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An Image Importance Partition-Based Compression Method for COVID-19 Computed Tomography Scan

IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 2022年第7期18卷 4596-4607页

作者： Zhang, Yumo Cai, Zhanchuan Ye, Ben Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China

As the coronavirus disease 2019 (COVID-19) spreads around the world, industrial automated medical diagnosis systems have been developed, which complete a large amount of medical diagnosis work through computed tomography (CT) images. In these systems, how to quickly store and transmit such a large amount of CT image information has important research significance. In this article, a more targeted COVID-19 chest CT image codec is proposed to make image data not only occupy less space but also have higher image quality. First, the bilateral lung contours are extracted to calculate the position information of the region of interest (ROI). Then, a CT image is classified into four types of nonuniform image blocks according to the characteristics of COVID-19 chest CT images and ROI position information. Next, a series of new transformations are proposed for more efficient transform coding. Finally, a flexible quantization strategy is proposed for the adaptive quantization part. In the experiments, the proposed method is superior to some of the existing methods with similar computational complexity. At the same bit rate, it significantly improves the image quality. This means that chest CT images can still be used for disease diagnosis while taking up less space. In addition, because of the low computational complexity of the proposed method, it can be more easily embedded into the CT equipment with low computational power.

关键词： Computed tomography Image coding COVID-19 partitioning algorithms Lung Transform coding Informatics Computed tomography (CT) image compression coronavirus disease 2019 (COVID-19) nonuniform partition (NUP) region of interest (ROI) U-system

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A Fast Community Detection Algorithm Based on Reconstructing Signed Networks

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IEEE SYSTEMS JOURNAL 2022年第1期16卷 614-625页

作者： Xia, Chengyi Luo, Yongping Wang, Li Li, Hui-Jia Tianjin Univ Technol Tianjin Key Lab Intelligence Comp & Novel Softwar Engn Res Ctr Learning Based Intelligent Syst Minist Educ Tianjin 300384 Peoples R China Beijing Univ Posts & Telecommun Sch Sci Beijing 100876 Peoples R China

Signed networks depict the individual cooperative or hostile relationship in a population, which can help to deeply mine the characteristics of complex networks and predict the potential collaboration between individuals by analyzing their interaction within different groups or communities. In this article, first of all, an improved modularity function for signed networks is proposed on the basis of the existing modularity function. Then, a new community detection algorithm for signed networks has also been devised, and time complexity analysis shows that the time required for the algorithm has a linear relationship with the number of nodes in the sparse networks. Meanwhile, the affinity index that can be used to convert directed signed networks into the corresponding undirected signed networks is come up with. Finally, the current algorithm has been applied into several illustrative and realistic networks. The experimental results indicate that the number of communities given by the proposed algorithm is consistent with that of actual communities, and thus, it can be further conducive to identifying the community structure hidden within the real-world systems.

关键词： Indexes partitioning algorithms Image edge detection Clustering algorithms Oscillators Heuristic algorithms Detection algorithms Affinity index community detection community structure modularity network reconstruction signed network

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Weighted Multiview Possibilistic C-Means Clustering With L2 Regularization

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第5期30卷 1357-1370页

作者： Benjamin, Josephine Bernadette M. Yang, Miin-Shen Chung Yuan Christian Univ Dept Appl Math Chungli 32023 Taiwan Univ Santo Tomas Dept Math Manila Philippines

Since social media, virtual communities and networks rapidly grow, multiview data become more popular. In general, multiview data always contain different feature components in different views. Although these data are extracted in different ways (views) from diverse settings and domains, they are used to describe the same samples, which make them highly related. Hence, applying (single-view) clustering methods for multiview data poses difficulty in achieving desirable clustering results. Thus, multiview clustering methods should be developed that will utilize available multiview information. Most of multiview clustering techniques currently use k-means due to its conceptual simplicity, and use fuzzy c-means (FCM) that the datapoints can belong to more than one cluster based on their membership degrees from 0 to 1. However, the use of k-means or FCM may degrade its performance due to the presence of noise and outliers, especially on large or high-dimensional datasets. The constraint imposed on the membership degrees of k-means and FCM tends to assign a corresponding high membership value to an outlier or a noisy data point. To address these drawbacks, possibilistic c-means (PCM) relaxes the membership constraint of k-means and FCM so that outliers and noisy datapoints can be properly identified. On the other hand, there are various extensions of k-means and FCM for multiview data, but no extension of PCM for multiview data was made in the literature. Thus, we use PCM in our proposed multiview clustering model. In this article, we propose novel weighted multiview PCM algorithms designed for clustering multiview data as well as view and feature weights on PCM approaches, called W-MV-PCM and W-MV-PCM with L2 regularization (W-MV-PCM-L2). In multiview clustering, different views may vary with respect to its importance and each view may contain some irrelevant features. In the proposed algorithms, a learning scheme is constructed to compute for the view weights, and featu

