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High-order Topology for Deep Single-Cell Multiview Fuzzy Clustering

IEEE TRANSACTIONS ON FUZZY SYSTEMS 2024年第8期32卷 4448-4459页

作者： Hu, Dayu Dong, Zhibin Liang, Ke Yu, Hao Wang, Siwei Liu, Xinwang Natl Univ Def Technol Sch Comp Changsha 410073 Peoples R China Intelligent Game & Decis Lab Beijing 100071 Peoples R China

Single-cell multiview clustering is essential for analyzing the different cell subtypes of the same cell from different views. Some attempts have been made, but most of these models still struggle to handle single-cell sequencing data, primarily due to their nonspecific design for cellular data. We observe that such data distinctively exhibits: 1) a profusion of high-order topological correlations, 2) a disparate distribution of information across different views, and 3) inherent fuzzy characteristics, indicating a cell's potential to associate with multiple cluster identities. Neglecting these key cellular patterns could significantly impair medical clustering. In response, we propose a specialized application of fuzzy clustering for single-cell sequencing data, namely, the deep single-cell multiview fuzzy clustering method. Concretely, we employ a random walk technique to capture high-order topological relationships on the cell graph and have developed a cross-view information aggregation mechanism that adaptively assigns weights to different views. Furthermore, to accurately reflect the dynamic insight in cellular development, we propose a deep fuzzy clustering strategy that allows cells to associate with diverse clusters. Extensive experiments conducted on three real-world single-cell multiview datasets demonstrate our method's superior performance.

关键词： Sequential analysis Clustering methods Clustering algorithms Fuzzy systems partitioning algorithms Data models Topology Cross-view aggregation deep fuzzy clustering high-order topology multiview clustering

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Fully Adaptive and Semi-Adaptive Frequency Sweep Algorithm Exploiting Loewner-State Model for EM Simulation of Multiport Systems

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IEEE TRANSACTIONS ON MICROWAVE THEORY AND TECHNIQUES 2025年

作者： Shilpa, T. N. Sinha, Rakesh Natl Inst Technol Rourkela Dept Elect Engn Rourkela 769008 India

This article employs a fully adaptive and semi-adaptive frequency sweep (AFS) algorithm using the Loewner matrix (LM)-based state model for the electromagnetic simulation. The proposed algorithms use two LM models with different or the same orders with small frequency perturbation for adaptive frequency selection. The error between the two models is calculated in each iteration, and the next frequency points are selected to minimize maximum error. With the help of memory, the algorithm terminates when the error between the model and the simulation result is reached within the specified error tolerance. In the fully AFS algorithm, the method starts with the minimum and maximum frequency of simulation. In the semi-adaptive algorithm, a novel approach has been proposed to determine the initial number of frequency points necessary for system interpolation based on the electrical size of the structure. The proposed algorithms have been compared with the Stoer-Bulirsch (SB) algorithm and Pradovera's minimal sampling algorithm for electromagnetic simulation. Four examples are presented using MATLAB R2024b. The results show that the proposed methods offer better performance in terms of speed, accuracy, and the requirement of the minimum number of frequency samples. The proposed method shows remarkable consistency with full-wave simulation data, and the algorithm can be effectively applicable to electromagnetic simulations.

关键词： Mathematical models Adaptation models Interpolation Approximation algorithms Electromagnetics partitioning algorithms Integrated circuit modeling Radio frequency Polynomials Adaptive systems Adaptive frequency sweep (AFS) EM modeling Loewner matrix (LM) Pradovera's algorithm state model Stoer-Bulirsch (SB) algorithm

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A Comprehensive Study of Load Balancing Approaches in Real-Time Multi-Core Systems for Mixed Real-Time Tasks

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IEEE ACCESS 2024年 12卷 53373-53395页

作者： Jadon, Shruti Kannan, Pradyuman Kavedi Kalaria, Urmil Varsha, K. R. Gupta, Karthik Honnavalli, Prasad B. PES Univ Dept Comp Sci Engn Bengaluru 560085 India

Real-time systems are becoming pervasive with the growing global connectivity and rising consumer demands. The need for real-time processing has become a crucial part of many business applications worldwide. A key factor that determines the time taken for an application to give out the result hinges on its ability to prioritize, manage, and execute real-time workloads. However, there are several difficulties and constraints connected with implementing tasks in a real-time context. This research study primarily focuses on load balancing for mixed real-time tasks on a multi-core system, one of the major challenges for executing real-time workloads. The purpose of load balancing is to distribute the load evenly among the processor(s) and maximize their utility while minimizing overall execution time. The goal of this paper is to present a critical analysis of existing load balancing techniques for both periodic and aperiodic tasks. The paper explores several factors including throughput, performance, migration time, response time, overhead, resource utilization, scalability, fault tolerance, power efficiency, and other variables that play a crucial role in assessing the efficacy of load balancing in real-time systems. The proposed has contributed in four folds. Firstly, the state-of-the-art of various load balancing algorithms are discussed followed by the architecture involved in real-time multi-core systems. Later, different load balancing based scheduling algorithms were compared on the basis of different schemas and metrics for algorithm evaluation is also provided. Finally, the paper also identifies areas that warrant further exploration or investigation, suggesting potential avenues for future research, and highlighting emerging trends or developments that may shape the field.

