arXiv subjects

Jun Hu

Publications and source records attributed to Jun Hu.

At least 109 records · Page 6Linked to original sources

arXiv2024

Analysis of fluid flow in fractal microfluidic channels

Fractal channels have significant applications in fields such as microfluidic chips and in vitro diagnostics. However, there is currently insufficient understanding and recognition of fluid flow within fractal channels. In this paper, the fluid flow within dendritic fractal structures was studied with finite element analysis (FEA), focusing on fluid velocity and mass fraction variations. The research introduces the ratio RA (the ratio of the longitudinal channel length to the lateral channel length) as a key parameter to evaluate fractal structures. A smaller RA corresponds to a flatter fractal configuration. By comparing flow rate, velocity, and mass distribution at the outlet of fractal channels with varying RA values, the results demonstrate that flatter fractal structures lead to more uniform flow distribution at the outlet. Meanwhile, the uniformity of fluid flow at the outlet of the fractal channels was analyzed. This work indicates that channel geometry significantly influences fluid dynamics, and also provides valuable insights into optimizing flow dynamics in small-scale applications, contributing to the design of more efficient fluidic devices.

physics.flu-dyn↗

arXiv2024

First-principles study of electronic and magnetic properties of Fe atoms on Cu2N/Cu(100)

First-principles calculations were conducted to investigate the structural, electronic and magnetic properties of single Fe atoms and Fe dimers on Cu2N/Cu(100). Upon adsorption of an Fe atom onto Cu2N/Cu(100), robust Fe-N bonds form, resulting in the incorporation of both single Fe atoms and Fe dimers within the surface Cu2N layer. The partial occupancy of Fe-3d orbitals lead to large spin moments on the Fe atoms. Interestingly, both single Fe atoms and Fe dimers exhibit in-plane magnetic anisotropy, with the magnetic anisotropy energy (MAE) of an Fe dimer exceeding twice that of a single Fe atom. This magnetic anisotropy can be attributed to the predominant contribution of the component along the x direction of the spin-orbital coupling Hamiltonian. Additionally, the formation of Fe-Cu dimers may further boost the magnetic anisotropy, as the energy levels of the Fe-3d orbitals are remarkably influenced by the presence of Cu atoms. Our study manifests the significance of uncovering the origin of magnetic anisotropy in engineering the magnetic properties of magnetic nanostructures.

cond-mat.mtrl-sci↗

arXiv2024

Multi-degree-of-freedom hybrid optical skyrmions

The optical counterparts of skyrmions have recently been constructed with diverse topological types and by different degrees of freedom, such as field, spins, and Stokes vectors, exhibiting extensive potential in modern information science. However, there is currently no method capable of generating multiple types of optical skyrmions in free space. Here, we present a simple approach for realizing hybrid optical skyrmions of electric field vectors, spin angular momentum and Stokes vectors in a same structured light field. We show that a vector beam truncated by an annular aperture can form an electric field skyrmion in the diffracted light field. In the meantime, electric field meron pairs, spin skyrmions and Stokes skyrmions can be generated by tuning spin-orbital coupling of the incident light.

physics.optics↗

arXiv2024

On Hecke algebras and $Z$-graded twisting, Shuffling and Zuckerman functors

Let $g$ be a complex semisimple Lie algebra with Weyl group $W$. Let $H(W)$ be the Iwahori-Hecke algebra associated to $W$. For each $w\in W$, let $T_w$ and $C_w$ be the corresponding $Z$-graded twisting functor and $Z$-graded shuffling functor respectively. In this paper we present a categorical action of $H(W)$ on the derived category $D^b(O_0^Z)$ of the $Z$-graded BGG category $O_0^Z$ via derived twisting functors as well as a categorical action of $H(W)$ on $D^b(O_0^Z)$ via derived shuffling functors. As applications, we get graded character formulae for $T_sL(x)$ and $C_sL(x)$ for each simple reflection $s$. We describe the graded shifts occurring in the action of the $Z$-graded twisting and shuffling functors on dual Verma modules and simple modules. We also characterize the action of the derived $Z$-graded Zuckerman functors on simple modules.

math.RT↗

arXiv2024

TG-LMM: Enhancing Medical Image Segmentation Accuracy through Text-Guided Large Multi-Modal Model