关键词： Clustering algorithms Phase change materials Linear programming partitioning algorithms Indexes Task analysis Parameter estimation Clustering fuzzy c-means (FCM) multiview clustering multiview data possibilistic c-means (PCM) weighted weighted multiview PCM (W-MV-PCM)

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Paving the Way for Consensus: Convergence of Block Gossip algorithms

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第11期68卷 7515-7527页

作者： Haddock, Jamie Jarman, Benjamin Yap, Chen Harvey Mudd Coll Dept Math Claremont CA 91711 USA Univ Calif Los Angeles Dept Math Los Angeles CA 90095 USA Planet Labs Inc San Francisco CA 94107 USA

Gossip protocols are popular methods for average consensus problems in distributed computing. We prove new convergence guarantees for a variety of such protocols, including path, clique, and synchronous pairwise gossip. These arise by exploiting the connection between these protocols and the block randomized Kaczmarz method for solving linear systems. Moreover, we extend existing convergence results for block randomized Kaczmarz to allow for a more general choice of blocks, rank-deficient systems, and provide a tighter convergence rate guarantee. Previous results for block randomized Kaczmarz assumed blocks formed a paving of the systems;our analysis, contrary to the title of our paper, generalizes to a broader class of blocks that we call coverings. We furthermore apply this analysis to inconsistent consensus models and obtain similar guarantees. An extensive empirical analysis of these methods is provided for a variety of synthetic and real networks.

关键词： Protocols Convergence Linear systems Iterative methods Eigenvalues and eigenfunctions partitioning algorithms Distributed computing Average consensus randomized block gossip algorithms linear systems stochastic iterative methods randomized block Kaczmarz

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Explored Normalized Cut With Random Walk Refining Term for Image Segmentation

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2022年 31卷 2893-2906页

作者： Zhu, Lei Kang, Xuejing Ye, Lizhu Ming, Anlong Beijing Univ Posts & Telecommun Natl Pilot Software Engn Sch Sch Comp Sci Beijing 100876 Peoples R China

The Normalized Cut (NCut) model is a popular graph-based model for image segmentation. But it suffers from the excessive normalization problem and weakens the small object and twig segmentation. In this paper, we propose an Explored Normalized Cut (ENCut) model that establishes a balance graph model by adopting a meaningful-loop and a k-step random walk, which reduces the energy of small salient region, so as to enhance the small object segmentation. To improve the twig segmentation, our ENCut model is further enhanced by a new Random Walk Refining Term (RWRT) that adds local attention to our model with the help of an un-supervising random walk. Finally, a move-making based strategy is developed to efficiently solve the ENCut model with RWRT. Experiments on three standard datasets indicate that our model can achieve state-of-the-art results among the NCut-based segmentation models.

关键词： Image segmentation Image edge detection Task analysis Computational modeling Analytical models partitioning algorithms Object segmentation Normalized Cut (NCut) image segmentation unsupervised learning graph signal processing

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Transfer Collaborative Fuzzy Clustering in Distributed Peer-to-Peer Networks

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第2期30卷 500-514页

作者： Dang, Bozhan Wang, Yingxu Zhou, Jin Wang, Rongrong Chen, Long Chen, C. L. Philip Zhang, Tong Han, Shiyuan Wang, Lin Chen, Yuehui Univ Jinan Shandong Prov Key Lab Network Based Intelligent C Jinan 250022 Peoples R China Univ Macau Fac Sci & Technol Dept Comp & Informat Sci Macau 999078 Peoples R China South China Univ Technol Sch Comp Sci & Engn Guangzhou 510641 Peoples R China