关键词： Task analysis Real-time systems Heuristic algorithms Scheduling Load management partitioning algorithms Scheduling algorithms real-time systems priority-driven algorithms EDF (earliest deadline first) RM (rate-monotonic) LBPSA (load balanced partitioning and scheduling algorithm) homogeneous multi-core heterogeneous multi-core static algorithms dynamic algorithms

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Dual Consensus Anchor Learning for Fast Multi-View Clustering

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2024年 33卷 5298-5311页

作者： Qin, Yalan Qin, Chuan Zhang, Xinpeng Feng, Guorui Shanghai Univ Sch Commun & Informat Engn Shanghai 200444 Peoples R China Univ Shanghai Sci & Technol Sch Opt Elect & Comp Engn Shanghai 200093 Peoples R China

Multi-view clustering usually attempts to improve the final performance by integrating graph structure information from different views and methods based on anchor are presented to reduce the computation cost for datasets with large scales. Despite significant progress, these methods pay few attentions to ensuring that the cluster structure correspondence between anchor graph and partition is built on multi-view datasets. Besides, they ignore to discover the anchor graph depicting the shared cluster assignment across views under the orthogonal constraint on actual bases in factorization. In this paper, we propose a novel Dual consensus Anchor Learning for Fast multi-view clustering (DALF) method, where the cluster structure correspondence between anchor graph and partition is guaranteed on multi-view datasets with large scales. It jointly learns anchors, constructs anchor graph and performs partition under a unified framework with the rank constraint imposed on the built Laplacian graph and the orthogonal constraint on the centroid representation. DALF simultaneously focuses on the cluster structure in the anchor graph and partition. The final cluster structure is simultaneously shown in the anchor graph and partition. We introduce the orthogonal constraint on the centroid representation in anchor graph factorization and the cluster assignment is directly constructed, where the cluster structure is shown in the partition. We present an iterative algorithm for solving the formulated problem. Extensive experiments demonstrate the effectiveness and efficiency of DALF on different multi-view datasets compared with other methods.

关键词： Matrix decomposition Clustering methods Clustering algorithms partitioning algorithms Laplace equations Periodic structures Optimization Multi-view clustering unified framework cluster structure rank constraints anchors efficiency

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LocalTGEP: A Lightweight Edge Partitioner for Time-Varying Graph

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IEEE TRANSACTIONS ON EMERGING TOPICS IN COMPUTING 2024年第2期12卷 455-466页

作者： Ji, Shengwei Bu, Chenyang Li, Lei Wu, Xindong Hefei Univ Sch Artificial Intelligence & Big Data Hefei 230039 Peoples R China Hefei Univ Technol Key Lab Knowledge Engn Big Data Minist Educ Hefei 230002 Peoples R China Hefei Univ Technol Inst Big Knowledge Sci Sch Comp Sci & Informat Engn Hefei 230002 Peoples R China Hefei Univ Technol Inst Big Knowledge Sci Hefei 230002 Peoples R China

Graph edge partitioning (GEP), the allocation of edges into different parts through cut vertices, is essential for the analytics of large-scale graphs. Most GEP models cannot be directly applied to a time-varying graph unless repartitioning the entire graph, which leads to a large consumption of resources. Although a few studies have focused on time-varying graph edge partitioning, they have ignored the memory consumption during the partitioning process. Therefore, a lightweight edge partitioner, referred to as LocalTGEP, broadening the application to time-varying graphs, is proposed herein. Three superiorities of LocalTGEP are highlighted as follows: 1) A satisfactory partitioning quality for a time-varying graph can be achieved without requiring global information owing to the local edge partitioning. 2) Memory consumption of the partitioner is significantly reduced using a novel storage framework of graph data in LocalTGEP. 3) The quality and efficiency of time-varying graph edge partitioning are optimized by designing the push and pop stages in LocalTGEP. Extensive experimental results obtained on 12 real-world graphs demonstrate that LocalTGEP outperforms rival algorithms in terms of memory consumption, partitioning quality, and efficiency.

