arXiv subjects

Yu Fan

Publications and source records attributed to Yu Fan.

31 records · Page 2Linked to original sources

arXiv2024

Inferior interfacial superconductivity in 1 UC FeSe/SrVO$_3$/SrTiO$_3$ with screened interfacial electron-phonon coupling

Monolayer FeSe/TiO$_x$ and FeSe/FeO$_x$ interfaces exhibit significant superconductivity enhancement compared to bulk FeSe, with interfacial electron-phonon coupling (EPC) playing a crucial role. However, the reduced dimensionality in monolayer FeSe, which may drive superconducting fluctuations, complicates the understanding of the enhancement mechanisms. Here we construct a new superconducting interface: monolayer FeSe/SrVO$_3$/SrTiO$_3$, in which the itinerant electrons of highly metallic SrVO$_3$ films can screen all the high-energy Fuchs-Kliewer phonons, including those of SrTiO$_3$, making it the first FeSe/oxide system with screened interfacial EPC while maintaining the monolayer FeSe thickness. Despite comparable doping levels, the heavily electron-doped monolayer FeSe/SrVO$_3$ exhibits a lower pairing temperature ($T_\mathrm{g}$ $\sim$ 48 K) than FeSe/SrTiO$_3$ and FeSe/LaFeO$_3$. Our findings disentangle the contributions of interfacial EPC from dimensionality on enhancing $T_\mathrm{g}$ in FeSe/oxide interfaces, underscoring the importance of interfacial EPC in $T_\mathrm{g}$ enhancement. This FeSe/VO$_x$ interface also provides a platform for studying the interfacial superconductivity.

cond-mat.supr-con↗

arXiv2024

Generalized and high-efficiency arbitrary-positioned buffer for smoothed particle hydrodynamics

This paper develops an arbitrary-positioned buffer for the smoothed particle hydrodynamics (SPH) method, whose generality and high efficiency are achieved through two techniques. First, with the local coordinate system established at each arbitrary-positioned in-/outlet, particle positions in the global coordinate system are transformed into those in it via coordinate transformation. Since one local axis is located perpendicular to the in-/outlet boundary, the position comparison between particles and the threshold line or surface can be simplified to just this coordinate dimension. Second, particle candidates subjected to position comparison at one specific in-/outlet are restricted to those within the local cell-linked lists nearby the defined buffer zone, which significantly enhances computational efficiency due to a small portion of particles being checked. With this developed buffer, particle generation and deletion at arbitrary-positioned in- and outlets of complex flows can be handled efficiently and straightforwardly. We validate the effectiveness and versatility of the developed buffer through 2-D and 3-D non-/orthogonal uni-/bidirectional flows with arbitrary-positioned in- and outlets, driven by either pressure or velocity boundary conditions.

cs.CE↗

arXiv2024

Dynamical pressure boundary condition for weakly-compressible smoothed particle hydrodynamics

This paper introduces a novel dynamical pressure boundary condition for weakly-compressible smoothed particle hydrodynamics (WCSPH). Unlike previous methods that rely on indirect approaches or ghost particles, our method integrates the dynamical boundary pressure directly into the SPH approximation of the pressure gradient on near-boundary particles. Additionally, we develop a meshfree bidirectional in-/outflow buffer by periodically relabelling buffer particles at each time step, a concept that has not been explored before. This simple yet effective buffer facilitates the simulation of both uni- and bidirectional flows, especially those with mixed in-/outflow boundary conditions. We validate the accuracy and convergence of our method through benchmark cases with available analytical solutions. Furthermore, we demonstrate its versatility in hemodynamic simulations by investigating generic carotid and aorta flows with the Windkessel model, paving the way for studying the cardiovascular system within a unified meshfree computational framework.

physics.flu-dyn↗

arXiv2024

A generalized hybrid method for surfactant dynamics

In this paper, we develop a generalized hybrid method for both two-dimensional (2-D) and three-dimensional (3-D) surfactant dynamics. While the Navier-Stokes equations are solved by the Eulerian method, the surfactant transport is tracked by a Lagrangian particle method, in which the remeshing technique is employed to prevent particle clustering. For the mass redistribution during remeshing, the redistribution weight is selected with weighted least squares, which shares the theoretical basis of the moving least squares method (MLS) and enables the present hybrid method to work in both 2-D and 3-D cases. This optimized mass redistribution effectively strengthens the robustness of the present hybrid method, as validated by 2-D topological changes of the dumbbell. The conservation, accuracy, and convergence of the present hybrid method have also been validated with both 2-D and 3-D test cases, including a translation circle/sphere, a deformed circle/sphere in the shear flow, and droplet deformation.

