arXiv subjects

Hui Zhang

Publications and source records attributed to Hui Zhang.

At least 325 records · Page 18Linked to original sources

arXiv2023

Planetesimal Growth in Evolving Protoplanetary Disks: Constraints from the Pebble Supply

In the core accretion model, planetesimals grow by mutual collisions and engulfing millimeter-to-centimeter particles, i.e., pebbles. Pebble accretion can significantly increase the accretion efficiency and help explain the presence of planets on wide orbits. However, the pebble supply is typically parameterized as a coherent pebble mass flux, sometimes being constant in space and time. Here we solve the dust advection and diffusion within viciously evolving protoplanetary disks to determine the pebble supply self-consistently. The pebbles are then accreted by planetesimals interacting with the gas disk via gas drag and gravitational torque. The pebble supply is variable with space and decays with time quickly, with a pebble flux below 10 $M_\oplus$ Myr$^{-1}$ after 1 Myr in our models. As a result, only when massive planetesimals ($>$ 0.01 $M_\oplus$) are luckily produced by the streaming instability or the disk has low viscosity ($α\sim 0.0001$) can the herd of planetesimals grow over a Mars mass within 2 Myr. By then, planetesimals only capture pebbles about 50 times their mass and as little as 10 times beyond 20 au due to limited pebble supply. Further studies considering multiple dust species in various disk conditions are warranted to fully assess the realistic pebble supply and its influence on planetesimal growth.

astro-ph.EP↗

arXiv2023

Integrated motion control and energy management of series hybrid electric vehicles: A multi-objective MPC approach

This paper considers the integrated motion control and energy management problems of the series hybrid electric vehicles (SHEV) with constraints. We propose a multi-objective model predictive control (MOMPC)-based energy management approach, which is embedded with the motion control to guarantee driving comfort. In addition, due to the slow response of the engine, it may cause excessive batter power when HEVs work in different conditions (e.g., uphill or sudden acceleration) with a certain request power; this implies the discharge current is too large. A battery current constraint is designed and incorporated into the MOMPC optimization problem and hence avoids the extra high charge-discharge current. This prevents potential safety hazards and extends the battery's life. Finally, numerical experiments are performed to verify the proposed approach.

math.OC↗

arXiv2023

Production of $X_{cs\bar{c}\bar{s}}$ in heavy ion collisions

The yields of $X_{cs\bar{c}\bar{s}}$ with its two possible configurations, i.e., the hadronic molecular state and tetraquark state, for Pb-Pb collisions at $\sqrt{s_{NN}}=5.02~\rm{TeV}$ is studied. A volume effect is found from the centrality distribution of $X_{cs\bar{c}\bar{s}}$, which could help to distinguish the inner structure of $X_{cs\bar{c}\bar{s}}$. We also show the rapidity and the transverse momentum distributions of $X_{cs\bar{c}\bar{s}}$ production as well as its elliptic flow coefficient as a function of the transverse momentum.

hep-ph↗

arXiv2023

Focused and Collaborative Feedback Integration for Interactive Image Segmentation

Interactive image segmentation aims at obtaining a segmentation mask for an image using simple user annotations. During each round of interaction, the segmentation result from the previous round serves as feedback to guide the user's annotation and provides dense prior information for the segmentation model, effectively acting as a bridge between interactions. Existing methods overlook the importance of feedback or simply concatenate it with the original input, leading to underutilization of feedback and an increase in the number of required annotations. To address this, we propose an approach called Focused and Collaborative Feedback Integration (FCFI) to fully exploit the feedback for click-based interactive image segmentation. FCFI first focuses on a local area around the new click and corrects the feedback based on the similarities of high-level features. It then alternately and collaboratively updates the feedback and deep features to integrate the feedback into the features. The efficacy and efficiency of FCFI were validated on four benchmarks, namely GrabCut, Berkeley, SBD, and DAVIS. Experimental results show that FCFI achieved new state-of-the-art performance with less computational overhead than previous methods. The source code is available at https://github.com/veizgyauzgyauz/FCFI.

