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An effective method for quantification,visualization,and analysis of 3D cell shape during early embryogenesis

Quantitative Biology 2025年第1期13卷 113-126页

作者： Zelin Li Zhaoke Huang Jianfeng Cao Guoye Guan Zhongying Zhao Hong Yan Department of Electrical Engineering City University of Hong KongHong KongChina Centre for Intelligent Multidimensional Data Analysis Limited Hong KongChina Center for Quantitative Biology Peking UniversityBeijingBeijingChina Department of Systems Biology Harvard Medical SchoolBostonMassachusettsUSA Department of Data Science Dana-Farber Cancer InstituteBostonMassachusettsUSA Department of Biology Hong Kong Baptist UniversityHong KongChina State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist UniversityHong KongChina

Embryogenesis is the most basic process in developmental *** and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic *** descriptors such as volume,surface area,and mean curvature often fall short,providing only a global view and lacking in local detail and reconstruction *** this,we introduce an effective integrated method,3D Cell Shape Quantification(3DCSQ),for transforming digitized 3D cell shapes into analytical feature vectors,named eigengrid(proposed grid descriptor like eigen value),eigenharmonic,and *** uniquely combine spherical grids,spherical harmonics,and principal component analysis for cell shape *** demonstrate 3DCSQ’s effectiveness in recognizing cellular morphological phenotypes and clustering *** to Caenorhabditis elegans embryos of 29 living embryos from 4-to 350-cell stages,3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell *** also provide automatically cell shape lineaging analysis *** method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes,presenting an advancement in biological imaging and analysis.

关键词： Caenorhabditis elegans(C.elegans) cell shape quantification eigen features(eigengrid eigenharmonic&eigenspectrum) lineage analysis morphological reproducibility spherical harmonics(SPHARM)

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LU Decomposition and Generalized Autoone-Takagi Decomposition of Dual Matrices and their Applications

arXiv

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arXiv 2025年

作者： Xu, Renjie Wei, Yimin Yan, Hong Center for Intelligent Multidimensional Data Analysis Hong Kong Science Park Shatin Hong Kong School of Mathematical Sciences Shanghai Key Laboratory of Contemporary Applied Mathematics Fudan University Shanghai200433 China Department of Electrical Engineering Center for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong

This paper uses matrix transformations to provide the Autoone-Takagi decomposition of dual complex symmetric matrices and extends it to dual quaternion η-Hermitian matrices. The LU decomposition of dual matrices is given using the general solution of the Sylvester equation, and its equivalence to the existence of rank-k decomposition and dual Moore-Penrose generalized inverse (DMPGI) is proved. Similar methods are then used to provide the Cholesky decomposition of dual real symmetric positive definite matrices. Both of our decompositions are driven by applications in numerical linear *** Codes 15A23, 15B33, 65F55 Copyright © 2025, The Authors. All rights reserved.

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ELFATT: Efficient Linear Fast Attention for Vision Transformers

arXiv

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arXiv 2025年

作者： Wu, Chong Che, Maolin Xu, Renjie Ran, Zhuoheng Yan, Hong Department of Electrical Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Kowloon Hong Kong School of Mathematics and Statistics Guizhou University Guiyang550025 China

The attention mechanism is the key to the success of transformers in different machine learning tasks. However, the quadratic complexity with respect to the sequence length of the vanilla softmax-based attention mechanism becomes the major bottleneck for the application of long sequence tasks, such as vision tasks. Although various efficient linear attention mechanisms have been proposed, they need to sacrifice performance to achieve high efficiency. What’s more, memory-efficient methods, such as FlashAttention-1-3, still have quadratic computation complexity which can be further improved. In this paper, we propose a novel efficient linear fast attention (ELFATT) mechanism to achieve low memory input/output operations, linear computational complexity, and high performance at the same time. ELFATT offers 4-7x speedups over the vanilla softmax-based attention mechanism in high-resolution vision tasks without losing performance. ELFATT is FlashAttention friendly. Using FlashAttention-2 acceleration, ELFATT still offers 2-3x speedups over the vanilla softmax-based attention mechanism on high-resolution vision tasks without losing performance. Even on edge GPUs, ELFATT still offers 1.6x to 2.0x speedups compared to state-of-the-art attention mechanisms in various power modes from 5W to 60W. Furthermore, ELFATT can be used to enhance and accelerate diffusion tasks directly without training. Copyright © 2025, The Authors. All rights reserved.

