arXiv subjects

Lingyun Wang

Publications and source records attributed to Lingyun Wang.

26 records · Page 2Linked to original sources

arXiv2024

Gaussian Time Machine: A Real-Time Rendering Methodology for Time-Variant Appearances

Recent advancements in neural rendering techniques have significantly enhanced the fidelity of 3D reconstruction. Notably, the emergence of 3D Gaussian Splatting (3DGS) has marked a significant milestone by adopting a discrete scene representation, facilitating efficient training and real-time rendering. Several studies have successfully extended the real-time rendering capability of 3DGS to dynamic scenes. However, a challenge arises when training images are captured under vastly differing weather and lighting conditions. This scenario poses a challenge for 3DGS and its variants in achieving accurate reconstructions. Although NeRF-based methods (NeRF-W, CLNeRF) have shown promise in handling such challenging conditions, their computational demands hinder real-time rendering capabilities. In this paper, we present Gaussian Time Machine (GTM) which models the time-dependent attributes of Gaussian primitives with discrete time embedding vectors decoded by a lightweight Multi-Layer-Perceptron(MLP). By adjusting the opacity of Gaussian primitives, we can reconstruct visibility changes of objects. We further propose a decomposed color model for improved geometric consistency. GTM achieved state-of-the-art rendering fidelity on 3 datasets and is 100 times faster than NeRF-based counterparts in rendering. Moreover, GTM successfully disentangles the appearance changes and renders smooth appearance interpolation.

cs.CV↗

arXiv2024

Fully Automated OCT-based Tissue Screening System

This study introduces a groundbreaking optical coherence tomography (OCT) imaging system dedicated for high-throughput screening applications using ex vivo tissue culture. Leveraging OCT's non-invasive, high-resolution capabilities, the system is equipped with a custom-designed motorized platform and tissue detection ability for automated, successive imaging across samples. Transformer-based deep learning segmentation algorithms further ensure robust, consistent, and efficient readouts meeting the standards for screening assays. Validated using retinal explant cultures from a mouse model of retinal degeneration, the system provides robust, rapid, reliable, unbiased, and comprehensive readouts of tissue response to treatments. This fully automated OCT-based system marks a significant advancement in tissue screening, promising to transform drug discovery, as well as other relevant research fields.

physics.med-ph↗

arXiv2024

Sub2Full: split spectrum to boost OCT despeckling without clean data

Optical coherence tomography (OCT) suffers from speckle noise, causing the deterioration of image quality, especially in high-resolution modalities like visible light OCT (vis-OCT). The potential of conventional supervised deep learning denoising methods is limited by the difficulty of obtaining clean data. Here, we proposed an innovative self-supervised strategy called Sub2Full (S2F) for OCT despeckling without clean data. This approach works by acquiring two repeated B-scans, splitting the spectrum of the first repeat as a low-resolution input, and utilizing the full spectrum of the second repeat as the high-resolution target. The proposed method was validated on vis-OCT retinal images visualizing sublaminar structures in outer retina and demonstrated superior performance over conventional Noise2Noise and Noise2Void schemes. The code is available at https://github.com/PittOCT/Sub2Full-OCT-Denoising.

eess.IV↗

arXiv2023

AG-CRC: Anatomy-Guided Colorectal Cancer Segmentation in CT with Imperfect Anatomical Knowledge

When delineating lesions from medical images, a human expert can always keep in mind the anatomical structure behind the voxels. However, although high-quality (though not perfect) anatomical information can be retrieved from computed tomography (CT) scans with modern deep learning algorithms, it is still an open problem how these automatically generated organ masks can assist in addressing challenging lesion segmentation tasks, such as the segmentation of colorectal cancer (CRC). In this paper, we develop a novel Anatomy-Guided segmentation framework to exploit the auto-generated organ masks to aid CRC segmentation from CT, namely AG-CRC. First, we obtain multi-organ segmentation (MOS) masks with existing MOS models (e.g., TotalSegmentor) and further derive a more robust organ of interest (OOI) mask that may cover most of the colon-rectum and CRC voxels. Then, we propose an anatomy-guided training patch sampling strategy by optimizing a heuristic gain function that considers both the proximity of important regions (e.g., the tumor or organs of interest) and sample diversity. Third, we design a novel self-supervised learning scheme inspired by the topology of tubular organs like the colon to boost the model performance further. Finally, we employ a masked loss scheme to guide the model to focus solely on the essential learning region. We extensively evaluate the proposed method on two CRC segmentation datasets, where substantial performance improvement (5% to 9% in Dice) is achieved over current state-of-the-art medical image segmentation models, and the ablation studies further evidence the efficacy of every proposed component.