cs.CV↗

arXiv2023

One Size Cannot Fit All: a Self-Adaptive Dispatcher for Skewed Hash Join in Shared-nothing RDBMSs

Shared-nothing architecture has been widely adopted in various commercial distributed RDBMSs. Thanks to the architecture, query can be processed in parallel and accelerated by scaling up the cluster horizontally on demand. In spite of that, load balancing has been a challenging issue in all distributed RDBMSs, including shared-nothing ones, which suffers much from skewed data distribution. In this work, we focus on one of the representative operator, namely Hash Join, and investigate how skewness among the nodes of a cluster will affect the load balance and eventual efficiency of an arbitrary query in shared-nothing RDBMSs. We found that existing Distributed Hash Join (Dist-HJ) solutions may not provide satisfactory performance when a value is skewed in both the probe and build tables. To address that, we propose a novel Dist-HJ solution, namely Partition and Replication (PnR). Although PnR provide the best efficiency in some skewness scenario, our exhaustive experiments over a group of shared-nothing RDBMSs show that there is not a single Dist-HJ solution that wins in all (data skew) scenarios. To this end, we further propose a self-adaptive Dist-HJ solution with a builtin sub-operator cost model that dynamically select the best Dist-HJ implementation strategy at runtime according to the data skew of the target query. We implement the solution in our commercial shared-nothing RDBMSs, namely KaiwuDB (former name ZNBase) and empirical study justifies that the self-adaptive model achieves the best performance comparing to a series of solution adopted in many existing RDBMSs.

cs.DB↗

arXiv2023

Resolving quantitative MRI model degeneracy with machine learning via training data distribution design

Quantitative MRI (qMRI) aims to map tissue properties non-invasively via models that relate these unknown quantities to measured MRI signals. Estimating these unknowns, which has traditionally required model fitting - an often iterative procedure, can now be done with one-shot machine learning (ML) approaches. Such parameter estimation may be complicated by intrinsic qMRI signal model degeneracy: different combinations of tissue properties produce the same signal. Despite their many advantages, it remains unclear whether ML approaches can resolve this issue. Growing empirical evidence appears to suggest ML approaches remain susceptible to model degeneracy. Here we demonstrate under the right circumstances ML can address this issue. Inspired by recent works on the impact of training data distributions on ML-based parameter estimation, we propose to resolve model degeneracy by designing training data distributions. We put forward a classification of model degeneracies and identify one particular kind of degeneracies amenable to the proposed attack. The strategy is demonstrated successfully using the Revised NODDI model with standard multi-shell diffusion MRI data as an exemplar. Our results illustrate the importance of training set design which has the potential to allow accurate estimation of tissue properties with ML.

physics.med-ph↗

arXiv2023

The moment map for the variety of Leibniz algebras

We consider the moment map $m:\mathbb{P}V_n\rightarrow \text{i}\mathfrak{u}(n)$ for the action of $\text{GL}(n)$ on $V_n=\otimes^{2}(\mathbb{C}^{n})^{*}\otimes\mathbb{C}^{n}$, and study the functional $F_n=\|m\|^{2}$ restricted to the projectivizations of the algebraic varieties of all $n$-dimensional Leibniz algebras $L_n$ and all $n$-dimensional symmetric Leibniz algebras $S_n$, respectively. Firstly, we give a description of the maxima and minima of the functional $F_n: L_n \rightarrow \mathbb{R}$, proving that they are actually attained at the symmetric Leibniz algebras. Then, for an arbitrary critical point $[μ]$ of $F_n: S_n \rightarrow \mathbb{R}$, we characterize the structure of $[μ]$ by virtue of the nonnegative rationality. Finally, we classify the critical points of $F_n: S_n \rightarrow \mathbb{R}$ for $n=2$, $3$, respectively.

math.RA↗

arXiv2023

Electron-irradiation-facilitated production of chemically homogenized nanotwins in nanolaminated carbides