We propose TG-LMM (Text-Guided Large Multi-Modal Model), a novel approach that leverages textual descriptions of organs to enhance segmentation accuracy in medical images. Existing medical image segmentation methods face several challenges: current medical automatic segmentation models do not effectively utilize prior knowledge, such as descriptions of organ locations; previous text-visual models focus on identifying the target rather than improving the segmentation accuracy; prior models attempt to use prior knowledge to enhance accuracy but do not incorporate pre-trained models. To address these issues, TG-LMM integrates prior knowledge, specifically expert descriptions of the spatial locations of organs, into the segmentation process. Our model utilizes pre-trained image and text encoders to reduce the number of training parameters and accelerate the training process. Additionally, we designed a comprehensive image-text information fusion structure to ensure thorough integration of the two modalities of data. We evaluated TG-LMM on three authoritative medical image datasets, encompassing the segmentation of various parts of the human body. Our method demonstrated superior performance compared to existing approaches, such as MedSAM, SAM and nnUnet.

cs.CV↗

arXiv2024

Efficient Heterogeneous Graph Learning via Random Projection

Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for deep learning on heterogeneous graphs. Typical HGNNs require repetitive message passing during training, limiting efficiency for large-scale real-world graphs. Recent pre-computation-based HGNNs use one-time message passing to transform a heterogeneous graph into regular-shaped tensors, enabling efficient mini-batch training. Existing pre-computation-based HGNNs can be mainly categorized into two styles, which differ in how much information loss is allowed and efficiency. We propose a hybrid pre-computation-based HGNN, named Random Projection Heterogeneous Graph Neural Network (RpHGNN), which combines the benefits of one style's efficiency with the low information loss of the other style. To achieve efficiency, the main framework of RpHGNN consists of propagate-then-update iterations, where we introduce a Random Projection Squashing step to ensure that complexity increases only linearly. To achieve low information loss, we introduce a Relation-wise Neighbor Collection component with an Even-odd Propagation Scheme, which aims to collect information from neighbors in a finer-grained way. Experimental results indicate that our approach achieves state-of-the-art results on seven small and large benchmark datasets while also being 230% faster compared to the most effective baseline. Surprisingly, our approach not only surpasses pre-processing-based baselines but also outperforms end-to-end methods.

cs.LG↗

arXiv2024

Neural Graph Matching for Video Retrieval in Large-Scale Video-driven E-commerce

With the rapid development of the short video industry, traditional e-commerce has encountered a new paradigm, video-driven e-commerce, which leverages attractive videos for product showcases and provides both video and item services for users. Benefitting from the dynamic and visualized introduction of items,video-driven e-commerce has shown huge potential in stimulating consumer confidence and promoting sales. In this paper, we focus on the video retrieval task, facing the following challenges: (1) Howto handle the heterogeneities among users, items, and videos? (2)How to mine the complementarity between items and videos for better user understanding? In this paper, we first leverage the dual graph to model the co-existing of user-video and user-item interactions in video-driven e-commerce and innovatively reduce user preference understanding to a graph matching problem. To solve it, we further propose a novel bi-level Graph Matching Network(GMN), which mainly consists of node- and preference-level graph matching. Given a user, node-level graph matching aims to match videos and items, while preference-level graph matching aims to match multiple user preferences extracted from both videos and items. Then the proposed GMN can generate and improve user embedding by aggregating matched nodes or preferences from the dual graph in a bi-level manner. Comprehensive experiments show the superiority of the proposed GMN with significant improvements over state-of-the-art approaches (e.g., AUC+1.9% and CTR+7.15%). We have developed it on a well-known video-driven e-commerce platform, serving hundreds of millions of users every day

cs.LG↗

arXiv2024

Estimating a function of the scale parameter in a gamma distribution with bounded variance

Given a gamma population with known shape parameter $α$, we develop a general theory for estimating a function $g(\cdot)$ of the scale parameter $β$ with bounded variance. We begin by defining a sequential sampling procedure with $g(\cdot)$ satisfying some desired condition in proposing the stopping rule, and show the procedure enjoys appealing asymptotic properties. After these general conditions, we substitute $g(\cdot)$ with specific functions including the gamma mean, the gamma variance, the gamma rate parameter, and a gamma survival probability as four possible illustrations. For each illustration, Monte Carlo simulations are carried out to justify the remarkable performance of our proposed sequential procedure. This is further substantiated with a real data study on weights of newly born babies.

