arXiv subjects

Ming Huang

Publications and source records attributed to Ming Huang.

34 records · Page 2Linked to original sources

arXiv2024

Flexible Variational Information Bottleneck: Achieving Diverse Compression with a Single Training

Information Bottleneck (IB) is a widely used framework that enables the extraction of information related to a target random variable from a source random variable. In the objective function, IB controls the trade-off between data compression and predictiveness through the Lagrange multiplier $β$. Traditionally, to find the trade-off to be learned, IB requires a search for $β$ through multiple training cycles, which is computationally expensive. In this study, we introduce Flexible Variational Information Bottleneck (FVIB), an innovative framework for classification task that can obtain optimal models for all values of $β$ with single, computationally efficient training. We theoretically demonstrate that across all values of reasonable $β$, FVIB can simultaneously maximize an approximation of the objective function for Variational Information Bottleneck (VIB), the conventional IB method. Then we empirically show that FVIB can learn the VIB objective as effectively as VIB. Furthermore, in terms of calibration performance, FVIB outperforms other IB and calibration methods by enabling continuous optimization of $β$. Our codes are available at https://github.com/sotakudo/fvib.

cs.LG↗

arXiv2024

Detecting Reddit Users with Depression Using a Hybrid Neural Network SBERT-CNN

Depression is a widespread mental health issue, affecting an estimated 3.8% of the global population. It is also one of the main contributors to disability worldwide. Recently it is becoming popular for individuals to use social media platforms (e.g., Reddit) to express their difficulties and health issues (e.g., depression) and seek support from other users in online communities. It opens great opportunities to automatically identify social media users with depression by parsing millions of posts for potential interventions. Deep learning methods have begun to dominate in the field of machine learning and natural language processing (NLP) because of their ease of use, efficient processing, and state-of-the-art results on many NLP tasks. In this work, we propose a hybrid deep learning model which combines a pretrained sentence BERT (SBERT) and convolutional neural network (CNN) to detect individuals with depression with their Reddit posts. The sentence BERT is used to learn the meaningful representation of semantic information in each post. CNN enables the further transformation of those embeddings and the temporal identification of behavioral patterns of users. We trained and evaluated the model performance to identify Reddit users with depression by utilizing the Self-reported Mental Health Diagnoses (SMHD) data. The hybrid deep learning model achieved an accuracy of 0.86 and an F1 score of 0.86 and outperformed the state-of-the-art documented result (F1 score of 0.79) by other machine learning models in the literature. The results show the feasibility of the hybrid model to identify individuals with depression. Although the hybrid model is validated to detect depression with Reddit posts, it can be easily tuned and applied to other text classification tasks and different clinical applications.

cs.CL↗

arXiv2023

Ultrasonic backscattering model for Rayleigh waves in polycrystals with Born and independent scattering approximations

This paper presents theoretical and numerical models for the backscattering of 2D Rayleigh waves in single-phase, untextured polycrystalline materials with statistically equiaxed grains. The theoretical model, based on our prior inclusion-induced Rayleigh wave scattering model and the independent scattering approximation, considers single scattering of Rayleigh-to-Rayleigh (R-R) waves. The numerical finite element model is established to accurately simulate the scattering problem and evaluate the theoretical model. Good quantitative agreement is observed between the theoretical model and the finite element results, especially for weakly scattering materials. The agreement decreases with the increase of the anisotropy index, owing to the reduced applicability of the Born approximation. However, the agreement remains generally good when weak multiple scattering is involved. In addition, the R-R backscattering behaviour of 2D Rayleigh waves is similar to the longitudinal-to-longitudinal and transverse-to-transverse backscattering of bulk waves, with the former exhibiting stronger scattering. These findings establish a foundation for using Rayleigh waves in quantitative characterisation of polycrystalline materials.

