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Guang Yang

Publications and source records attributed to Guang Yang.

At least 307 records · Page 17Linked to original sources
arXiv2023

Simulation and analytical modeling of high-speed droplet impact onto a surface

The fluid dynamics of liquid droplet impact on surfaces hold significant relevance to various industrial applications. However, high impact velocities introduce compressible effects, leading to material erosion. A gap in understanding and modeling these effects has motivated this study. We simulated droplet impacts on surfaces and proposed a new analytical model for impact pressure and droplet turning line, targeting at predictions for enhanced cavitation. The highly compressed liquid behind the droplet expands sideways, causing lateral jetting. As the droplet encounters a shock wave, it reflects as a rarefaction wave, leading to low-pressure zones within the droplet. These zones converge at the droplet's center, causing cavitation, which, upon collapse, induces another shock wave, contributing to erosion. Using the well-established model for the low-velocity impact shows a significant discrepancy. Hence, an analytical model for the turning line radius is introduced, incorporating the lateral jetting's characteristic length scale. Comparing our model with existing ones, our new model exhibits superior predictive accuracy.

physics.flu-dyn↗
arXiv2023

Stain Consistency Learning: Handling Stain Variation for Automatic Digital Pathology Segmentation

Stain variation is a unique challenge associated with automated analysis of digital pathology. Numerous methods have been developed to improve the robustness of machine learning methods to stain variation, but comparative studies have demonstrated limited benefits to performance. Moreover, methods to handle stain variation were largely developed for H&E stained data, with evaluation generally limited to classification tasks. Here we propose Stain Consistency Learning, a novel framework combining stain-specific augmentation with a stain consistency loss function to learn stain colour invariant features. We perform the first, extensive comparison of methods to handle stain variation for segmentation tasks, comparing ten methods on Masson's trichrome and H&E stained cell and nuclei datasets, respectively. We observed that stain normalisation methods resulted in equivalent or worse performance, while stain augmentation or stain adversarial methods demonstrated improved performance, with the best performance consistently achieved by our proposed approach. The code is available at: https://github.com/mlyg/stain_consistency_learning

eess.IV↗
arXiv2023

CEERS Key Paper I: An Early Look into the First 500 Myr of Galaxy Formation with JWST

We present an investigation into the first 500 Myr of galaxy evolution from the Cosmic Evolution Early Release Science (CEERS) survey. CEERS, one of 13 JWST ERS programs, targets galaxy formation from z~0.5 to z>10 using several imaging and spectroscopic modes. We make use of the first epoch of CEERS NIRCam imaging, spanning 35.5 sq. arcmin, to search for candidate galaxies at z>9. Following a detailed data reduction process implementing several custom steps to produce high-quality reduced images, we perform multi-band photometry across seven NIRCam broad and medium-band (and six Hubble broadband) filters focusing on robust colors and accurate total fluxes. We measure photometric redshifts and devise a robust set of selection criteria to identify a sample of 26 galaxy candidates at z~9-16. These objects are compact with a median half-light radius of ~0.5 kpc. We present an early estimate of the z~11 rest-frame ultraviolet (UV) luminosity function, finding that the number density of galaxies at M_UV ~ -20 appears to evolve very little from z~9 to z~11. We also find that the abundance (surface density [arcmin^-2]) of our candidates exceeds nearly all theoretical predictions. We explore potential implications, including that at z>10 star formation may be dominated by top-heavy initial mass functions, which would result in an increased ratio of UV light per unit halo mass, though a complete lack of dust attenuation and/or changing star-formation physics may also play a role. While spectroscopic confirmation of these sources is urgently required, our results suggest that the deeper views to come with JWST should yield prolific samples of ultra-high-redshift galaxies with which to further explore these conclusions.

astro-ph.GA↗
arXiv2023

Data-Free Distillation Improves Efficiency and Privacy in Federated Thorax Disease Analysis

Thorax disease analysis in large-scale, multi-centre, and multi-scanner settings is often limited by strict privacy policies. Federated learning (FL) offers a potential solution, while traditional parameter-based FL can be limited by issues such as high communication costs, data leakage, and heterogeneity. Distillation-based FL can improve efficiency, but it relies on a proxy dataset, which is often impractical in clinical practice. To address these challenges, we introduce a data-free distillation-based FL approach FedKDF. In FedKDF, the server employs a lightweight generator to aggregate knowledge from different clients without requiring access to their private data or a proxy dataset. FedKDF combines the predictors from clients into a single, unified predictor, which is further optimized using the learned knowledge in the lightweight generator. Our empirical experiments demonstrate that FedKDF offers a robust solution for efficient, privacy-preserving federated thorax disease analysis.

