithm, especially in function fitting with jumping discontinuity.

stat.ME↗

arXiv2024

Anomalous Long-Distance Coherence in Critically-Driven Cavity Magnonics

Developing quantum networks necessitates coherently connecting distant systems via remote strong coupling. Here, we demonstrate long-distance coherence in cavity magnonics operating in the linear regime. By locally setting the cavity near critical coupling with travelling photons, non-local magnon-photon coherence is established via strong coupling over a 2-meter distance. We observe two anomalies in this long-distance coherence: first, the coupling strength oscillates twice the period of conventional photon-mediated couplings; second, clear mode splitting is observed within the cavity linewidth. Both effects cannot be explained by conventional coupled-mode theory, which reveal the tip of an iceberg of photon-mediated coupling in systems under critical driving. Our work shows the potential of using critical phenomena for harnessing long-distance coherence in distributed systems.

quant-ph↗

arXiv2024

Putting Our Minds Together: Iterative Exploration for Collaborative Mind Mapping

We delineate the development of a mind-mapping system designed concurrently for both VR and desktop platforms. Employing an iterative methodology with groups of users, we systematically examined and improved various facets of our system, including interactions, communication mechanisms and gamification elements, to streamline the mind-mapping process while augmenting situational awareness and promoting active engagement among collaborators. We also report our observational findings on these facets from this iterative design process.

cs.HC↗

arXiv2024

DomainVerse: A Benchmark Towards Real-World Distribution Shifts For Tuning-Free Adaptive Domain Generalization

Traditional cross-domain tasks, including domain adaptation and domain generalization, rely heavily on training model by source domain data. With the recent advance of vision-language models (VLMs), viewed as natural source models, the cross-domain task changes to directly adapt the pre-trained source model to arbitrary target domains equipped with prior domain knowledge, and we name this task Adaptive Domain Generalization (ADG). However, current cross-domain datasets have many limitations, such as unrealistic domains, unclear domain definitions, and the inability to fine-grained domain decomposition, which drives us to establish a novel dataset DomainVerse for ADG. Benefiting from the introduced hierarchical definition of domain shifts, DomainVerse consists of about 0.5 million images from 390 fine-grained realistic domains. With the help of the constructed DomainVerse and VLMs, we propose two methods called Domain CLIP and Domain++ CLIP for tuning-free adaptive domain generalization. Extensive and comprehensive experiments demonstrate the significance of the dataset and the effectiveness of the proposed methods.

cs.CV↗

arXiv2024

Transport response of topological hinge modes in $α$-Bi$_4$Br$_4$

Electronic topological phases are renowned for their unique properties, where conducting surface states exist on the boundary of an insulating three-dimensional bulk. While the transport response of the surface states has been extensively studied, the response of the topological hinge modes remains elusive. Here, we investigate a layered topological insulator $α$-Bi$_4$Br$_4$, and provide the first evidence for quantum transport in gapless topological hinge states existing within the insulating bulk and surface energy gaps. Our magnetoresistance measurements reveal pronounced h/e periodic (where h denotes Planck's constant and e represents the electron charge) Aharonov-Bohm oscillation. The observed periodicity, which directly reflects the enclosed area of phase-coherent electron propagation, matches the area enclosed by the sample hinges, providing compelling evidence for the quantum interference of electrons circumnavigating around the hinges. Notably, the h/e oscillations evolve as a function of magnetic field orientation, following the interference paths along the hinge modes that are allowed by topology and symmetry, and in agreement with the locations of the hinge modes according to our scanning tunneling microscopy images. Remarkably, this demonstration of quantum transport in a topological insulator can be achieved using a flake geometry and we show that it remains robust even at elevated temperatures. Our findings collectively reveal the quantum transport response of topological hinge modes with both topological nature and quantum coherence, which can be directly applied to the development of efficient quantum electronic devices.

cond-mat.mes-hall↗

arXiv2024

Simultaneous measurements on cosmic curvature and opacity using latest HII regions and $H(z)$ observations

The different spatial curvatures of the universe affect the measurement of cosmological distances, which may also contribute to explaining the observed dimming of type Ia supernovae. This phenomenon may be caused by the opacity of the universe. Similarly, the opacity of the universe can lead to a bias in our measurements of curvature. Thus, it is necessary to measure cosmic curvature and opacity simultaneously. In this paper, we propose a new model-independent method to simultaneously measure the cosmic curvature and opacity by using the latest observations of HII galaxies acting as standard candles and the latest Hubble parameter observations. The machine learning method-Artificial Neural Network is adopted to reconstruct observed Hubble parameter $H(z)$ observations. Our results support a slightly opaque and flat universe at $1σ$ confidence level by using previous 156 HII regions sample. However, the negative curvature is obtained by using the latest 181 HII regions sample in the redshift range $z\sim 2.5$. More importantly, we obtain the simultaneous measurements with precision on the cosmic opacity $\rmΔτ\sim 10^{-2}$ and curvature $\rmΔΩ_K\sim 10^{-1}$. A strong degeneracy between the cosmic opacity and curvature parameters is also revealed in this analysis.