physics.comp-ph↗

arXiv2024

Analysis of the particle relaxation method for generating uniform particle distributions in smoothed particle hydrodynamics

We establish a theoretical framework of the particle relaxation method for uniform particle generation of Smoothed Particle Hydrodynamics. We achieve this by reformulating the particle relaxation as an optimization problem. The objective function is an integral difference between discrete particle-based and smoothed-analytical volume fractions. The analysis demonstrates that the particle relaxation method in the domain interior is essentially equivalent to employing a gradient descent approach to solve this optimization problem, and we can extend such an equivalence to the bounded domain by introducing a proper boundary term. Additionally, each periodic particle distribution has a spatially uniform particle volume, denoted as characteristic volume. The relaxed particle distribution has the largest characteristic volume, and the kernel cut-off radius determines this volume. This insight enables us to control the relaxed particle distribution by selecting the target kernel cut-off radius for a given kernel function.

math.NA↗

arXiv2023

A diffusive wetting model for water entry/exit based on the weakly-compressible SPH method

This paper proposes a diffusive wetting model for the weakly-compressible smoothed particle hydrodynamics (WCSPH) method to simulate individual water entry/exit as well as the complete process from water entry to exit. The model is composed of a physically consistent diffusive wetting equation to describe the wetting evolution at the fluid-solid interface, a wetting-coupled identification approach to determine the type of fluid particles by taking into account the wetting degree of the contacted solid, and a numerical regularization on the fluid particles at fully wetted fluid-solid interface. The accuracy, efficiency, and versatility of the present model are validated through qualitative and quantitative comparisons with experiments, including the 3-D water entry of a sphere, the 2-D water entry/exit of a cylinder, and the complete process from water entry to exit of a 2-D cylinder.

physics.flu-dyn↗

arXiv2023

A 2D hybrid method for interfacial transport of passive scalars

A hybrid Eulerian-Lagrangian method is proposed to simulate passive scalar transport on arbitrary shape interface. In this method, interface deformation is tracked by an Eulerian method while the transport of the passive scalar on the material interface is solved by a single-layer Lagrangian particle method. To avoid particle clustering, a novel remeshing approach is proposed. This remeshing method can resample particles, adjust the position of particles by a relaxation process, and transfer mass from pre-existing particles to resampled particles via a redistribution process, which preserves mass both globally and locally. Computational costs are controlled by an adaptive remeshing strategy. Accuracy is assessed by a series of test cases.

physics.comp-ph↗

arXiv2023

Orientation-dependent electron-phonon coupling in interfacial superconductors LaAlO3/KTaO3

The emergent superconductivity at the LaAlO3/KTaO3 interfaces exhibits a mysterious dependence on the KTaO3 crystallographic orientations. Here we show, by soft X-ray angle-resolved photoemission spectroscopy, that the interfacial superconductivity is contributed by mobile electrons with unexpected quasi-three-dimensional character, beyond the "two-dimensional electron gas" scenario in describing oxide interfaces. At differently-oriented interfaces, the quasi-three-dimensional electron gas ubiquitously exists and spatially overlaps with the small q Fuchs-Kliewer surface phonons. Intriguingly, electrons and the Fuchs-Kliewer phonons couple with different strengths depending on the interfacial orientations, and the stronger coupling correlates with the higher superconducting transition temperature. Our results provide a natural explanation for the orientation-dependent superconductivity, and the first evidence that interfacial orientations can affect electron-phonon coupling strength over several nanometers, which may have profound implications for the applications of oxide interfaces in general.

cond-mat.supr-con↗

arXiv2022

A No-reference Quality Assessment Metric for Point Cloud Based on Captured Video Sequences