physics.app-ph↗

arXiv2023

Stiffness matrix method for modelling wave propagation in arbitrary multilayers

Natural and engineered media usually involve combinations of solid, fluid and porous layers, and accurate and stable modelling of wave propagation in such complex multilayered media is fundamental to evaluating their properties with wave-based methods. Here we present a general stiffness matrix method for modelling waves in arbitrary multilayers. The method first formulates stiffness matrices for individual layers based on the governing wave equations for fluids and solids, and the Biot theory for porous materials. Then it utilises the boundary conditions considered at layer interfaces to assemble the layer matrices into a global system of equations, to obtain solutions for reflection and transmission coefficients at any incidence. Its advantage over existing methods is manifested by its unconditional computational stability, and its validity is proved by experimental validations on single solid sheets, porous layers, and porous-solid-porous battery electrodes. This establishes a powerful theoretical platform that allows us to develop advanced wave-based methods to quantitatively characterise properties of the layers, especially for layers of porous materials.

physics.class-ph↗

arXiv2023

Frustrated total internal reflection of ultrasonic waves at a fluid-coupled elastic plate

A complete treatment regarding frustrated total internal reflection (FTIR) of ultrasonic waves is presented and validated against experiments, providing a theoretical explanation for the physics behind this phenomenon. Two different approaches are used to develop a theoretical model capable of studying transmission in fluid-coupled elastic plates. One is the multiple reflections approach (analogous to the study of FTIR in electromagnetic/optical waves), which is shown to have limited applicability for incident angles beyond the first critical angle. This prompted us to address the problem using the second, potentials-based approach, which is established and validated against experimental data with correct predictions for a thin air-coupled steel sheet. A relation between the transmitted power and the dispersion curves for guided waves in the plate is established, highlighting the two fundamental causes of FTIR in such systems. First, a thin plate, when compared to the wavelength of the wave incident on it, will always be subjected to the effects of FTIR. This is because the evanescent wave created inside the plate will never assume negligible values, thus always allowing transmission to the other side. Second, and more surprisingly, the excitation of the fundamental antisymmetric mode $A_0$ of the plate is shown to be a direct enabler of FTIR, even for thicker plates.

physics.class-ph↗

arXiv2023

Automated Sleep Staging via Parallel Frequency-Cut Attention

This paper proposes a novel framework for automatically capturing the time-frequency nature of electroencephalogram (EEG) signals of human sleep based on the authoritative sleep medicine guidance. The framework consists of two parts: the first part extracts informative features by partitioning the input EEG spectrograms into a sequence of time-frequency patches. The second part is constituted by an attention-based architecture to efficiently search for the correlation between partitioned time-frequency patches and defining factors of sleep stages in parallel. The proposed pipeline is validated on the Sleep Heart Health Study dataset with new state-of-the-art results for the stages wake, N2, and N3, obtaining respective F1 scores of 0.93, 0.88, and 0.87, with only EEG signals used. The proposed method also has a high inter-rater reliability of 0.80 kappa. We also visualize the correspondence between sleep staging decisions and features extracted by the proposed method, providing strong interpretability for our model.

cs.LG↗

arXiv2022

Theoretical and numerical modeling of Rayleigh wave scattering by an elastic inclusion

This work presents theoretical and numerical models for the backscattering of two-dimensional Rayleigh waves by an elastic inclusion, with the host material being isotropic and the inclusion having arbitrary shape and crystallographic symmetry. The theoretical model is developed based on the reciprocity theorem using the far-field Green's function and the Born approximation, assuming a small acoustic impedance difference between the host and inclusion materials. The numerical finite element (FE) model is established to deliver relatively accurate simulation of the scattering problem and to evaluate the approximations of the theoretical model. Quantitative agreement is observed between the theoretical model and the FE results for arbitrarily-shaped surface/subsurface inclusions with isotropic/anisotropic properties. The agreement is excellent when the wavelength of the Rayleigh wave is larger than, or comparable to, the size of the inclusion, but it deteriorates as the wavelength gets smaller. Also, the agreement decreases with the anisotropy index for inclusions of anisotropic symmetry. The results lay the foundation for using Rayleigh waves for quantitative characterization of surface/subsurface inclusions, while also demonstrating its limitations.

physics.class-ph↗

arXiv2022

A New Outlier Removal Strategy Based on Reliability of Correspondence Graph for Fast Point Cloud Registration