eess.IV↗
arXiv2023

Assessing and Improving Syntactic Adversarial Robustness of Pre-trained Models for Code Translation

Context: Pre-trained models (PTMs) have demonstrated significant potential in automatic code translation. However, the vulnerability of these models in translation tasks, particularly in terms of syntax, has not been extensively investigated. Objective: To fill this gap, our study aims to propose a novel approach CoTR to assess and improve the syntactic adversarial robustness of PTMs in code translation. Method: CoTR consists of two components: CoTR-A and CoTR-D. CoTR-A generates adversarial examples by transforming programs, while CoTR-D proposes a semantic distance-based sampling data augmentation method and adversarial training method to improve the model's robustness and generalization capabilities. The Pass@1 metric is used by CoTR to assess the performance of PTMs, which is more suitable for code translation tasks and offers a more precise evaluation in real world scenarios. Results: The effectiveness of CoTR is evaluated through experiments on real world Java to Python datasets. The results demonstrate that CoTR-A can significantly reduce the performance of existing PTMs, while CoTR-D effectively improves the robustness of PTMs. Conclusion: Our study identifies the limitations of current PTMs, including large language models, in code translation tasks. It highlights the potential of CoTR as an effective solution to enhance the robustness of PTMs for code translation tasks.

cs.SE↗
arXiv2023

CEERS: MIRI deciphers the spatial distribution of dust-obscured star formation in galaxies at $0.1<z<2.5$

[Abridged] We combined HST images from the Cosmic Assembly Near-infrared Deep Extragalactic Legacy Survey with JWST images from the Cosmic Evolution Early Release Science (CEERS) survey to measure the stellar and dust-obscured star formation distributions of a mass-complete ($>10^{10}M_\odot$) sample of 69 star-forming galaxies (SFGs) at $0.1 75$; 35 galaxies). At lower $S/N$, simulations show that simultaneous measurements of the size and Sérsic index become unreliable. We extended our study to fainter sources ($S/N>10$; 69 galaxies) by fixing their Sérsic index to unity. The Sérsic index of bright galaxies ($S/N>75$) has a median value of 0.7, which, together with their axis ratio distribution, suggests a disk-like morphology in the rest-MIR. Galaxies above the main sequence (MS; i.e., starbursts) have rest-MIR sizes that are a factor 2 smaller than their rest-optical sizes. The median rest-optical to rest-MIR size ratio of MS galaxies increases with stellar mass, from 1.1 at $10^{9.8}M_\odot$ to 1.6 at $10^{11}M_\odot$. This mass-dependent trend resembles the one found in the literature between the rest-optical and rest-near-infrared sizes of SFGs, suggesting that it is due to radial color gradients affecting rest-optical sizes and that the sizes of the stellar and star-forming components of SFGs are, on average, consistent at all masses. There is, however, a small population of SFGs (15%) with a compact star-forming component embedded in a larger stellar structure. This could be the missing link between galaxies with an extended stellar component and those with a compact stellar component, the so-called blue nuggets.

astro-ph.GA↗
arXiv2023

CEERS: 7.7 $μ$m PAH Star Formation Rate Calibration with JWST MIRI

We test the relationship between UV-derived star formation rates (SFRs) and the 7.7 $μ$m polycyclic aromatic hydrocarbon (PAH) luminosities from the integrated emission of galaxies at z ~ 0 - 2. We utilize multi-band photometry covering 0.2 - 160 $μ$m from HST, CFHT, JWST, Spitzer, and Herschel for galaxies in the Cosmic Evolution Early Release Science (CEERS) Survey. We perform spectral energy distribution (SED) modeling of these data to measure dust-corrected far-UV (FUV) luminosities, $L_{FUV}$, and UV-derived SFRs. We then fit SED models to the JWST/MIRI 7.7 - 21 $μ$m CEERS data to derive rest-frame 7.7 $μ$m luminosities, $L_{770}$, using the average flux density in the rest-frame MIRI F770W bandpass. We observe a correlation between $L_{770}$ and $L_{FUV}$, where log $L_{770}$ is proportional to (1.27+/-0.04) log $L_{FUV}$. $L_{770}$ diverges from this relation for galaxies at lower metallicities, lower dust obscuration, and for galaxies dominated by evolved stellar populations. We derive a "single-wavelength" SFR calibration for $L_{770}$ which has a scatter from model estimated SFRs (${σ_{ΔSFR}}$) of 0.24 dex. We derive a "multi-wavelength" calibration for the linear combination of the observed FUV luminosity (uncorrected for dust) and the rest-frame 7.7 $μ$m luminosity, which has a scatter of ${σ_{ΔSFR}}$ = 0.21 dex. The relatively small decrease in $σ$ suggests this is near the systematic accuracy of the total SFRs using either calibration. These results demonstrate that the rest-frame 7.7 $μ$m emission constrained by JWST/MIRI is a tracer of the SFR for distant galaxies to this accuracy, provided the galaxies are dominated by star-formation with moderate-to-high levels of attenuation and metallicity.