关键词： Memory management Image edge detection partitioning algorithms Data models Knowledge engineering Big Data Social networking (online) Graph edge partitioning local information time-varying graph

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The Power of Two Matrices in Spectral algorithms for Community Recovery

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IEEE TRANSACTIONS ON INFORMATION THEORY 2024年第5期70卷 3599-3621页

作者： Dhara, Souvik Gaudio, Julia Mossel, Elchanan Sandon, Colin Purdue Univ Sch Ind Engn W Lafayette IN 47907 USA Northwestern Univ Dept Ind Engn & Management Sci Evanston IL 60208 USA Massachussetts Inst Technol Dept Math Cambridge MA 02139 USA Ecole Polytech Fed Lausanne Chair Math Data Sci CH-1015 Lausanne Switzerland

Spectral algorithms are some of the main tools in optimization and inference problems on graphs. Typically, the graph is encoded as a matrix and eigenvectors and eigenvalues of the matrix are then used to solve the given graph problem. Spectral algorithms have been successfully used for graph partitioning, hidden clique recovery and graph coloring. In this paper, we study the power of spectral algorithms using two matrices in a graph partitioning problem. We use two different matrices resulting from two different encodings of the same graph and then combine the spectral information coming from these two matrices. We analyze a two-matrix spectral algorithm for the problem of identifying latent community structure in large random graphs. In particular, we consider the problem of recovering community assignments exactly in the censored stochastic block model, where each edge status is revealed independently with some probability. We show that spectral algorithms based on two matrices are optimal and succeed in recovering communities up to the information theoretic threshold. Further, we show that for most choices of the parameters, any spectral algorithm based on one matrix is suboptimal. The latter observation is in contrast to our prior works (2022a, 2022b) which showed that for the symmetric Stochastic Block Model and the Planted Dense Subgraph problem, a spectral algorithm based on one matrix achieves the information theoretic threshold. We additionally provide more general geometric conditions for the (sub)-optimality of spectral algorithms.

关键词： Encoding Stochastic processes partitioning algorithms Symmetric matrices Classification algorithms Clustering algorithms Optimization Stochastic block model spectral algorithms information-theoretic boundary

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Redundancy-Free and Load-Balanced TGNN Training With Hierarchical Pipeline Parallelism

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第11期35卷 1904-1919页

作者： Xia, Yaqi Zhang, Zheng Yang, Donglin Hu, Chuang Zhou, Xiaobo Chen, Hongyang Sang, Qianlong Cheng, Dazhao Wuhan Univ Sch Comp Sci Wuhan 430072 Hubei Peoples R China Nvidia Corp Santa Clara CA 95051 USA Univ Macau IOTSC Macau 999078 Peoples R China Univ Macau Dept Comp & Informat Sci Macau 999078 Peoples R China Zhejiang Lab Res Ctr Graph Comp Hangzhou 311100 Peoples R China

Recently, Temporal Graph Neural Networks (TGNNs), as an extension of Graph Neural Networks, have demonstrated remarkable effectiveness in handling dynamic graph data. Distributed TGNN training requires efficiently tackling temporal dependency, which often leads to excessive cross-device communication that generates significant redundant data. However, existing systems are unable to remove the redundancy in data reuse and transfer, and suffer from severe communication overhead in a distributed setting. This work introduces Sven, a co-designed algorithm-system library aimed at accelerating TGNN training on a multi-GPU platform. Exploiting dependency patterns of TGNN models, we develop a redundancy-free graph organization to mitigate redundant data transfer. Additionally, we investigate communication imbalance issues among devices and formulate the graph partitioning problem as minimizing the maximum communication balance cost, which is proved to be an NP-hard problem. We propose an approximation algorithm called Re-FlexBiCut to tackle this problem. Furthermore, we incorporate prefetching, adaptive micro-batch pipelining, and asynchronous pipelining to present a hierarchical pipelining mechanism that mitigates the communication overhead. Sven represents the first comprehensive optimization solution for scaling memory-based TGNN training. Through extensive experiments conducted on a 64-GPU cluster, Sven demonstrates impressive speedup, ranging from 1.9x to 3.5x, compared to State-of-the-Art approaches. Additionally, Sven achieves up to 5.26x higher communication efficiency and reduces communication imbalance by up to 59.2%.