eess.IV↗

arXiv2020

Photoanodes Based on TiO$_2$ and $α$-Fe$_2$O$_3$ for Solar Water Splitting Superior Role of 1D Nanoarchitectures and of Combined Heterostructures

Solar driven photoelectrochemical water splitting (PEC-WS) using semiconductor photoelectrodes represents a promising approach for a sustainable and environmentally friendly production of renewable energy vectors and fuel sources, such as dihydrogen (H2). In this context, titanium dioxide (TiO$_2$) and iron oxide (hematite, $α$-Fe$_2$O$_3$) are among the most investigated candidates as photoanode materials, mainly owing to their resistance to photocorrosion, non-toxicity, natural abundance, and low production cost. Major drawbacks are, however, an inherently low electrical conductivity and a limited hole diffusion length that significantly affect the performance of TiO$_2$ and $α$-Fe$_2$O$_3$ in PEC devices. To this regard, one-dimensional (1D) nanostructuring is typically applied as it provides several superior features such as a significant enlargement of the material surface area, extended contact between the semiconductor and the electrolyte and, most remarkably, preferential electrical transport that overall suppress charge carrier recombination and improve TiO$_2$ and $α$-Fe$_2$O$_3$ photo-electrocatalytic properties. The present review describes various synthetic methods, properties and PEC applications of 1D-photoanodes (nanotubes, nanorods, nanofibers, nanowires) based on titania, hematite, and on $α$-Fe$_2$O$_3$/TiO$_2$ heterostructures. Various routes towards modification and enhancement of PEC activity of 1D photoanodes are also discussed including doping, decoration with co-catalysts and heterojunction engineering. Finally, the challenges related to the optimization of charge transfer kinetics in both oxides are highlighted.

physics.app-ph↗

arXiv2020

LFZip: Lossy compression of multivariate floating-point time series data via improved prediction

Time series data compression is emerging as an important problem with the growth in IoT devices and sensors. Due to the presence of noise in these datasets, lossy compression can often provide significant compression gains without impacting the performance of downstream applications. In this work, we propose an error-bounded lossy compressor, LFZip, for multivariate floating-point time series data that provides guaranteed reconstruction up to user-specified maximum absolute error. The compressor is based on the prediction-quantization-entropy coder framework and benefits from improved prediction using linear models and neural networks. We evaluate the compressor on several time series datasets where it outperforms the existing state-of-the-art error-bounded lossy compressors. The code and data are available at https://github.com/shubhamchandak94/LFZip

eess.SP↗

arXiv2017

Hadronic coupling constants of $g_{σππ}$ in lattice QCD

We investigate the coupling constant $g_{σππ}$ for the hadronic decay $σ\toππ$ only using the relevant three-point function, which is evaluated by the moving-wall source technique with a pretty good noise-to-signal ratio. This simulation is carried out on a $40^3\times96$ MILC gauge configuration with $N_f=2+1$ flavor of the "Asqtad" improved staggered dynamical sea quarks at the lattice spacing $a \approx 0.09$ fm. Our estimated value for this given MILC fine lattice gauge ensemble $g_{σππ}=2.71(42)$ GeV.

hep-lat↗

arXiv2016

Studying the $ρ$ resonance parameters with staggered fermions

We deliver a lattice study of $ρ$ resonance parameters with p-wave $ππ$ scattering phases, which are extracted by finite-size methods at one center-of-mass frame and four moving frames for six MILC lattice ensembles with pion masses ranging from $346$ to $ 176$ MeV. The effective range formula is applied to describe the scattering phases as a function of the energy covering the resonance region, this allows us to extract $ρ$ resonance parameters and to investigate the quark-mass dependence. Lattice studies with three flavors of the Asqtad-improved staggered fermions enable us to use the moving-wall source technique on large lattice spatial dimensions ($L=64$) and small light $u/d$ quarks. Numerical computations are carried out at two lattice spacings, $a \approx 0.12$ and $0.09$ fm.

hep-lat↗