stat.ME↗

arXiv2024

EEG-DBNet: A Dual-Branch Network for Temporal-Spectral Decoding in Motor-Imagery Brain-Computer Interfaces

Motor imagery electroencephalogram (EEG)-based brain-computer interfaces (BCIs) offer significant advantages for individuals with restricted limb mobility. However, challenges such as low signal-to-noise ratio and limited spatial resolution impede accurate feature extraction from EEG signals, thereby affecting the classification accuracy of different actions. To address these challenges, this study proposes an end-to-end dual-branch network (EEG-DBNet) that decodes the temporal and spectral sequences of EEG signals in parallel through two distinct network branches. Each branch comprises a local convolutional block and a global convolutional block. The local convolutional block transforms the source signal from the temporal-spatial domain to the temporal-spectral domain. By varying the number of filters and convolution kernel sizes, the local convolutional blocks in different branches adjust the length of their respective dimension sequences. Different types of pooling layers are then employed to emphasize the features of various dimension sequences, setting the stage for subsequent global feature extraction. The global convolution block splits and reconstructs the feature of the signal sequence processed by the local convolution block in the same branch and further extracts features through the dilated causal convolutional neural networks. Finally, the outputs from the two branches are concatenated, and signal classification is completed via a fully connected layer. Our proposed method achieves classification accuracies of 85.84% and 91.60% on the BCI Competition 4-2a and BCI Competition 4-2b datasets, respectively, surpassing existing state-of-the-art models. The source code is available at https://github.com/xicheng105/EEG-DBNet.

cs.HC↗

arXiv2024

Competing multiferroic phases in monolayer and few-layer NiI$_{2}$

A recent experiment reported type-II multiferroicity in monolayer (ML) NiI$_{2}$ based on a presumed spiral magnetic configuration (Spiral-B), which is, as we found here, under debate in the ML limit. Freestanding ML NiI$_{2}$ breaks its C$_{3}$ symmetry, as it prefers a striped antiferromagnetic order (AABB-AFM) along with an intralayer antiferroelectric (AFE) order. However, substrate confinement may preserve the C$_{3}$ symmetry and/or apply tensile strain to the ML. This leads to another spiral magnetic order (Spiral-$IV^X$), while 2L shows a different order (Spiral-$V^Y$) and Spiral-B dominates in thicker layers. Thus, three multiferroic phases, namely, Spiral-B+FE, Spiral-$IV^X$ +FE, Spiral-$V^Y$+FE, and an anti-multiferroic AABB-AFM+AFE one, show layer-thickness-dependent and geometry-dependent dominance, ascribed to competitions among thickness-dependent Kitaev, biquadratic, and Heisenberg spin-exchange interactions and single-ion magnetic anisotropy. Our theoretical results clarify the debate on the multiferroicity of ML NiI$_{2}$ and shed light on the role of layer-stacking-induced changes in noncollinear spin-exchange interactions and magnetic anisotropy in thickness-dependent magnetism.

cond-mat.mtrl-sci↗

arXiv2024

Quality assurance of organs-at-risk delineation in radiotherapy

The delineation of tumor target and organs-at-risk is critical in the radiotherapy treatment planning. Automatic segmentation can be used to reduce the physician workload and improve the consistency. However, the quality assurance of the automatic segmentation is still an unmet need in clinical practice. The patient data used in our study was a standardized dataset from AAPM Thoracic Auto-Segmentation Challenge. The OARs included were left and right lungs, heart, esophagus, and spinal cord. Two groups of OARs were generated, the benchmark dataset manually contoured by experienced physicians and the test dataset automatically created using a software AccuContour. A resnet-152 network was performed as feature extractor, and one-class support vector classifier was used to determine the high or low quality. We evaluate the model performance with balanced accuracy, F-score, sensitivity, specificity and the area under the receiving operator characteristic curve. We randomly generated contour errors to assess the generalization of our method, explored the detection limit, and evaluated the correlations between detection limit and various metrics such as volume, Dice similarity coefficient, Hausdorff distance, and mean surface distance. The proposed one-class classifier outperformed in metrics such as balanced accuracy, AUC, and others. The proposed method showed significant improvement over binary classifiers in handling various types of errors. Our proposed model, which introduces residual network and attention mechanism in the one-class classification framework, was able to detect the various types of OAR contour errors with high accuracy. The proposed method can significantly reduce the burden of physician review for contour delineation.