The traditional collaborative fuzzy clustering can effectively perform data clustering in distributed peer-to-peer networks, which is an impossible task to complete for the centralized clustering methods due to privacy and security requirements or network transmission technology constraints. But it will increase the number of clustering iterations and lead to lower efficiency of the clustering. Moreover, the collaborative mechanism hidden in the iterative process of clustering cannot be well revealed and explained. In this article, a novel series of transfer collaborative fuzzy clustering algorithms are proposed to solve these issues. In the first basic algorithm, the transfer learning among neighbor nodes vividly expresses the collaborative mechanism and enhances the information collaboration to accelerate the convergence of fuzzy clustering. Meanwhile, neighbor nodes can learn the knowledge from each other to further promote their respective clustering performance. Then, an improved version, with the learning-rate-adjustable strategy instead of fixed values, is designed to highlight the different influence between neighbor nodes, and the appropriate learning rates between neighbor nodes are achieved to ensure the stable clustering accuracy. Finally, two extended versions with the attribute-weight-entropy regularization technique are presented for the clustering of high dimensional sparse data and the extraction of important subspace features. Experiments show the efficiency of the proposed algorithms compared with the related prototype-based clustering methods.

关键词： Clustering algorithms Collaboration Peer-to-peer computing Distributed databases Clustering methods Prototypes partitioning algorithms Adjustable learning rate attribute-weight-entropy regularization collaborative fuzzy clustering distributed peer-to-peer network transfer learning

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Improved Clustering algorithms for the Bipartite Stochastic Block Model

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IEEE TRANSACTIONS ON INFORMATION THEORY 2022年第3期68卷 1960-1975页

作者： Ndaoud, Mohamed Sigalla, Suzanne Tsybakov, Alexandre B. ESSEC Business Sch Dept Decis Sci F-95000 Cergy France CREST ENSAE Paris Dept Stat F-91120 Palaiseau France

We establish sufficient conditions of exact and almost full recovery of the node partition in Bipartite Stochastic Block Model (BSBM) using polynomial time algorithms. First, we improve upon the known conditions of almost full recovery by spectral clustering algorithms in BSBM. Next, we propose a new computationally simple and fast procedure achieving exact recovery under milder conditions than the state of the art. Namely, if the vertex sets V-1 and V-2 in BSBM have sizes n(1) and n(2), we show that the condition p = Omega (max (root log n(1)/n(1)n(2), log n(1)/n(2))) on the edge intensity p is sufficient for exact recovery within V-1. This condition exhibits an elbow at n(2) asymptotic to n(1) log n(1) between the low-dimensional and high-dimensional regimes. The suggested procedure is a variant of Lloyd's iterations initialized with a well-chosen spectral estimator leading to what we expect to be the optimal condition for exact recovery in BSBM. The optimality conjecture is supported by showing that, for a supervised oracle procedure, such a condition is necessary to achieve exact recovery. The key elements of the proof techniques are different from classical community detection tools on random graphs. Numerical studies confirm our theory, and show that the suggested algorithm is both very fast and achieves almost the same performance as the supervised oracle. Finally, using the connection between planted satisfiability problems and the BSBM, we improve upon the sufficient number of clauses to completely recover the planted assignment.

关键词： Stochastic processes Gaussian mixture model Clustering algorithms partitioning algorithms Unsupervised learning Random variables Mixture models Bipartite stochastic block model exact recovery almost full recovery spectral methods clustering phase transition

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TiC2D: Trajectory Inference From Single-Cell RNA-Seq Data Using Consensus Clustering

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第4期19卷 2512-2522页

作者： Gan, Yanglan Li, Ning Guo, Cheng Zou, Guobing Guan, Jihong Zhou, Shuigeng Donghua Univ Sch Comp Sci & Technol Shanghai 200051 Peoples R China Shanghai Univ Sch Comp Engn & Sci Shanghai 200444 Peoples R China Tongji Univ Dept Comp Sci & Technol Shanghai 200092 Peoples R China Fudan Univ Sch Comp Sci Shanghai Key Lab Intelligent Informat Proc Shanghai 200433 Peoples R China