astro-ph.CO↗

arXiv2023

Variability of Radar Backscattering from Bare Soil Disturbed by Surface Parameters Uncertainties

Surface roughness and dielectric properties are crucial in characterizing radar backscattering from bare soil surfaces. However, their estimation depends on the surface size of the sampling profile, and the complex relative permittivity is disturbed by different dielectric spectrum models. Hence, it is desirable to know how the uncertainty associated with roughness and complex relative permittivity propagates to radar backscattering coefficients. To identify the extent to which the uncertainty propagates, we examined the roughness sample variance and bias of complex relative permittivity. Then, we evaluated the error of radar backscattering coefficients as a function of incident angle, frequency, and polarization induced by each of the two uncertainty sources and their coupling. The results help interpret the discrepancy among model predictions and in-situ measurements and suggest a minimum surface size to estimate the RMS height and correlation length to confine the radar backscattering coefficients' error.

physics.app-ph↗

arXiv2023

Fast Algorithms for Finite Element nonlinear Discrete Systems to Solve the PNP Equations

The Poisson-Nernst-Planck (PNP) equations are one of the most effective model for describing electrostatic interactions and diffusion processes in ion solution systems, and have been widely used in the numerical simulations of biological ion channels, semiconductor devices, and nanopore systems. Due to the characteristics of strong coupling, convection dominance, nonlinearity and multiscale, the classic Gummel iteration for the nonlinear discrete system of PNP equations converges slowly or even diverges. We focus on fast algorithms of nonlinear discrete system for the general PNP equations, which have better adaptability, friendliness and efficiency than the Gummel iteration. First, a geometric full approximation storage (FAS) algorithm is proposed to improve the slow convergence speed of the Gummel iteration. Second, an algebraic FAS algorithm is designed, which does not require multi-level geometric information and is more suitable for practical computation compared with the geometric one. Finally, improved algorithms based on the acceleration technique and adaptive method are proposed to solve the problems of excessive coarse grid iterations and insufficient adaptability to the size of computational domain in the algebraic FAS algorithm. The numerical experiments are shown for the geometric, algebraic FAS and improved algorithms respectively to illustrate the effiency of the algorithms.

math.NA↗

arXiv2023

Policy Evaluation for Temporal and/or Spatial Dependent Experiments

The aim of this paper is to establish a causal link between the policies implemented by technology companies and the outcomes they yield within intricate temporal and/or spatial dependent experiments. We propose a novel temporal/spatio-temporal Varying Coefficient Decision Process (VCDP) model, capable of effectively capturing the evolving treatment effects in situations characterized by temporal and/or spatial dependence. Our methodology encompasses the decomposition of the Average Treatment Effect (ATE) into the Direct Effect (DE) and the Indirect Effect (IE). We subsequently devise comprehensive procedures for estimating and making inferences about both DE and IE. Additionally, we provide a rigorous analysis of the statistical properties of these procedures, such as asymptotic power. To substantiate the effectiveness of our approach, we carry out extensive simulations and real data analyses.

stat.ME↗

arXiv2023

The Existence the Solution of Nonlinear Discrete Schemes and Convergence of a Linearized Iterative Method for time-dependent PNP Equations

We establish the existence theory of several commonly used finite element (FE) nonlinear fully discrete solutions, and the convergence theory of a linearized iteration. First, it is shown for standard FE, SUPG and edge-averaged method respectively that the stiffness matrix is a column M-matrix under certain conditions, and then the existence theory of these three FE nonlinear fully discrete solutions is presented by using Brouwer's fixed point theorem. Second, the contraction of a commonly used linearized iterative method-Gummel iteration is proven, and then the convergence theory is established for the iteration. At last, a numerical experiment is shown to verifies the theories.