Twin boundaries have been exploited to stabilize ultrafine grains and improve the mechanical properties of nanomaterials. The production of twin boundaries and nanotwins is however prohibitively challenging in carbide ceramics. Using scanning transmission electron microscopes as a unique platform for atomic-scale structure engineering, we demonstrate that twin platelets could be produced in carbides by engineering antisite defects. Antisite defects at metal sites in various layered ternary carbides are collectively and controllably generated and the metal elements are homogenized by electron irradiation, which transforms the twin-like lamellae into nanotwin platelets. Accompanying the chemical homogenization, α-Ti3AlC2 transforms to unconventional \b{eta}-Ti3AlC2. The chemical homogeneity and the width of the twin platelets can be tuned by the dose and energy of bombarding electrons. Chemically homogenized nanotwins can boost the hardness by ~45%. Our results provide a new way to produce ultrathin (<5 nm) nanotwin platelets in scientifically and technologically important carbide materials and showcase the feasibility of defect engineering by an angstrom-sized electron probe.

cond-mat.mtrl-sci↗

arXiv2023

The moment map for the variety of associative algebras

We consider the moment map $m:\mathbb{P}V_n\rightarrow \text{i}\mathfrak{u}(n)$ for the action of $\text{GL}(n)$ on $V_n=\otimes^{2}(\mathbb{C}^{n})^{*}\otimes\mathbb{C}^{n}$, and study the critical points of the functional $F_n=\|m\|^{2}: \mathbb{P} V_n \rightarrow \mathbb{R}$. Firstly, we prove that $[μ]\in \mathbb{P}V_n$ is a critical point if and only if $\text{M}_μ=c_μ I+D_μ$ for some $c_μ \in \mathbb{R}$ and $D_μ \in \text{Der}(μ),$ where $m([μ])=\frac{\text{M}_μ}{\|μ\|^{2}}$. Then we show that any algebra $μ$ admits a Nikolayevsky derivation $ϕ_μ$ which is unique up to automorphism, and if moreover, $[μ]$ is a critical point of $F_n$, then $ϕ_μ=-\frac{1}{c_μ}D_μ.$ Secondly, we characterize the maxima and minima of the functional $F_n: \mathcal{A}_n \rightarrow \mathbb{R}$, where $\mathcal{A}_n$ denotes the projectivization of the algebraic varieties of all $n$-dimensional associative algebras. Furthermore, for an arbitrary critical point $[μ]$ of $F_n: \mathcal{A}_n \rightarrow \mathbb{R}$, we also obtain a description of the algebraic structure of $[μ]$. Finally, we classify the critical points of $F_n: \mathcal{A}_n \rightarrow \mathbb{R}$ for $n=2$, $3$, respectively.

math.DG↗

arXiv2023

Machine-learning-informed parameter estimation improves the reliability of spinal cord diffusion MRI

Purpose: We address the challenge of inaccurate parameter estimation in diffusion MRI when the signal-to-noise ratio (SNR) is very low, as in the spinal cord. The accuracy of conventional maximum-likelihood estimation (MLE) depends highly on initialisation. Unfavourable choices could result in suboptimal parameter estimates. Current methods to address this issue, such as grid search (GS) can increase computation time substantially. Methods: We propose a machine learning (ML) informed MLE approach that combines conventional MLE with ML approaches synergistically. ML-based methods have been developed recently to improve the speed and precision of parameter estimation. However, they can generate high systematic bias in estimated parameters when SNR is low. In the proposed ML-MLE approach, an artificial neural network model is trained to provide sensible initialisation for MLE efficiently, with the final solution determined by MLE, avoiding biases typically affecting pure ML estimations. Results: Using parameter estimation of neurite orientation dispersion and density imaging as an example, simulation and in vivo experiments suggest that the ML-MLE method can reduce outlier estimates from conventional MLE in white matter voxels affected by CSF contamination. It also accelerates computation compared to GS-MLE. Conclusion: The ML-MLE method can improve the reliability of parameter estimation with reduced computation time compared to GS-MLE, making it a practical tool for diffusion dataset with low SNR.

physics.med-ph↗

arXiv2022

Mirror frameworks for relatively Lipschitz and monotone-like variational inequalities

