arXiv subjects

Hui Zhang

Publications and source records attributed to Hui Zhang.

At least 307 records · Page 17Linked to original sources

arXiv2023

Rician likelihood loss for quantitative MRI using self-supervised deep learning

Purpose: Previous quantitative MR imaging studies using self-supervised deep learning have reported biased parameter estimates at low SNR. Such systematic errors arise from the choice of Mean Squared Error (MSE) loss function for network training, which is incompatible with Rician-distributed MR magnitude signals. To address this issue, we introduce the negative log Rician likelihood (NLR) loss. Methods: A numerically stable and accurate implementation of the NLR loss was developed to estimate quantitative parameters of the apparent diffusion coefficient (ADC) model and intra-voxel incoherent motion (IVIM) model. Parameter estimation accuracy, precision and overall error were evaluated in terms of bias, variance and root mean squared error and compared against the MSE loss over a range of SNRs (5 - 30). Results: Networks trained with NLR loss show higher estimation accuracy than MSE for the ADC and IVIM diffusion coefficients as SNR decreases, with minimal loss of precision or total error. At high effective SNR (high SNR and small diffusion coefficients), both losses show comparable accuracy and precision for all parameters of both models. Conclusion: The proposed NLR loss is numerically stable and accurate across the full range of tested SNRs and improves parameter estimation accuracy of diffusion coefficients using self-supervised deep learning. We expect the development to benefit quantitative MR imaging techniques broadly, enabling more accurate parameter estimation from noisy data.

cs.LG↗

arXiv2023

DRMC: A Generalist Model with Dynamic Routing for Multi-Center PET Image Synthesis

Multi-center positron emission tomography (PET) image synthesis aims at recovering low-dose PET images from multiple different centers. The generalizability of existing methods can still be suboptimal for a multi-center study due to domain shifts, which result from non-identical data distribution among centers with different imaging systems/protocols. While some approaches address domain shifts by training specialized models for each center, they are parameter inefficient and do not well exploit the shared knowledge across centers. To address this, we develop a generalist model that shares architecture and parameters across centers to utilize the shared knowledge. However, the generalist model can suffer from the center interference issue, \textit{i.e.} the gradient directions of different centers can be inconsistent or even opposite owing to the non-identical data distribution. To mitigate such interference, we introduce a novel dynamic routing strategy with cross-layer connections that routes data from different centers to different experts. Experiments show that our generalist model with dynamic routing (DRMC) exhibits excellent generalizability across centers. Code and data are available at: https://github.com/Yaziwel/Multi-Center-PET-Image-Synthesis.

eess.IV↗

arXiv2023

SSC3OD: Sparsely Supervised Collaborative 3D Object Detection from LiDAR Point Clouds

Collaborative 3D object detection, with its improved interaction advantage among multiple agents, has been widely explored in autonomous driving. However, existing collaborative 3D object detectors in a fully supervised paradigm heavily rely on large-scale annotated 3D bounding boxes, which is labor-intensive and time-consuming. To tackle this issue, we propose a sparsely supervised collaborative 3D object detection framework SSC3OD, which only requires each agent to randomly label one object in the scene. Specifically, this model consists of two novel components, i.e., the pillar-based masked autoencoder (Pillar-MAE) and the instance mining module. The Pillar-MAE module aims to reason over high-level semantics in a self-supervised manner, and the instance mining module generates high-quality pseudo labels for collaborative detectors online. By introducing these simple yet effective mechanisms, the proposed SSC3OD can alleviate the adverse impacts of incomplete annotations. We generate sparse labels based on collaborative perception datasets to evaluate our method. Extensive experiments on three large-scale datasets reveal that our proposed SSC3OD can effectively improve the performance of sparsely supervised collaborative 3D object detectors.

cs.CV↗

arXiv2023

Patch-CNN: Training data-efficient deep learning for high-fidelity diffusion tensor estimation from minimal diffusion protocols

We propose a new method, Patch-CNN, for diffusion tensor (DT) estimation from only six-direction diffusion weighted images (DWI). Deep learning-based methods have been recently proposed for dMRI parameter estimation, using either voxel-wise fully-connected neural networks (FCN) or image-wise convolutional neural networks (CNN). In the acute clinical context -- where pressure of time limits the number of imaged directions to a minimum -- existing approaches either require an infeasible number of training images volumes (image-wise CNNs), or do not estimate the fibre orientations (voxel-wise FCNs) required for tractogram estimation. To overcome these limitations, we propose Patch-CNN, a neural network with a minimal (non-voxel-wise) convolutional kernel (3$\times$3$\times$3). Compared with voxel-wise FCNs, this has the advantage of allowing the network to leverage local anatomical information. Compared with image-wise CNNs, the minimal kernel vastly reduces training data demand. Evaluated against both conventional model fitting and a voxel-wise FCN, Patch-CNN, trained with a single subject is shown to improve the estimation of both scalar dMRI parameters and fibre orientation from six-direction DWIs. The improved fibre orientation estimation is shown to produce improved tractogram.

