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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...

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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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Exposing Computer-Generated Images Via Amplified Texture Differences Learning

Exposing Computer-Generated Images Via Amplified Texture Dif...

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2023 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2023

作者： Xu, Qiang Wang, Zhe Mi, Zhongjie Yan, Hong Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Department of Electrical Engineering Kowloon Hong Kong Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Department of Advanced Design and Systems Engineering Hong Kong School of Electronic Information and Electrical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (纸本)9798350337020

Many Computer-Generated (CG) images are spreading widely on the Internet, which may deliberately misinform or deceive the public. Therefore, distinguishing CG images from natural photographic (PG) has become a frontier research topic in the field of image forensics. Although many algorithms have been proposed, it is still very challenging to detect CG images generated by the recent cutting-edge generative methods. Besides, most existing algorithms tend to generalize poorly when facing different unseen multimodal generative models. To address this issue, a novel method based on amplified texture differences learning is proposed to tackle this problem. We first design a deep texture enhancement module for discriminative texture amplification. Specifically, a semantic segmentation module is utilized to generate semantic segmentation map for the affine transformation operation guidance, which can be further used to recover the texture in different regions of the input image. Then, the combination of the original image and the high-frequency components of the original and enhanced images are fed into a hybrid neural network equipped with attention mechanisms, which refines intermediate features and facilitates trace exploration in spatial and channel dimensions respectively. By verifying on several commonly used benchmark datasets and a newly constructed dataset11The benchmark is available at https://***/191578010/DSGCG. with more realistic and diverse images, the experimental results demonstrate that the proposed approach outperforms some existing methods. © 2023 IEEE.

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RO-SVD: A Reconfigurable Hardware Copyright Protection Framework for AIGC Applications

arXiv

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arXiv 2024年

作者： Ran, Zhuoheng Abdelgawad, Muhammad A.A. Zhang, Zekai Cheung, Ray C.C. Yan, Hong Department of Electrical Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong

The dramatic surge in the utilisation of generative artificial intelligence (GenAI) underscores the need for a secure and efficient mechanism to responsibly manage, use and disseminate multi-dimensional data generated by artificial intelligence (AI). In this paper, we propose a blockchain-based copyright traceability framework called ring oscillator-singular value decomposition (RO-SVD), which introduces decomposition computing to approximate low-rank matrices generated from hardware entropy sources and establishes an AI-generated content (AIGC) copyright traceability mechanism at the device level. By leveraging the parallelism and reconfigurability of field-programmable gate arrays (FPGAs), our framework can be easily constructed on existing AI-accelerated devices and provide a low-cost solution to emerging copyright issues of AIGC. We developed a hardware-software (HW/SW) co-design prototype based on comprehensive analysis and on-board experiments with multiple AI-applicable FPGAs. Using AI-generated images as a case study, our framework demonstrated effectiveness and emphasised customisation, unpredictability, efficiency, management and reconfigurability. To the best of our knowledge, this is the first practical hardware study discussing and implementing copyright traceability specifically for AI-generated content. Copyright © 2024, The Authors. All rights reserved.

关键词： Reconfigurable hardware

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A survey of superpixel methods and their applications

TechRxiv

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TechRxiv 2024年

作者： Wu, Chong Yan, Hong Department of Electrical Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Kowloon Hong Kong

Superpixels can preserve the structure and reduce the redundancy of the original image. Because of these advantages, superpixel generation or superpixel segmentation is widely used as a pre-processing step in many image processing tasks. Although superpixels can be employed to reduce computational complexity, some challenges, such as the non-Euclidean feature learning problem introduced by superpixels, still exist. This survey provides a comprehensive overview of the state-of-the-art superpixel methods, major challenges, commonly used evaluation metrics, applications of superpixels, and potential future directions for the study of superpixels. We first give a review of the state-of-the-art superpixel methods. Next, we use different evaluation metrics to evaluate the performance of 25 up-to-date superpixel methods on different datasets in different noisy environments. After that, we introduce several up-to-date applications of superpixels. Finally, we give several possible future directions for addressing the challenges of superpixels. © 2024, CC BY.