关键词： Training Graph neural networks Pipeline processing partitioning algorithms Heuristic algorithms Approximation algorithms Task analysis Communication balance distributed training dynamic GNN pipeline parallelism redundancy-free

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Incomplete Multiple Kernel Alignment Maximization for Clustering

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2024年第3期46卷 1412-1424页

作者： Liu, Xinwang Natl Univ Def Technol Coll Comp Changsha 410073 Peoples R China

Multiple kernel alignment (MKA) maximization criterion has been widely applied into multiple kernel clustering (MKC) and many variants have been recently developed. Though demonstrating superior clustering performance in various applications, it is observed that none of them can effectively handle incomplete MKC, where parts or all of the pre-specified base kernel matrices are incomplete. To address this issue, we propose to integrate the imputation of incomplete kernel matrices and MKA maximization for clustering into a unified learning framework. The clustering of MKA maximization guides the imputation of incomplete kernel elements, and the completed kernel matrices are in turn combined to conduct the subsequent MKC. These two procedures are alternately performed until convergence. By this way, the imputation and MKC processes are seamlessly connected, with the aim to achieve better clustering performance. Besides theoretically analyzing the clustering generalization error bound, we empirically evaluate the clustering performance on several multiple kernel learning (MKL) benchmark datasets, and the results indicate the superiority of our algorithm over existing state-of-the-art counterparts.

关键词： Kernel Clustering algorithms Optimization Task analysis Pattern analysis partitioning algorithms Minimization Multiple kernel clustering multi-view clustering kernel alignment maximization

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Optimizing Task Orchestration for Distributed Real-Time Electromagnetic Transient Simulation

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IEEE ACCESS 2024年 12卷 74818-74830页

作者： Guo, Qi Guo, Haiping Lu, Yuanhong Guo, Tianyu Zhang, Jie Zhang, Jingyue Luo, Hui Huang, Libin Zhao, Yanjun China Southern Power Grid Elect Power Res Inst State Key Lab HVDC Guangzhou 510663 Peoples R China Guangdong Prov Key Lab Intelligent Operat & Contro Guangzhou 510663 Peoples R China China Southern Power Grid Elect Power Res Inst CSG Key Lab Power Syst Simulat Guangzhou 510663 Peoples R China China Southern Power Grid Co Ltd Guangzhou Peoples R China

Transient simulation in power engineering is crucial as it models the dynamic behavior of power systems during sudden events like faults or short circuits. Electromagnetic transient simulations involve multiple coordinated tasks. Traditional simulations are centralized and struggle to meet scalability requirements. To achieve these goals, distributed electromagnetic transient simulation has emerged as a new trend. Nevertheless, the distributed electromagnetic transient simulation introduces network communication. Achieving real-time simulation across distributed nodes poses the challenge of minimizing communication costs. In this paper, our proposal focuses on optimizing the task orchestration to reduce communication costs. Specifically, in the electromagnetic transient simulation, these tasks has certain communication pattern where the communicated objects of each task are pre-defined. We represent the pattern as a graph, with tasks represented as nodes and communications as edges. Furthermore, we propose to use graph partition with the objective of minimal communication costs and fine tune the partitions with the resource requirements of each distributed node. The experimental results demonstrate that our proposal has strength in achieving high-performance electromagnetic transient simulation.

关键词： Task analysis Real-time systems Power system dynamics Costs partitioning algorithms Transient analysis Proposals Electromagnetic transients Distributed processing Power distribution Electromagnetic transient simulation distributed system task orchestration

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Load Balancing With Traffic Splitting for QoS Enhancement in 5G HetNets

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IEEE TRANSACTIONS ON NETWORK SCIENCE AND ENGINEERING 2024年第6期11卷 6272-6284页

作者： Manan, Abdul Shahid, Syed Maaz Kim, SungKyung Kwon, Sungoh Univ Ulsan Dept Elect Elect & Comp Engn Ulsan 44610 South Korea Elect & Telecommun Res Inst ETRI Intelligent Small Cell Res Sect Daejeon 34129 South Korea

In heterogeneous networks (HetNets), high user density and random small cell deployment often result in uneven User Equipment (UE) distributions among cells. This can lead to excessive resource usage in some cells and a degradation of Quality of Service (QoS) for users, even while resources in other cells remain underutilized. To address this challenge, we propose a load-balancing algorithm for 5G HetNets that employs traffic splitting for dual connectivity (DC) users. By enabling traffic splitting, DC allows UEs to receive data from both macro and small cells, thereby enhancing network performance in terms of load balancing and QoS improvement. To prevent cell overloading, we formulate the problem of minimizing load variance across 5G HetNet cells using traffic splitting. We derive a theoretical expression to determine the optimal split ratio by considering the cell load conditions. The proposed algorithm dynamically adjusts the data traffic split for DC users based on the optimal split ratio and, if necessary, offloads edge users from overloaded macro cells to underloaded macro cells to achieve uniform network load distribution. Simulation results demonstrate that the proposed algorithm achieves more even load distribution than other load balancing algorithms and increases network throughput and the number of QoS-satisfied users.

关键词： Computer architecture Microprocessors Quality of service 5G mobile communication Load management Resource management Throughput Optimization Network architecture partitioning algorithms 5G HetNets load balancing QoS traffic splitting dual connectivity

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