astro-ph.GA↗
arXiv2023

High Accuracy and Cost-Saving Active Learning 3D WD-UNet for Airway Segmentation

We propose a novel Deep Active Learning (DeepAL) model-3D Wasserstein Discriminative UNet (WD-UNet) for reducing the annotation effort of medical 3D Computed Tomography (CT) segmentation. The proposed WD-UNet learns in a semi-supervised way and accelerates learning convergence to meet or exceed the prediction metrics of supervised learning models. Our method can be embedded with different Active Learning (AL) strategies and different network structures. The model is evaluated on 3D lung airway CT scans for medical segmentation and show that the use of uncertainty metric, which is parametrized as an input of query strategy, leads to more accurate prediction results than some state-of-the-art Deep Learning (DL) supervised models, e.g.,3DUNet and 3D CEUNet. Compared to the above supervised DL methods, our WD-UNet not only saves the cost of annotation for radiologists but also saves computational resources. WD-UNet uses a limited amount of annotated data (35% of the total) to achieve better predictive metrics with a more efficient deep learning model algorithm.

eess.IV↗
arXiv2023

Complex semiclassical theory for non-Hermitian quantum systems

Non-Hermitian quantum systems exhibit fascinating characteristics such as non-Hermitian topological phenomena and skin effect, yet their studies are limited by the intrinsic difficulties associated with their eigenvalue problems, especially in larger systems and higher dimensions. In Hermitian systems, the semiclassical theory has played an active role in analyzing spectrum, eigenstate, phase, transport properties, etc. Here, we establish a complex semiclassical theory applicable to non-Hermitian quantum systems by an analytical continuation of the physical variables such as momentum, position, time, and energy in the equations of motion and quantization condition to the complex domain. Further, we propose a closed-orbit scheme and physical meaning under such complex variables. We demonstrate that such a framework straightforwardly yields complex energy spectra and quantum states, topological phases and transitions, and even the skin effect in non-Hermitian quantum systems, presenting an unprecedented perspective toward nontrivial non-Hermitian physics, even with larger systems and higher dimensions.

cond-mat.mes-hall↗
arXiv2023

Three-Sensor 2ω Method with Multi-directional Layout: A General Methodology for Measuring Thermal Conductivity of Solid Materials

Anisotropic thermal transport plays a key role in both theoretical study and engineering practice of heat transfer, but accurately measuring anisotropic thermal conductivity remains a significant challenge. To address this issue, we propose the three-sensor 2ω method in this study, which is capable of accurately measuring the isotropic or anisotropic thermal conductivity of solid materials. In this method, several three-sensor groups following the design guidelines are fabricated upon the sample along different characteristic directions, and each group consists of three parallel metal sensors with unequal widths and distances optimally designed based on sensitivity analysis. Among the three sensors, the outer two serve as AC heaters and the middle one as a DC detector. The 2ω voltage signals across the detector in each three-sensor group are measured, and then the data are processed by the proposed Intersection Method to derive the thermal conductivities along directions of interest. The application of the detector's 2ω instead of the heater's 3ω voltage signals eliminates the errors introduced by the uncertainties of thermal resistance in superficial structures (metal layer, insulation layer, interface, etc.). Meanwhile, by replacing the fitting algorithm with the Intersection Method, the local optimum trap of multivariate fitting is avoided. To verify the accuracy and reliability, four typical monocrystalline semiconductors, i.e., Si, GaN, AlN, and {β-Ga _2 O _3}, are measured, and the results are consistent with the literature. This method will provide a comprehensive and versatile solution for the thermal conductivity measurements of solid materials.

physics.ins-det↗
arXiv2023

T1/T2 relaxation temporal modelling from accelerated acquisitions using a Latent Transformer

Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application. We propose a deep learning method that incorporates a time dependency Latent Transformer module to model relationships between parameterised time frames for improved reconstruction from undersampled data. The module, implemented as a multi-resolution sequence-to-sequence transformer, is integrated into an encoder-decoder architecture to leverage the inherent temporal correlations in relaxation processes. The presented results for accelerated T1 and T2 mapping show the model recovers maps with higher fidelity by explicit incorporation of time dynamics. This work demonstrates the importance of temporal modelling for artifact-free reconstruction in quantitative MRI.

eess.SP↗
arXiv2023

Swin Deformable Attention Hybrid U-Net for Medical Image Segmentation

Medical image segmentation is a crucial task in the field of medical image analysis. Harmonizing the convolution and multi-head self-attention mechanism is a recent research focus in this field, with various combination methods proposed. However, the lack of interpretability of these hybrid models remains a common pitfall, limiting their practical application in clinical scenarios. To address this issue, we propose to incorporate the Shifted Window (Swin) Deformable Attention into a hybrid architecture to improve segmentation performance while ensuring explainability. Our proposed Swin Deformable Attention Hybrid UNet (SDAH-UNet) demonstrates state-of-the-art performance on both anatomical and lesion segmentation tasks. Moreover, we provide a direct and visual explanation of the model focalization and how the model forms it, enabling clinicians to better understand and trust the decision of the model. Our approach could be a promising solution to the challenge of developing accurate and interpretable medical image segmentation models.

eess.IV↗
arXiv2023

LQR-CBF-RRT*: Safe and Optimal Motion Planning

We present LQR-CBF-RRT*, an incremental sampling-based algorithm for offline motion planning. Our framework leverages the strength of Control Barrier Functions (CBFs) and Linear Quadratic Regulators (LQR) to generate safety-critical and optimal trajectories for a robot with dynamics described by an affine control system. CBFs are used for safety guarantees, while LQRs are employed for optimal control synthesis during edge extensions. Popular CBF-based formulations for safety critical control require solving Quadratic Programs (QPs), which can be computationally expensive. Moreover, LQR-based controllers require repetitive applications of first-order Taylor approximations for nonlinear systems, which can also create an additional computational burden. To improve the motion planning efficiency, we verify the satisfaction of the CBF constraints directly in edge extension to avoid the burden of solving the QPs. We store computed optimal LQR gain matrices in a hash table to avoid re-computation during the local linearization of the rewiring procedure. Lastly, we utilize the Cross-Entropy Method for importance sampling to improve sampling efficiency. Our results show that the proposed planner surpasses its counterparts in computational efficiency and performs well in an experimental setup.

cs.RO↗
arXiv2023

Style Transfer and Self-Supervised Learning Powered Myocardium Infarction Super-Resolution Segmentation

This study proposes a pipeline that incorporates a novel style transfer model and a simultaneous super-resolution and segmentation model. The proposed pipeline aims to enhance diffusion tensor imaging (DTI) images by translating them into the late gadolinium enhancement (LGE) domain, which offers a larger amount of data with high-resolution and distinct highlighting of myocardium infarction (MI) areas. Subsequently, the segmentation task is performed on the LGE style image. An end-to-end super-resolution segmentation model is introduced to generate high-resolution mask from low-resolution LGE style DTI image. Further, to enhance the performance of the model, a multi-task self-supervised learning strategy is employed to pre-train the super-resolution segmentation model, allowing it to acquire more representative knowledge and improve its segmentation performance after fine-tuning. https: github.com/wlc2424762917/Med_Img

eess.IV↗
arXiv2023

CMRxRecon: An open cardiac MRI dataset for the competition of accelerated image reconstruction

Cardiac magnetic resonance imaging (CMR) has emerged as a valuable diagnostic tool for cardiac diseases. However, a limitation of CMR is its slow imaging speed, which causes patient discomfort and introduces artifacts in the images. There has been growing interest in deep learning-based CMR imaging algorithms that can reconstruct high-quality images from highly under-sampled k-space data. However, the development of deep learning methods requires large training datasets, which have not been publicly available for CMR. To address this gap, we released a dataset that includes multi-contrast, multi-view, multi-slice and multi-coil CMR imaging data from 300 subjects. Imaging studies include cardiac cine and mapping sequences. Manual segmentations of the myocardium and chambers of all the subjects are also provided within the dataset. Scripts of state-of-the-art reconstruction algorithms were also provided as a point of reference. Our aim is to facilitate the advancement of state-of-the-art CMR image reconstruction by introducing standardized evaluation criteria and making the dataset freely accessible to the research community. Researchers can access the dataset at https://www.synapse.org/#!Synapse:syn51471091/wiki/.

cs.CV↗
arXiv2023

$\mathrm{SAM^{Med}}$: A medical image annotation framework based on large vision model