cs.CV↗

arXiv2023

Combining Self-Supervised and Supervised Learning with Noisy Labels

Since convolutional neural networks (CNNs) can easily overfit noisy labels, which are ubiquitous in visual classification tasks, it has been a great challenge to train CNNs against them robustly. Various methods have been proposed for this challenge. However, none of them pay attention to the difference between representation and classifier learning of CNNs. Thus, inspired by the observation that classifier is more robust to noisy labels while representation is much more fragile, and by the recent advances of self-supervised representation learning (SSRL) technologies, we design a new method, i.e., CS$^3$NL, to obtain representation by SSRL without labels and train the classifier directly with noisy labels. Extensive experiments are performed on both synthetic and real benchmark datasets. Results demonstrate that the proposed method can beat the state-of-the-art ones by a large margin, especially under a high noisy level.

cs.CV↗

arXiv2023

A Weighted Randomized Sparse Kaczmarz Method for Solving Linear Systems

The randomized sparse Kaczmarz method, designed for seeking the sparse solutions of the linear systems $Ax=b$, selects the $i$-th projection hyperplane with likelihood proportional to $\|a_{i}\|_2^2$, where $a_{i}^T$ is $i$-th row of $A$. In this work, we propose a weighted randomized sparse Kaczmarz method, which selects the $i$-th projection hyperplane with probability proportional to $\lvert\langle a_{i},x_{k}\rangle-b_{i}\rvert^p$, where $0<p<\infty$, for possible acceleration. It bridges the randomized Kaczmarz and greedy Kaczmarz by parameter $p$. Theoretically, we show its linear convergence rate in expectation with respect to the Bregman distance in the noiseless and noisy cases, which is at least as efficient as the randomized sparse Kaczmarz method. The superiority of the proposed method is demonstrated via a group of numerical experiments.

math.NA↗

arXiv2023

When SAM Meets Shadow Detection

As a promptable generic object segmentation model, segment anything model (SAM) has recently attracted significant attention, and also demonstrates its powerful performance. Nevertheless, it still meets its Waterloo when encountering several tasks, e.g., medical image segmentation, camouflaged object detection, etc. In this report, we try SAM on an unexplored popular task: shadow detection. Specifically, four benchmarks were chosen and evaluated with widely used metrics. The experimental results show that the performance for shadow detection using SAM is not satisfactory, especially when comparing with the elaborate models. Code is available at https://github.com/LeipingJie/SAMSh.

cs.CV↗

arXiv2023

Three-dimensional structural and compositional inhomogeneity in zeolites unraveled by low-dose electron ptychography

Structural and compositional inhomogeneity is common in zeolites and considerably affects their properties. Conventional transmission electron microscopy (TEM) cannot provide sufficient information on local structures in zeolites due to the thickness-limited lateral resolution, lack of depth resolution, and electron dose-constrained focusing. We demonstrate that a multislice ptychography method based on four-dimensional scanning TEM (4D-STEM) data can overcome these limitations. The 4D-STEM ptychography image obtained from a ~40-nm thick MFI zeolite exhibits an ultrahigh lateral resolution of ~0.85 Å, enabling the unambiguous identification of individual framework oxygen (O) atoms and the precise determination of the orientations of adsorbed molecules. Furthermore, a depth resolution of ~6.6 nm is achieved, allowing the three-dimensional probing of O vacancies and phase boundaries in zeolites. The results of the 4D-STEM ptychography provide an unprecedented complete understanding of the intergrowth of MFI and MEL zeolites in three dimensions. The 4D-STEM ptychography can be generally applied to various zeolites and other materials with similar electron beam sensitivity.

cond-mat.mtrl-sci↗

arXiv2023

Prototypical Residual Networks for Anomaly Detection and Localization

Anomaly detection and localization are widely used in industrial manufacturing for its efficiency and effectiveness. Anomalies are rare and hard to collect and supervised models easily over-fit to these seen anomalies with a handful of abnormal samples, producing unsatisfactory performance. On the other hand, anomalies are typically subtle, hard to discern, and of various appearance, making it difficult to detect anomalies and let alone locate anomalous regions. To address these issues, we propose a framework called Prototypical Residual Network (PRN), which learns feature residuals of varying scales and sizes between anomalous and normal patterns to accurately reconstruct the segmentation maps of anomalous regions. PRN mainly consists of two parts: multi-scale prototypes that explicitly represent the residual features of anomalies to normal patterns; a multisize self-attention mechanism that enables variable-sized anomalous feature learning. Besides, we present a variety of anomaly generation strategies that consider both seen and unseen appearance variance to enlarge and diversify anomalies. Extensive experiments on the challenging and widely used MVTec AD benchmark show that PRN outperforms current state-of-the-art unsupervised and supervised methods. We further report SOTA results on three additional datasets to demonstrate the effectiveness and generalizability of PRN.

cs.CV↗

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↗