关键词： Machine vision

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TOWARDS SCALABLE TOPOLOGICAL REGULARIZERS

arXiv

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arXiv 2025年

作者： Wong, Hiu-Tung Lee, Darrick Yan, Hong Centre for Intelligent Multidimensional Data Analysis Science and Technology Park Hong Kong School of Mathematics University of Edinburgh United Kingdom Department of Electrical Engineering City University of Hong Kong Kowloon Hong Kong

Latent space matching, which consists of matching distributions of features in latent space, is a crucial component for tasks such as adversarial attacks and defenses, domain adaptation, and generative modelling. Metrics for probability measures, such as Wasserstein and maximum mean discrepancy, are commonly used to quantify the differences between such distributions. However, these are often costly to compute, or do not appropriately take the geometric and topological features of the distributions into consideration. Persistent homology is a tool from topological data analysis which quantifies the multi-scale topological structure of point clouds, and has recently been used as a topological regularizer in learning tasks. However, computation costs preclude larger scale computations, and discontinuities in the gradient lead to unstable training behavior such as in adversarial tasks. We propose the use of principal persistence measures, based on computing the persistent homology of a large number of small subsamples, as a topological regularizer. We provide a parallelized GPU implementation of this regularizer, and prove that gradients are continuous for smooth densities. Furthermore, we demonstrate the efficacy of this regularizer on shape matching, image generation, and semi-supervised learning tasks, opening the door towards a scalable regularizer for topological features. © 2025, CC BY.

关键词： Probability distributions

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Volume Tells: Dual Cycle-Consistent Diffusion for 3D Fluorescence Microscopy De-noising and Super-Resolution

arXiv

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arXiv 2025年

作者： Li, Zelin Wang, Chenwei Huang, Zhaoke Ma, Yiming Zhao, Cunming Zhao, Zhongying Yan, Hong Department of Electrical Engineering City University of Hong Kong Hong Kong Centre for Intelligent Multidimensional Data Analysis Hong Kong Science Park Hong Kong Department of Biology Hong Kong Baptist University Hong Kong

3D fluorescence microscopy is essential for understanding fundamental life processes through long-term live-cell imaging. However, due to inherent issues in imaging principles, it faces significant challenges including spatially varying noise and anisotropic resolution, where the axial resolution lags behind the lateral resolution up to 4.5 times. Meanwhile, laser power is kept low to maintain cell viability, leading to inaccessible low-noise and high-resolution paired ground truth (GT). To tackle these limitations, a dual Cycle-consistent Diffusion is proposed to effectively mine intra-volume imaging priors within 3D cell volumes in an unsupervised manner, i.e., Volume Tells (VTCD), achieving de-noising and super-resolution (SR) simultaneously. Specifically, a spatially iso-distributed denoiser is designed to exploit the noise distribution consistency between adjacent low-noise and high-noise regions within the 3D cell volume, suppressing the spatially varying noise. Then, in light of the structural consistency of the cell volume, a cross-plane global-propagation SR module propagates high-resolution details from the XY plane into adjacent regions in the XZ and YZ planes, progressively enhancing resolution across the entire 3D cell volume. Experimental results on 10 in vivo cellular dataset demonstrate high improvements in both denoising and super-resolution, with axial resolution enhanced from ∼ 430 nm to ∼ 90 nm. © 2025, CC BY.