Recently, large vision model, Segment Anything Model (SAM), has revolutionized the computer vision field, especially for image segmentation. SAM presented a new promptable segmentation paradigm that exhibit its remarkable zero-shot generalization ability. An extensive researches have explore the potential and limits of SAM in various downstream tasks. In this study, we presents $\mathrm{SAM^{Med}}$, an enhanced framework for medical image annotation that leverages the capabilities of SAM. $\mathrm{SAM^{Med}}$ framework consisted of two submodules, namely $\mathrm{SAM^{assist}}$ and $\mathrm{SAM^{auto}}$. The $\mathrm{SAM^{assist}}$ demonstrates the generalization ability of SAM to the downstream medical segmentation task using the prompt-learning approach. Results show a significant improvement in segmentation accuracy with only approximately 5 input points. The $\mathrm{SAM^{auto}}$ model aims to accelerate the annotation process by automatically generating input prompts. The proposed SAP-Net model achieves superior segmentation performance with only five annotated slices, achieving an average Dice coefficient of 0.80 and 0.82 for kidney and liver segmentation, respectively. Overall, $\mathrm{SAM^{Med}}$ demonstrates promising results in medical image annotation. These findings highlight the potential of leveraging large-scale vision models in medical image annotation tasks.

cs.CV↗
arXiv2023

CEERS MIRI Imaging: Data Reduction and Quality Assessment

The Cosmic Evolution Early Release Science Survey (CEERS), targeting the Extended Groth Strip extragalactic field, is one of the JWST Director's Discretionary Early Release Science programs. To date, all observations have been executed and include NIRCam/MIRI imaging and NIRSpec/NIRCam spectroscopic exposures. Here, we discuss the MIRI imaging, which includes eight pointings, four of which provide deep imaging with the bluer bands (F560W, F770W) and four with contiguous wavelength coverage in F1000W, F1280W, F1500W, and F1800W, where two of these also include coverage in F770W and F2100W. We present a summary of the data, the data quality, and data reduction. The data reduction is based on the JWST Calibration Pipeline combined with custom modifications and additional steps designed to enhance the output quality, including improvements in astrometry and the removal of detector artifacts. We estimate the image depth of the reduced mosaics, and show that these generally agree with expectations from the Exposure Time Calculator. We compare the MIRI F560W and F770W flux densities for bright sources to measurements from Spitzer/IRAC Ch3 (5.8 $μ$m) and Ch4 (8.0 $μ$m), and we find that they agree with systematic differences of $<0.1$ mag. For the redder MIRI bands, we assess their quality by studying the spectral energy distributions (SEDs) of Galactic stars. The SEDs are consistent with the expected Rayleigh-Jeans law with a deviation $\sim 0.03$ mag, indicating that the MIRI colors are reliable. We also discuss all publicly released data products (images and source catalogs), which are available on the CEERS website (https://ceers.github.io/).

astro-ph.GA↗
arXiv2023

Multi-scale, Data-driven and Anatomically Constrained Deep Learning Image Registration for Adult and Fetal Echocardiography

Temporal echocardiography image registration is a basis for clinical quantifications such as cardiac motion estimation, myocardial strain assessments, and stroke volume quantifications. In past studies, deep learning image registration (DLIR) has shown promising results and is consistently accurate and precise, requiring less computational time. We propose that a greater focus on the warped moving image's anatomic plausibility and image quality can support robust DLIR performance. Further, past implementations have focused on adult echocardiography, and there is an absence of DLIR implementations for fetal echocardiography. We propose a framework that combines three strategies for DLIR in both fetal and adult echo: (1) an anatomic shape-encoded loss to preserve physiological myocardial and left ventricular anatomical topologies in warped images; (2) a data-driven loss that is trained adversarially to preserve good image texture features in warped images; and (3) a multi-scale training scheme of a data-driven and anatomically constrained algorithm to improve accuracy. Our tests show that good anatomical topology and image textures are strongly linked to shape-encoded and data-driven adversarial losses. They improve different aspects of registration performance in a non-overlapping way, justifying their combination. Despite fundamental distinctions between adult and fetal echo images, we show that these strategies can provide excellent registration results in both adult and fetal echocardiography using the publicly available CAMUS adult echo dataset and our private multi-demographic fetal echo dataset. Our approach outperforms traditional non-DL gold standard registration approaches, including Optical Flow and Elastix. Registration improvements could be translated to more accurate and precise clinical quantification of cardiac ejection fraction, demonstrating a potential for translation.

eess.IV↗