关键词： Superpixels

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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)

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RO-SVD: A Reconfigurable Hardware Copyright Protection Framework for AIGC Applications

RO-SVD: A Reconfigurable Hardware Copyright Protection Frame...

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International Conference on Application Specific Systems (ASAP), Architectures and Processors

作者： Zhuoheng Ran Muhammad A.A. Abdelgawad Zekai Zhang Ray C.C. Cheung Hong Yan Department of Electrical Engineering Centre for Intelligent Multidimensional Data Analysis City University of Hong Kong Hong Kong SAR China

ISBN: (数字)9798350349634

ISBN: (纸本)9798350349641

The dramatic surge in the utilisation of generative artificial intelligence (GenAI) underscores the need for a secure and efficient mechanism to responsibly manage, use and disseminate multidimensional data generated by artificial intelligence (AI). In this paper, we propose a blockchain-based copyright traceability framework called ring oscillator-singular value decomposition (RO-SVD), which introduces decomposition computing to approximate low-rank matrices generated from hardware entropy sources and establishes an AI-generated content (AIGC) copyright traceability mechanism at the device level. By leveraging the parallelism and reconfigurability of field-programmable gate arrays (FPGAs), our framework can be easily constructed on existing AI -accelerated devices and provide a low-cost solution to emerging copyright issues of AIGC. We developed a hardware-software (HW /SW) co-design prototype based on comprehensive analysis and on-board experiments with multiple AI-applicable FPGAs. Using AI-generated images as a case study, oyr framework demonstrated effectiveness and emphasised customisation, unpredictability, efficiency, manage-ment and reconfigurability. To the best of our knowledge, this is the first practical hardware study discussing and implementing copyright traceability specifically for AI -generated conten t.

关键词： Program processors Generative AI Systems architecture Surge protection Prototypes Parallel processing Hardware

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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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Cross-Domain Object Classification Via Successive Subspace Alignment

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

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

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

关键词： Training Learning systems Degradation Deep learning Visualization Neural networks Signal processing

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CShaperApp:Segmenting and analyzing cellular morphologies of the developing Caenorhabditis elegans embryo

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Quantitative Biology 2024年第3期12卷 329-334页

作者： Jianfeng Cao Lihan Hu Guoye Guan Zelin Li Zhongying Zhao Chao Tang Hong Yan Department of Electrical Engineering City University of Hong KongHong KongChina Centre for Intelligent Multidimensional Data Analysis Limited Hong KongChina College of Computer and Information Hohai UniversityNanjingChina Center for Quantitative Biology Peking UniversityBeijingChina Department of Biology Hong Kong Baptist UniversityHong KongChina State Key Laboratory of Environmental and Biological Analysis Hong Kong Baptist UniversityHong KongChina Peking-Tsinghua Center for Life Sciences Peking UniversityBeijingChina School of Physics Peking UniversityBeijingChina

Caenorhabditis elegans has been widely used as a model organism in developmental biology due to its invariant *** this study,we developed a desktop software CShaperApp to segment fluorescence-labeled images of cell membranes and analyze cellular morphologies interactively during *** *** on the previously proposed framework CShaper,CShaperApp empowers biologists to automatically and efficiently extract quantitative cellular morphological data with either an existing deep learning model or a fine-tuned one adapted to their in-house *** results show that it takes about 30 min to process a three-dimensional time-lapse(4D)dataset,which consists of 150 image stacks at a~1.5-min interval and covers *** embryogenesis from the 4-cell to 350-cell *** robustness of CShaperApp is also validated with the datasets from different ***,modularized implementation increases the flexibility in multi-task applications and promotes its flexibility for future *** cell morphology over development has emerged as a focus of interest in developmental biology,CShaperApp is anticipated to pave the way for those studies by accelerating the high-throughput generation of systems-level quantitative data *** software can be freely downloaded from the website of Github(cao13jf/CShaperApp)and is executable on Windows,macOS,and Linux operating systems.

关键词： C.elegans embryogenesis cellular morphology cellular segmentation deep learning desktop software

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