关键词： Fluorescence microscopy

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data Resolution and Future Challenges in Automated Undersea Cable System Design

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IEEE Network 2025年

作者： Wang, Tianjiao Wang, Xinyu Moran, Bill Wang, Zengfu Zukerman, Moshe Hong Kong Science Park Center for Intelligent Multidimensional Data Analysis Hong Kong Hong Kong The University of Melbourne Department of Electrical and Electronic Engineering MelbourneVIC3010 Australia Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China Northwestern Polytechnical University School of Automation Xi’an710072 China City University of Hong Kong Department of Electrical Engineering Kowloon Hong Kong

Undersea optical cables form the backbone of global Internet infrastructure, carrying nearly 99% of international Internet data. As demand for high-speed and reliable connectivity continues to grow, optimizing undersea cable path planning has become essential for reducing costs, mitigating risks, and ensuring high-quality service delivery. While traditional manual planning methods remain widely used by cable system designers, surveyors, and planners, the increasing complexity of cable deployment necessitates the integration of automated methods that leverage advanced computational techniques to enhance efficiency and decision-making. This paper reviews the state-of-the-art in cable path planning and system design, emphasizing the important role of data density in achieving optimal cable paths. In particular, we present realistic path-planning scenarios on the Earth’s surface and describe the Fast Marching Method (FMM), a computational approach that efficiently determines optimal routing based on available data. Simulation results show that increasing map resolution, along with accompanying data at the higher resolution, significantly enhances path planning accuracy, reduces deployment costs, and improves risk assessment, underscoring the importance of acquiring and utilizing high-density data. This understanding leads to a discussion of the challenges of data acquisition and computational scalability in processing large-scale datasets, and the importance of collaboration between cable system engineers, geospatial data scientists, and software developers. This paper proposes a collaborative framework in which automated algorithms complement traditional planning methods, enabling cost-effective solutions that continually adapt and become more effective through expert feedback. This research contributes to a more resilient, efficient, and adaptive global undersea communication network, paving the way for the next generation of intelligent, optimized cable infrastructur

关键词： Risk assessment

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An effective method for quantification, visualization, and analysis of 3D cell shape during early embryogenesis

引用

Quantitative Biology 2025年第1期 134-147页

作者： Zelin Li Zhaoke Huang Jianfeng Cao Guoye Guan Zhongying Zhao Hong Yan Department of Electrical Engineering City University of Hong Kong Centre for Intelligent Multidimensional Data Analysis Limited Center for Quantitative Biology Peking University Department of Systems Biology Harvard Medical School Department of Data Science Dana-Farber Cancer Institute Department of Biology Hong Kong Baptist University State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist University

Embryogenesis is the most basic process in developmental *** and simply quantifying cell shape is challenging for the complex and dynamic 3D embryonic cells. Traditional descriptors such as volume,surface area, and mean curvature often fall short, providing only a global view and lacking in local detail and reconstruction capability. Addressing this, we introduce an effective integrated method, 3D Cell Shape Quantification(3DCSQ), for transforming digitized 3D cell shapes into analytical feature vectors, named eigengrid(proposed grid descriptor like eigen value), eigenharmonic, and eigenspectrum. We uniquely combine spherical grids, spherical harmonics, and principal component analysis for cell shape quantification. We demonstrate 3DCSQ's effectiveness in recognizing cellular morphological phenotypes and clustering cells. Applied to Caenorhabditis elegans embryos of 29 living embryos from 4-to 350-cell stages,3DCSQ identifies and quantifies biologically reproducible cellular patterns including distinct skin cell deformations. We also provide automatically cell shape lineaging analysis program. This method not only systematizes cell shape description and evaluation but also monitors cell differentiation through shape changes, presenting an advancement in biological imaging and analysis.