cs.CV↗

arXiv2024

Square-Root Inverse Filter-based GNSS-Visual-Inertial Navigation

While Global Navigation Satellite System (GNSS) is often used to provide global positioning if available, its intermittency and/or inaccuracy calls for fusion with other sensors. In this paper, we develop a novel GNSS-Visual-Inertial Navigation System (GVINS) that fuses visual, inertial, and raw GNSS measurements within the square-root inverse sliding window filtering (SRI-SWF) framework in a tightly coupled fashion, which thus is termed SRI-GVINS. In particular, for the first time, we deeply fuse the GNSS pseudorange, Doppler shift, single-differenced pseudorange, and double-differenced carrier phase measurements, along with the visual-inertial measurements. Inherited from the SRI-SWF, the proposed SRI-GVINS gains significant numerical stability and computational efficiency over the start-of-the-art methods. Additionally, we propose to use a filter to sequentially initialize the reference frame transformation till converges, rather than collecting measurements for batch optimization. We also perform online calibration of GNSS-IMU extrinsic parameters to mitigate the possible extrinsic parameter degradation. The proposed SRI-GVINS is extensively evaluated on our own collected UAV datasets and the results demonstrate that the proposed method is able to suppress VIO drift in real-time and also show the effectiveness of online GNSS-IMU extrinsic calibration. The experimental validation on the public datasets further reveals that the proposed SRI-GVINS outperforms the state-of-the-art methods in terms of both accuracy and efficiency.

cs.RO↗

arXiv2024

Unblind Text Inputs: Predicting Hint-text of Text Input in Mobile Apps via LLM

Mobile apps have become indispensable for accessing and participating in various environments, especially for low-vision users. Users with visual impairments can use screen readers to read the content of each screen and understand the content that needs to be operated. Screen readers need to read the hint-text attribute in the text input component to remind visually impaired users what to fill in. Unfortunately, based on our analysis of 4,501 Android apps with text inputs, over 0.76 of them are missing hint-text. These issues are mostly caused by developers' lack of awareness when considering visually impaired individuals. To overcome these challenges, we developed an LLM-based hint-text generation model called HintDroid, which analyzes the GUI information of input components and uses in-context learning to generate the hint-text. To ensure the quality of hint-text generation, we further designed a feedback-based inspection mechanism to further adjust hint-text. The automated experiments demonstrate the high BLEU and a user study further confirms its usefulness. HintDroid can not only help visually impaired individuals, but also help ordinary people understand the requirements of input components. HintDroid demo video: https://youtu.be/FWgfcctRbfI.

cs.HC↗

arXiv2024

Beam test of a baseline vertex detector prototype for CEPC

The Circular Electron Positron Collider (CEPC) has been proposed to enable more thorough and precise measurements of the properties of Higgs, W, and Z bosons, as well as to search for new physics. In response to the stringent performance requirements of the vertex detector for the CEPC, a baseline vertex detector prototype was tested and characterized for the first time using a 6 GeV electron beam at DESY II Test Beam Line 21. The baseline vertex detector prototype is designed with a cylindrical barrel structure that contains six double-sided detector modules (ladders). Each side of the ladder includes TaichuPix-3 sensors based on Monolithic Active Pixel Sensor (MAPS) technology, a flexible printed circuit, and a carbon fiber support structure. Additionally, the readout electronics and the Data Acquisition system were also examined during this beam test. The performance of the prototype was evaluated using an electron beam that passed through six ladders in a perpendicular direction. The offline data analysis indicates a spatial resolution of about 5 um, with detection efficiency exceeding 99 % and an impact parameter resolution of about 5.1 um. These promising results from this baseline vertex detector prototype mark a significant step toward realizing the optimal vertex detector for the CEPC.