Registration is a basic yet crucial task in point cloud processing. In correspondence-based point cloud registration, matching correspondences by point feature techniques may lead to an extremely high outlier ratio. Current methods still suffer from low efficiency, accuracy, and recall rate. We use a simple and intuitive method to describe the 6-DOF (degree of freedom) curtailment process in point cloud registration and propose an outlier removal strategy based on the reliability of the correspondence graph. The method constructs the corresponding graph according to the given correspondences and designs the concept of the reliability degree of the graph node for optimal candidate selection and the reliability degree of the graph edge to obtain the global maximum consensus set. The presented method could achieve fast and accurate outliers removal along with gradual aligning parameters estimation. Extensive experiments on simulations and challenging real-world datasets demonstrate that the proposed method can still perform effective point cloud registration even the correspondence outlier ratio is over 99%, and the efficiency is better than the state-of-the-art. Code is available at https://github.com/WPC-WHU/GROR.

cs.CV↗

arXiv2022

Cancer Subtyping via Embedded Unsupervised Learning on Transcriptomics Data

Cancer is one of the deadliest diseases worldwide. Accurate diagnosis and classification of cancer subtypes are indispensable for effective clinical treatment. Promising results on automatic cancer subtyping systems have been published recently with the emergence of various deep learning methods. However, such automatic systems often overfit the data due to the high dimensionality and scarcity. In this paper, we propose to investigate automatic subtyping from an unsupervised learning perspective by directly constructing the underlying data distribution itself, hence sufficient data can be generated to alleviate the issue of overfitting. Specifically, we bypass the strong Gaussianity assumption that typically exists but fails in the unsupervised learning subtyping literature due to small-sized samples by vector quantization. Our proposed method better captures the latent space features and models the cancer subtype manifestation on a molecular basis, as demonstrated by the extensive experimental results.

cs.LG↗

arXiv2022

Quantitative characterisation of the layered structure within lithium-ion batteries using ultrasonic resonance

Lithium-ion batteries (LIBs) are becoming an important energy storage solution to achieve carbon neutrality, but it remains challenging to characterise their internal states for the assurance of performance, durability and safety. This work reports a simple but powerful non-destructive characterisation technique, based on the formation of ultrasonic resonance from the repetitive layers within LIBs. A physical model is developed from the ground up, to interpret the results from standard experimental ultrasonic measurement setups. As output, the method delivers a range of critical pieces of information about the inner structure of LIBs, such as the number of layers, the average thicknesses of electrodes, the image of internal layers, and the states of charge variations across individual layers. This enables the quantitative tracking of internal cell properties, potentially providing new means of quality control during production processes, and tracking the states of health and charge during operation.

physics.class-ph↗

arXiv2022

Appraising scattering theories for polycrystals of any symmetry using finite elements

This paper uses 3D grain-scale finite element (FE) simulations to appraise the classical scattering theory of plane longitudinal wave propagation in untextured polycrystals with statistically equiaxed grains belonging to the seven crystal symmetries. As revealed from the results of 10,390 materials, the classical theory has a linear relationship with the elastic scattering factor at the quasi-static velocity limit, whereas the reference FE and self-consistent (SC) results generally exhibit a quadratic relationship. As supported by the results of 90 materials, such order difference also extends to the attenuation and phase velocity, leading to larger differences between the classical theory and the FE results for more strongly scattering materials. Alternatively, two approximate models are proposed to achieve more accurate calculations by including an additional quadratic term. One model uses quadratic coefficients from quasi-static SC velocity fits and is thus symmetry-specific, while the other uses theoretically-determined coefficients and is valid for any individual material. These simple models generally deliver more accurate attenuation and phase velocity (particularly the second model) than the classical theory, especially for strongly scattering materials. However, the models are invalid for the attenuation of materials with negative quadratic coefficients.