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Efficient CUR decomposition for interpretable low-rank approximations and imaging applications

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Neurocomputing 2025年 645卷

作者： Abdelgawad, Muhammad A.A. Cheung, Ray C.C. Yan, Hong Department of Electrical Engineering and Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong Department of Electrical Engineering Faculty of Engineering Minia University El-Minia 61517 Egypt

Low-rank approximations based on the selected columns and rows from a given matrix are an alternative approach to singular value decomposition (SVD) and offer more interpretable outputs. They have been successfully used in pattern recognition, recommendation systems, bioinformatics, and social network analysis. One popular approach is CUR matrix decomposition, which represents the data matrix using three small matrices and can be achieved through randomized or deterministic approaches. Deterministic algorithms yield higher approximation accuracy than randomized ones. However, they are highly computationally expensive, especially with large datasets. To address these limitations, we introduce a new method for efficiently computing the CUR matrix decomposition called HyCUR. It incorporates a deterministic sampling strategy based on guided information of the singular vectors and a randomized sampling to select rows and columns for efficient CUR low-rank approximation. HyCUR overcomes the main drawbacks of deterministic methods and can estimate the approximation rank using an incremental QR factorization approach, which also approximates the SVD of the data matrix instead of the classical method. Therefore, HyCUR is suitable for applications with unknown data rank. Experimental results on synthetic and real datasets of varying sizes demonstrate that HyCUR achieves comparable accuracy to recent deterministic approaches while significantly improving computational efficiency for large low-rank datasets. We validate the proposed method in two real imaging applications: simultaneous sample and feature extraction from large datasets and hyperspectral unmixing to extract endmember signatures and estimate their fractions from hyperspectral image datasets (HSIs). The experiment results show promising performance in both applications. © 2025 Elsevier B.V.

关键词： Feature Selection

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Cross-Domain Object Classification Via Successive Subspace Alignment 48

Cross-Domain Object Classification Via Successive Subspace A...

引用

48th IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2023

作者： Chen, Kecheng Li, Haoliang Yan, Hong City University of Hong Kong Department of Electrical Engineering Center for Intelligent Multidimensional Data Analysis Kowloon Hong Kong

ISBN: (纸本)9781728163277

Recently, successive subspace learning (SSL)-based methods have shown to be effective for the task of visual object classification with mild data desire and mathematically transparent interpretable capability. However, existing SSL-based methods rely heavily on the data-centric subspace representations, leading to potential performance degradation problem in case of the domain shift between the training (a.k.a., source domain) and testing (a.k.a., target domain) data. To address this limitation, we propose an effective successive subspace learning method based on existing SSL-based methods. Specifically, we introduce a novel linear transformation layer to align eigenvectors in SSL module between source and target domains, as such, the discrepancy between source and target domains will be reduced, resulting in better cross-domain performance. The effectiveness of our proposed method is demonstrated on the Office-Caltech-10 and Office-31 benchmark datasets by using features extracted from pre-trained deep neural networks as input. © 2023 IEEE.

关键词： Linear transformations

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3D Cellular Segmentation of Live Embryos via Topologically and Biologically Boundary-aware Semi-supervised Learning

3D Cellular Segmentation of Live Embryos via Topologically a...

引用

2024 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2024

作者： Li, Zelin Huang, Zhaoke Yan, Hong City University of Hong Kong Department of Electrical Engineering Hong Kong Centre for Intelligent Multidimensional Data Analysis Hong Kong

ISBN: (纸本)9781665410205

3D cellular segmentation of fluorescence images of live embryos is a fundamental step in the analysis of embryonic developmental processes. However, existing fully supervised learning methods (CNN-based) to achieve this often suffer from non-robust loss functions and insufficient training data. Previous work on cellular segmentation did not consider topological information and biological constraints. In this paper, we propose a novel semi-supervised method using topological loss and biologically boundary-aware synthetic (labeled and unlabeled mixed) ground truth for 3D cellular segmentation of live embryos. The topological loss function guides the model to extract features in latent space. Semi-supervised learning and synthetic datasets improve the accuracy of inner and outer membrane recognition on a large number of unlabeled images. Experiment results and evaluation on 1472 live embryo images show that our method outperforms existing deep-learning models. This method can be adapted to images of live embryos of other animals. © 2024 IEEE.

关键词： Self-supervised learning

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