Cellular programs often exhibit strong heterogeneity and asynchrony in the timing of program execution. Single-cell RNA-seq technology has provided an unprecedented opportunity for characterizing these cellular processes by simultaneously quantifying many parameters at single-cell resolution. Robust trajectory inference is a critical step in the analysis of dynamic temporal gene expression, which can shed light on the mechanisms of normal development and diseases. Here, we present TiC2D, a novel algorithm for cell trajectory inference from single-cell RNA-seq data, which adopts a consensus clustering strategy to precisely cluster cells. To evaluate the power of TiC2D, we compare it with three state-of-the-art methods on four independent single-cell RNA-seq datasets. The results show that TiC2D can accurately infer developmental trajectories from single-cell transcriptome. Furthermore, the reconstructed trajectories enable us to identify key genes involved in cell fate determination and to obtain new insights about their roles at different developmental stages.

关键词： Trajectory Clustering algorithms partitioning algorithms Gene expression Diseases Computer science Manifolds Trajectory inference consensus clustering gene partition single-cell transcriptome

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Multiobjective Evolutionary Interpretable Type-2 Fuzzy Systems With Structure and Parameter Learning for Hexapod Robot Control

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IEEE TRANSACTIONS ON SYSTEMS MAN CYBERNETICS-SYSTEMS 2022年第5期52卷 3066-3078页

作者： Su, Wu-Chung Juang, Chia-Feng Hsu, Chi-Ming Natl Chung Hsing Univ Dept Elect Engn Taichung 402 Taiwan

This article proposes a data-driven multiobjective evolutionary interval type-2 fuzzy-logic system (IT2FLS) learning approach that considers both system performance and rule interpretability. One characteristic of the evolutionary learning framework is that the IT2FLS structure is incrementally generated instead of searching from an initially huge grid-type rule base, which significantly reduces the parameter search space. Another one is that a new constrained objective function is proposed to improve the distinguishability of the generated interval type-2 fuzzy sets (FSs) without restricting them to be evenly distributed. Because of the tradeoff between system performance and interpretability, a new multiobjective ant colony optimization (ACO) algorithm is proposed to optimize the IT2FLS parameters and improve optimization performance. The evolutionary IT2FLS learning approach is applied to control a real wall-following hexapod robot. The approach shows the advantages of model-free design and interpretability and robustness to noise in the evolved type-2 fuzzy rules. Simulations with performance comparisons and experiments in controlling a real robot show the advantages of the learning approach.

关键词： Frequency selective surfaces Optimization Robots Linear programming Robot sensing systems Input variables partitioning algorithms Ant colony optimization (ACO) evolutionary fuzzy systems evolutionary robots interpretable fuzzy systems multiobjective optimization type-2 fuzzy systems

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Multi-Level Area Balancing of Clustered Graphs

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2022年第7期28卷 2682-2696页

作者： Wu, Hsiang-Yun Nollenburg, Martin Viola, Ivan TU Wien A-1040 Vienna Austria King Abdullah Univ Sci & Technol KAUST Thuwal 23955 Saudi Arabia

We present a multi-level area balancing technique for laying out clustered graphs to facilitate a comprehensive understanding of the complex relationships that exist in various fields, such as life sciences and sociology. Clustered graphs are often used to model relationships that are accompanied by attribute-based grouping information. Such information is essential for robust data analysis, such as for the study of biological taxonomies or educational backgrounds. Hence, the ability to smartly arrange textual labels and packing graphs within a certain screen space is therefore desired to successfully convey the attribute data . Here we propose to hierarchically partition the input screen space using Voronoi tessellations in multiple levels of detail. In our method, the position of textual labels is guided by the blending of constrained forces and the forces derived from centroidal Voronoi cells. The proposed algorithm considers three main factors: (1) area balancing, (2) schematized space partitioning, and (3) hairball management. We primarily focus on area balancing, which aims to allocate a uniform area for each textual label in the diagram. We achieve this by first untangling a general graph to a clustered graph through textual label duplication, and then coupling with spanning-tree-like visual integration. We illustrate the feasibility of our approach with examples and then evaluate our method by comparing it with well-known conventional approaches and collecting feedback from domain experts.

关键词： Layout Visualization Clustering algorithms Data visualization Shape partitioning algorithms Chemical elements Graph drawing Voronoi tessellation multi-level spatially-efficient layout

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