Nonconvex-nonconcave saddle-point optimization in machine learning has triggered lots of research for studying non-monotone variational inequalities (VI). In this work, we introduce two mirror frameworks, called mirror extragradient method and mirror extrapolation method, for approximating solutions to relatively Lipschitz and monotone-like VIs. The former covers the well-known Nemirovski's mirror prox method and Nesterov's dual extrapolation method, and the recently proposed Bregman extragradient method; all of them can be reformulated into a scheme that is very similar to the original form of extragradient method. The latter includes the operator extrapolation method and the Bregman extrapolation method as its special cases. The proposed mirror frameworks allow us to present a unified and improved convergence analysis for all these existing methods under relative Lipschitzness and monotone-like conditions that may be the currently weakest assumptions.

math.OC↗

arXiv2022

Personalized local heating neutralizing individual, spatial and temporal thermo-physiological variances in extreme cold environments

In this paper, we investigate the feasibility, robustness and optimization of introducing personal comfort systems (PCS), apparatuses that promises in energy saving and comfort improvement, into a broader range of environments. We report a series of laboratory experiments systematically examining the effect of personalized heating in neutralizing individual, spatial and temporal variations of thermal demands. The experiments were conducted in an artificial climate chamber at -15 degC in order to simulate extreme cold environments. We developed a heating garment with 20 pieces of 20 * 20 cm2 heating cloth (grouped into 9 regions) comprehensively covering human body. Surface temperatures of the garment can be controlled independently, quickly (within 20 seconds), precisely (within 1 degC) and easily (through a tablet) up to 45 degC. Participants were instructed to adjust surface temperatures of each segment to their preferences, with their physiological, psychological and adjustment data collected. We found that active heating could significantly and stably improve thermal satisfaction. The overall TSV and TCV were improved 1.50 and 1.53 during the self-adjustment phase. Preferred heating surface temperatures for different segments varied widely. Further, even for the same segment, individual differences among participants were considerable. Such variances were observed through local heating powers, while unnoticeable among thermal perception votes. In other words, all these various differences could be neutralized given the flexibility in personalized adjustments. Our research reaffirms the paradigm of "adaptive thermal comfort" and will promote innovations on human-centric design for more efficient PCSs.

cs.HC↗

arXiv2022

A photonic chip-based machine learning approach for the prediction of molecular properties

Machine learning methods have revolutionized the discovery process of new molecules and materials. However, the intensive training process of neural networks for molecules with ever-increasing complexity has resulted in exponential growth in computation cost, leading to long simulation time and high energy consumption. Photonic chip technology offers an alternative platform for implementing neural networks with faster data processing and lower energy usage compared to digital computers. Photonics technology is naturally capable of implementing complex-valued neural networks at no additional hardware cost. Here, we demonstrate the capability of photonic neural networks for predicting the quantum mechanical properties of molecules. To the best of our knowledge, this work is the first to harness photonic technology for machine learning applications in computational chemistry and molecular sciences, such as drug discovery and materials design. We further show that multiple properties can be learned simultaneously in a photonic chip via a multi-task regression learning algorithm, which is also the first of its kind as well, as most previous works focus on implementing a network in the classification task.

cs.ET↗

arXiv2022

On proximal gradient mapping and its minimization in norm via potential function-based acceleration

The proximal gradient descent method, well-known for composite optimization, can be completely described by the concept of proximal gradient mapping. In this paper, we highlight our previous two discoveries of proximal gradient mapping--norm monotonicity and refined descent, with which we are able to extend the recently proposed potential function-based framework from gradient descent to proximal gradient descent.

math.OC↗

arXiv2022

Recycling of Perovskite Substrate

The use of water-soluble sacrificial layer of Sr$_3$Al$_2$O$_6$ has tremendously boosted the research on freestanding functional oxide thin films, especially thanks to its ultimate capability to produce high-quality epitaxial perovskite thin films. However, the costly single-crystalline substrates, e.g. SrTiO$_3$, were generally discarded after obtaining the freestanding thin films. Here, we demonstrate that the SrTiO$_3$ substrates can be recycled to fabricate La$_{0.7}$Sr$_{0.3}$MnO$_3$ films with nearly identical structural and electrical properties. After attaining freestanding thin films, the residues on SrTiO$_3$ can be removed by 80 \degree C hot water soaking and rinsing treatments. Consequently, the surface of SrTiO$_3$ reverted to its original step-and-terrace structure.