math.NA↗

arXiv2023

Testing the FLRW metric with the Hubble and transversal BAO measurements

The cosmological principle is one of the fundamental assumptions of the standard model of Cosmology (SCM), and it allow us to describe cosmic distances and clocks by using the Friedmann-Lema$\rm{\hat{ı}}$tre-Roberton-Walker (FLRW) metric. Thus, it is essential to test the FLRW metric with cosmological observations to verify the validity of the SCM. In this work, we perform tests of the FLRW metric by comparing the observational comoving angles between the Hubble $H(z)$ and angular Baryon Acoustic Oscillation (BAO) measurements. The Gaussian process is employed to reconstruct the Hubble $H(z)$ measurements and the angular diameter distance (ADD) from the transversal BAO data. A non-parametric method is adopted to probe the possible deviations from the FLRW metric at any redshift by comparing the comoving distances from the reconstructed Hubble $H(z)$ measurements with the ADD reconstructed from the transversal BAO data. Then, we propose two types of parameterizations for the deviations from the FLRW metric, and test the FLRW metric by using the priors of specific sound horizon scales. To avoid the bias caused by the prior of a specific sound horizon scale, we perform the consistency test with a flat prior of the sound horizon scale. We find that there a concordance between the FLRW metric and the observational data by using parametric and non-parametric methods, and the parameterizations can be employed to test the FLRW metric in a new way independent of the sound horizon scale.

gr-qc↗

arXiv2023

Counterfactual Graph Transformer for Traffic Flow Prediction

Traffic flow prediction (TFP) is a fundamental problem of the Intelligent Transportation System (ITS), as it models the latent spatial-temporal dependency of traffic flow for potential congestion prediction. Recent graph-based models with multiple kinds of attention mechanisms have achieved promising performance. However, existing methods for traffic flow prediction tend to inherit the bias pattern from the dataset and lack interpretability. To this end, we propose a Counterfactual Graph Transformer (CGT) model with an instance-level explainer (e.g., finding the important subgraphs) specifically designed for TFP. We design a perturbation mask generator over input sensor features at the time dimension and the graph structure on the graph transformer module to obtain spatial and temporal counterfactual explanations. By searching the optimal perturbation masks on the input data feature and graph structures, we can obtain the concise and dominant data or graph edge links for the subsequent TFP task. After re-training the utilized graph transformer model after counterfactual perturbation, we can obtain improved and interpretable traffic flow prediction. Extensive results on three real-world public datasets show that CGT can produce reliable explanations and is promising for traffic flow prediction.

cs.LG↗

arXiv2023

Error Analysis of Virtual Element Method for the Poisson-Boltzmann Equation

The Poisson-Boltzmann equation is a nonlinear elliptic equation with Dirac distribution sources, which has been widely applied to the prediction of electrostatics potential of biological biomolecular systems in solution. In this paper, we discuss and analysis the virtual element method for the Poisson-Boltzmann equation on general polyhedral meshes. Under the low regularity of the solution of the whole domain, nearly optimal error estimates in both L2-norm and H1-norm for the virtual element approximation are obtained. The numerical experiment on different polyhedral meshes shows the efficiency of the virtual element method and verifies the proposed theoretical prediction.

math.NA↗

arXiv2023

An Entire Renal Anatomy Extraction Network for Advanced CAD During Partial Nephrectomy

Partial nephrectomy (PN) is common surgery in urology. Digitization of renal anatomies brings much help to many computer-aided diagnosis (CAD) techniques during PN. However, the manual delineation of kidney vascular system and tumor on each slice is time consuming, error-prone, and inconsistent. Therefore, we proposed an entire renal anatomies extraction method from Computed Tomographic Angiographic (CTA) images fully based on deep learning. We adopted a coarse-to-fine workflow to extract target tissues: first, we roughly located the kidney region, and then cropped the kidney region for more detail extraction. The network we used in our workflow is based on 3D U-Net. To dealing with the imbalance of class contributions to loss, we combined the dice loss with focal loss, and added an extra weight to prevent excessive attention. We also improved the manual annotations of vessels by merging semi-trained model's prediction and original annotations under supervision. We performed several experiments to find the best-fitting combination of variables for training. We trained and evaluated the models on our 60 cases dataset with 3 different sources. The average dice score coefficient (DSC) of kidney, tumor, cyst, artery, and vein, were 90.9%, 90.0%, 89.2%, 80.1% and 82.2% respectively. Our modulate weight and hybrid strategy of loss function increased the average DSC of all tissues about 8-20%. Our optimization of vessel annotation improved the average DSC about 1-5%. We proved the efficiency of our network on renal anatomies segmentation. The high accuracy and fully automation make it possible to quickly digitize the personal renal anatomies, which greatly increases the feasibility and practicability of CAD application on urology surgery.

eess.IV↗