Point cloud is one of the most widely used digital formats of 3D models, the visual quality of which is quite sensitive to distortions such as downsampling, noise, and compression. To tackle the challenge of point cloud quality assessment (PCQA) in scenarios where reference is not available, we propose a no-reference quality assessment metric for colored point cloud based on captured video sequences. Specifically, three video sequences are obtained by rotating the camera around the point cloud through three specific orbits. The video sequences not only contain the static views but also include the multi-frame temporal information, which greatly helps understand the human perception of the point clouds. Then we modify the ResNet3D as the feature extraction model to learn the correlation between the capture videos and corresponding subjective quality scores. The experimental results show that our method outperforms most of the state-of-the-art full-reference and no-reference PCQA metrics, which validates the effectiveness of the proposed method.

eess.IV↗

arXiv2020

Histopathological imaging features- versus molecular measurements-based cancer prognosis modeling

For most if not all cancers, prognosis is of significant importance, and extensive modeling research has been conducted. With the genetic nature of cancer, in the past two decades, multiple types of molecular data (such as gene expressions and DNA mutations) have been explored. More recently, histopathological imaging data, which is routinely collected in biopsy, has been shown as informative for modeling prognosis. In this study, using the TCGA LUAD and LUSC data as a showcase, we examine and compare modeling lung cancer overall survival using gene expressions versus histopathological imaging features. High-dimensional regularization methods are adopted for estimation and selection. Our analysis shows that gene expressions have slightly better prognostic performance. In addition, most of the gene expressions are found to be weakly correlated imaging features. It is expected that this study can provide some insight into utilizing the two types of important data in cancer prognosis modeling and into lung cancer overall survival.

stat.AP↗

arXiv2019

Hyper-Sphere Quantization: Communication-Efficient SGD for Federated Learning

The high cost of communicating gradients is a major bottleneck for federated learning, as the bandwidth of the participating user devices is limited. Existing gradient compression algorithms are mainly designed for data centers with high-speed network and achieve $O(\sqrt{d} \log d)$ per-iteration communication cost at best, where $d$ is the size of the model. We propose hyper-sphere quantization (HSQ), a general framework that can be configured to achieve a continuum of trade-offs between communication efficiency and gradient accuracy. In particular, at the high compression ratio end, HSQ provides a low per-iteration communication cost of $O(\log d)$, which is favorable for federated learning. We prove the convergence of HSQ theoretically and show by experiments that HSQ significantly reduces the communication cost of model training without hurting convergence accuracy.

cs.LG↗

arXiv2018

Can Image Enhancement be Beneficial to Find Smoke Images in Laparoscopic Surgery?

Laparoscopic surgery has a limited field of view. Laser ablation in a laproscopic surgery causes smoke, which inevitably influences the surgeon's visibility. Therefore, it is of vital importance to remove the smoke, such that a clear visualization is possible. In order to employ a desmoking technique, one needs to know beforehand if the image contains smoke or not, to this date, there exists no accurate method that could classify the smoke/non-smoke images completely. In this work, we propose a new enhancement method which enhances the informative details in the RGB images for discrimination of smoke/non-smoke images. Our proposed method utilizes weighted least squares optimization framework~(WLS). For feature extraction, we use statistical features based on bivariate histogram distribution of gradient magnitude~(GM) and Laplacian of Gaussian~(LoG). We then train a SVM classifier with binary smoke/non-smoke classification task. We demonstrate the effectiveness of our method on Cholec80 dataset. Experiments using our proposed enhancement method show promising results with improvements of 4\% in accuracy and 4\% in F1-Score over the baseline performance of RGB images. In addition, our approach improves over the saturation histogram based classification methodologies Saturation Analysis~(SAN) and Saturation Peak Analysis~(SPA) by 1/5\% and 1/6\% in accuracy/F1-Score metrics.

cs.CV↗

arXiv2012

Learning Continuous-Time Social Network Dynamics

We demonstrate that a number of sociology models for social network dynamics can be viewed as continuous time Bayesian networks (CTBNs). A sampling-based approximate inference method for CTBNs can be used as the basis of an expectation-maximization procedure that achieves better accuracy in estimating the parameters of the model than the standard method of moments algorithmfromthe sociology literature. We extend the existing social network models to allow for indirect and asynchronous observations of the links. A Markov chain Monte Carlo sampling algorithm for this new model permits estimation and inference. We provide results on both a synthetic network (for verification) and real social network data.

cs.SI↗