physics.ins-det↗

arXiv2024

Mixed Variational Formulation of Coupled Plates

This paper proposes a mixed variational formulation for the problem of two coupled plates with a rigid {junction}. The proposed mixed {formulation} introduces {the union of} stresses and moments as {an auxiliary variable}, which {are} commonly of great interest in practical applications. The primary challenge lies in determining a suitable {space involving} both boundary and junction conditions of the auxiliary variable. The {theory} of densely defined operators in Hilbert spaces is employed to define {a nonstandard Sobolev space} without the use of trace operators. The well-posedness is established for the mixed formulation. Based on these conditions, this paper provides a framework {of} conforming {mixed} finite element methods. Numerical experiments are given to validate the theoretical results.

math.NA↗

arXiv2024

A hybrid iterative method based on MIONet for PDEs: Theory and numerical examples

We propose a hybrid iterative method based on MIONet for PDEs, which combines the traditional numerical iterative solver and the recent powerful machine learning method of neural operator, and further systematically analyze its theoretical properties, including the convergence condition, the spectral behavior, as well as the convergence rate, in terms of the errors of the discretization and the model inference. We show the theoretical results for the frequently-used smoothers, i.e. Richardson (damped Jacobi) and Gauss-Seidel. We give an upper bound of the convergence rate of the hybrid method w.r.t. the model correction period, which indicates a minimum point to make the hybrid iteration converge fastest. Several numerical examples including the hybrid Richardson (Gauss-Seidel) iteration for the 1-d (2-d) Poisson equation are presented to verify our theoretical results, and also reflect an excellent acceleration effect. As a meshless acceleration method, it is provided with enormous potentials for practice applications.

math.NA↗

arXiv2024

Inhibition of water vapor condensation by dipropylene glycol droplets on hydrophobic surfaces via vapor sink strategy

Condensation, frosting and icing are natural phenomena, and have been enduring challenges for human society and modern engineering. These phenomena pose a range of issues, from hazardous icy road surfaces, damage to electronic devices due to condensation, to frost accumulation on power lines and aircraft. Dipropylene glycol (DG) is a non-toxic, non-corrosive, and biologically safe substance with excellent hygroscopicity, capable of inhibiting condensation and ice formation through the vapor sink strategy. However, there has been limited research on DG. Herein, we provides a detailed study of the performance of DG droplets in suppressing condensation with experiments and theoretical analysis. Results found that a dry zone ring would form around the DG droplet on a cooling solid surface. The ratio of the dry-zone radius to the DG droplet radius increases with the solid surface temperature and scales as 1/(Tdew-Tc). Results indicated that the cooling time of substrate surface does not affect the ratio in a short period. When the temperature is higher, the ratio decreases slightly with DG droplet volume; while it keeps almost still, when the temperature is lower. A theoretical model is also proposed to reveal the relationship between the ratio and the substrate surface temperature. These findings hold the promise of not only enhancing our understanding of condensation suppression but also advancing the development of innovative solutions for various industries, including transportation, electronics, aviation, and power distribution.

physics.flu-dyn↗

arXiv2024

MGDCF: Distance Learning via Markov Graph Diffusion for Neural Collaborative Filtering

Graph Neural Networks (GNNs) have recently been utilized to build Collaborative Filtering (CF) models to predict user preferences based on historical user-item interactions. However, there is relatively little understanding of how GNN-based CF models relate to some traditional Network Representation Learning (NRL) approaches. In this paper, we show the equivalence between some state-of-the-art GNN-based CF models and a traditional 1-layer NRL model based on context encoding. Based on a Markov process that trades off two types of distances, we present Markov Graph Diffusion Collaborative Filtering (MGDCF) to generalize some state-of-the-art GNN-based CF models. Instead of considering the GNN as a trainable black box that propagates learnable user/item vertex embeddings, we treat GNNs as an untrainable Markov process that can construct constant context features of vertices for a traditional NRL model that encodes context features with a fully-connected layer. Such simplification can help us to better understand how GNNs benefit CF models. Especially, it helps us realize that ranking losses play crucial roles in GNN-based CF tasks. With our proposed simple yet powerful ranking loss InfoBPR, the NRL model can still perform well without the context features constructed by GNNs. We conduct experiments to perform detailed analysis on MGDCF.

cs.SI↗