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

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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)

Sequential Multi-objective Multi-agent Reinforcement Learning Approach for Predictive Maintenance

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arXiv 2025年

作者： Chen, Yan Liu, Cheng Department of Systems Engineering City University of Hong Kong Hong Kong Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong

Existing predictive maintenance (PdM) methods typically focus solely on whether to replace system components without considering the costs incurred by inspection. However, a well-considered approach should be able to minimize Remaining Useful Life (RUL) at engine replacement while maximizing inspection interval. To achieve this, multi-agent reinforcement learning (MARL) can be introduced. However, due to the sequential and mutually constraining nature of these 2 objectives, conventional MARL is not applicable. Therefore, this paper introduces a novel framework and develops a Sequential Multi-objective Multi-agent Proximal Policy Optimization (SMOMA-PPO) algorithm. Furthermore, to provide comprehensive and effective degradation information to RL agents, we also employed Gated Recurrent Unit, quantile regression, and probability distribution fitting to develop a GRU-based RUL Prediction (GRP) model. Experiments demonstrate that the GRP method significantly improves the accuracy of RUL predictions in the later stages of system operation compared to existing methods. When incorporating its output into SMOMA-PPO, we achieve at least a 15% reduction in average RUL without unscheduled replacements (UR), nearly a 10% increase in inspection interval, and an overall decrease in maintenance costs. Importantly, our approach offers a new perspective for addressing multi-objective maintenance planning with sequential constraints, effectively enhancing system reliability and reducing maintenance expenses. Copyright © 2025, The Authors. All rights reserved.

关键词： Predictive maintenance

Few-shot crack image classification using clip based on bayesian optimization

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arXiv 2025年

作者： Zhang, Yingchao Liu, Cheng Department of Systems Engineering City University of Hong Kong Hong Kong Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong

This study proposes a novel few-shot crack image classification model based on CLIP and Bayesian optimization. By combining multimodal information and Bayesian approach, the model achieves efficient classification of crack images in a small number of training samples. The CLIP model employs its robust feature extraction capabilities to facilitate precise classification with a limited number of samples. In contrast, Bayesian optimisation enhances the robustness and generalization of the model, while reducing the reliance on extensive labelled data. The results demonstrate that the model exhibits robust performance across a diverse range of dataset scales, particularly in the context of small sample sets. The study validates the potential of the method in civil engineering crack *** Codes 68T07 © 2025, CC BY-NC-ND.

关键词： Image classification

Causal Inference based Transfer Learning with LLMs: An Efficient Framework for Industrial RUL Prediction

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arXiv 2025年

作者： Chen, Yan Liu, Cheng Department of Systems Engineering City University of Hong Kong Hong Kong Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong

Accurate prediction of Remaining Useful Life (RUL) for complex machinery is critical for industrial prognostics but challenged by high-dimensional, noisy sensor data. We propose the Causal-Informed data Pruning Framework (CIDPF). It pioneers causal inference for industrial data pruning, identifying sensor signals with robust causal relationships to RUL via PCMCI-based stability analysis, while a Gaussian Mixture Model (GMM) screens anomalies. By training on only 10% of data after pruning, CIDPF fine-tunes pre-trained LLMs with parameter-efficient strategies, reducing training time by 90% compared to traditional approach. Experiments on N-CMAPSS show CIDPF achieves 26% lower RMSE than existing methods and 25% improvement over full-data baselines, demonstrating superior accuracy with drastically reduced computational costs. The framework’s adaptability to multi-condition scenarios underscores its practicality for industrial deployment. Copyright © 2025, The Authors. All rights reserved.

关键词： Transfer learning

ELFATT: Efficient Linear Fast Attention for Vision Transformers

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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

TOWARDS SCALABLE TOPOLOGICAL REGULARIZERS

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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

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

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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

Research on Phoneme Recognition using Attention-based Methods 11

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Research on Phoneme Recognition using Attention-based Method...

11th International Conference on Computing and Pattern Recognition, ICCPR 2022

作者： Zhang, Yupei Centre for Intelligent Multidimensional Data Analysis Limited Hong Kong

ISBN: (纸本)9781450397056

A phoneme is the smallest sound unit of a language. Every language has its corresponding phonemes. Phoneme recognition can be used in speech-based applications such as auto speech recognition and lip sync. This paper proposes an end-to-end deep learning model called Connectionist Temporal Classification (CTC) and attention-based seq2seq network that consists of one bi-GRU layer in the encoder and one GRU layer in the decoder, for recognizing the phonemes in speech. Experiments on the TIMIT dataset demonstrate its advantages on some other seq2seq networks, with over 50% improvements after applying the attention mechanism. © 2022 ACM.

关键词： Decoding

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

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Neurocomputing 2025年 645卷

作者： Muhammad A.A. Abdelgawad Ray C.C. Cheung Hong Yan 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.

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