physics.app-ph↗

arXiv2022

Fast method and convergence analysis of fractional magnetohydrodynamic coupled flow and heat transfer model for generalized second-grade fluid

In this paper, we first establish a new fractional magnetohydrodynamic (MHD) coupled flow and heat transfer model for a generalized second-grade fluid. This coupled model consists of a fractional momentum equation and a heat conduction equation with a generalized form of Fourier law. The second-order fractional backward difference formula is applied to the temporal discretization and the Legendre spectral method is used for the spatial discretization. The fully discrete scheme is proved to be stable and convergent with an accuracy of $O(τ^2+N^{-r})$, where $τ$ is the time step size and $N$ is the polynomial degree. To reduce the memory requirements and computational cost, a fast method is developed, which is based on a globally uniform approximation of the trapezoidal rule for integrals on the real line. And the strict convergence of the numerical scheme with this fast method is proved. We present the results of several numerical experiments to verify the effectiveness of the proposed method. Finally, we simulate the unsteady fractional MHD flow and heat transfer of the generalized second-grade fluid through a porous medium. The effects of the relevant parameters on the velocity and temperature are presented and analyzed in detail.

math.NA↗

arXiv2022

Electronic properties of monolayer copper selenide with one-dimensional moiré patterns

Strain engineering is a vital way to manipulate the electronic properties of two-dimensional (2D) materials. As a typical representative of transition metal mono-chalcogenides (TMMs), a honeycomb CuSe monolayer features with one-dimensional (1D) moiré patterns owing to the uniaxial strain along one of three equivalent orientations of Cu(111) substrates. Here, by combining low-temperature scanning tunneling microscopy/spectroscopy (STM/S) experiments and density functional theory (DFT) calculations, we systematically investigate the electronic properties of the strained CuSe monolayer on the Cu(111) substrate. Our results show the semiconducting feature of CuSe monolayer with a band gap of 1.28 eV and the 1D periodical modulation of electronic properties by the 1D moiré patterns. Except for the uniaxially strained CuSe monolayer, we observed domain boundary and line defects in the CuSe monolayer, where the biaxial-strain and strain-free conditions can be investigated respectively. STS measurements for the three different strain regions show that the first peak in conduction band will move downward with the increasing strain. DFT calculations based on the three CuSe atomic models with different strain inside reproduced the peak movement. The present findings not only enrich the fundamental comprehension toward the influence of strain on electronic properties at 2D limit, but also offer the benchmark for the development of 2D semiconductor materials.

cond-mat.mtrl-sci↗

arXiv2022

Self-Regularized Prototypical Network for Few-Shot Semantic Segmentation

The deep CNNs in image semantic segmentation typically require a large number of densely-annotated images for training and have difficulties in generalizing to unseen object categories. Therefore, few-shot segmentation has been developed to perform segmentation with just a few annotated examples. In this work, we tackle the few-shot segmentation using a self-regularized prototypical network (SRPNet) based on prototype extraction for better utilization of the support information. The proposed SRPNet extracts class-specific prototype representations from support images and generates segmentation masks for query images by a distance metric - the fidelity. A direct yet effective prototype regularization on support set is proposed in SRPNet, in which the generated prototypes are evaluated and regularized on the support set itself. The extent to which the generated prototypes restore the support mask imposes an upper limit on performance. The performance on the query set should never exceed the upper limit no matter how complete the knowledge is generalized from support set to query set. With the specific prototype regularization, SRPNet fully exploits knowledge from the support and offers high-quality prototypes that are representative for each semantic class and meanwhile discriminative for different classes. The query performance is further improved by an iterative query inference (IQI) module that combines a set of regularized prototypes. Our proposed SRPNet achieves new state-of-art performance on 1-shot and 5-shot segmentation benchmarks.

cs.CV↗