cond-mat.mtrl-sci↗

arXiv2022

Bottom-up Growth of Graphene Nanospears and Nanoribbons

Graphene nanoribbons (GNRs) are considered one of the most promising materials for next generation electronics, however a reliable and controllable synthesis method is still lacking. Here, we report the CVD growth of GNRs on a copper surface and the corresponding mechanisms of growth. One-dimensional GNR growth is enabled by a vapor-liquid-solid (VLS) graphene growth guided by on-surface propagation of a liquid catalyst particle. Controlling the suppression of vapor-solid-solid (VSS) graphene growth along the width direction of the GNR by tuning the flow of H2 during growth gives rise to a spear head-shaped graphene that we term graphene nanospears (GNSs). The real-time visual and spatially resolved observations confirm the VSS growth of graphene can be fully suppressed and lead to GNR formation on Cu surface. These findings reveal key insight into the growth mechanism of graphene and open a door for achieving a facile and scalable method of synthesizing free standing GNRs.

cond-mat.mes-hall↗

arXiv2021

Finite element and semi-analytical study of elastic wave propagation in strongly scattering polycrystals

This work studies scattering-induced elastic wave attenuation and phase velocity variation in 3D untextured cubic polycrystals with statistically equiaxed grains using the theoretical second-order approximation (SOA) and Born approximation models and the grain-scale finite element (FE) model, pushing the boundary towards strongly scattering materials. The results for materials with Zener anisotropy indices A>1 show a good agreement between the theoretical and FE models in the transition and stochastic regions. In the Rayleigh regime, the agreement is reasonable for common structural materials with 1 1, a semi-analytical model is proposed by iterating the far-field Born approximation and optimising the iterative coefficient. The proposed model agrees remarkably well with the FE model across all studied materials with greatly differing microstructures; the model validity also extends to the quasi-static velocity limit. For polycrystals with A<1, it is found that the agreement between the SOA and FE results is excellent for all studied materials and the correction of the model is not needed.

physics.class-ph↗

arXiv2020

Universal mechanical exfoliation of large-area 2D crystals

Two-dimensional (2D) materials provide extraordinary opportunities for exploring phenomena arising in atomically thin crystals. Beginning with the first isolation of graphene, mechanical exfoliation has been a key to provide high-quality 2D materials but despite improvements it is still limited in yield, lateral size and contamination. Here we introduce a contamination-free, one-step and universal Au-assisted mechanical exfoliation method and demonstrate its effectiveness by isolating 40 types of single-crystalline monolayers, including elemental 2D crystals, metal-dichalcogenides, magnets and superconductors. Most of them are of millimeter-size and high-quality, as shown by transfer-free measurements of electron microscopy, photo spectroscopies and electrical transport. Large suspended 2D crystals and heterojunctions were also prepared with high-yield. Enhanced adhesion between the crystals and the substrates enables such efficient exfoliation, for which we identify a common rule that underpins a universal route for producing large-area monolayers and thus supports studies of fundamental properties and potential application of 2D materials.

cond-mat.mes-hall↗

arXiv2019

Chemically Induced Transformation of CVD-Grown Bilayer Graphene into Single Layer Diamond

Notwithstanding numerous density functional studies on the chemically induced transformation of multilayer graphene into a diamond-like film, a comprehensive convincing experimental proof of such a conversion is still lacking. We show that the fluorination of graphene sheets in Bernal (AB)-stacked bilayer graphene (AB-BLG) grown by chemical vapor deposition on a single crystal CuNi(111) surface triggers the formation of interlayer carbon-carbon bonds, resulting in a fluorinated diamond monolayer (F-diamane). Induced by fluorine chemisorption, the phase transition from AB-BLG to single layer diamond was studied and verified by X-ray photoelectron, ultraviolet photoelectron, Raman, UV-Vis, electron energy loss spectroscopies, transmission electron microscopy, and DFT calculations.

cond-mat.mtrl-sci↗

arXiv2003

Meissner Effect and Constraints

We notice some beautiful geometrical defects found in liquid crystals, and explain them by imposing a constraint. We study the way constraints can occur, and introduce the concept of massive fields. We develop the theory of magnetic field expulsion in superconductors as an example. We notice strong analogies with the formation of grain boundaries in crystals, and realize that we do not understand crystals very deeply

cond-mat↗