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Improved Clustering algorithms for the Bipartite Stochastic Block Model

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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ARHap: Association Rule Haplotype Phasing

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第6期19卷 3281-3294页

作者： Mazrouee, Sepideh Univ Calif San Diego Med La Jolla CA 92093 USA

This article proposes a novel approach for Individual Human phasing through discovery of interesting hidden relations among single variant sites. The proposed framework, called ARHap, learns strong association rules among variant loci on the genome and develops a combinatorial approach for fast and accurate haplotype phasing based on the discovered associations. ARHap is composed of two main modules or processing phases. In the first phase, called association rule learning, ARHap identifies quantitative association rules from a collection of DNA reads of the organismunder study, resulting in a set of strong rules that reveal the inter-dependency of alleles. In the next phase, called haplotype reconstruction, we develop algorithms to utilize the learned rules to construct highly reliable haplotypes at individual single nucleotide polymorphism(SNP) sites. ARHap has several features that lead to both fast and accurate haplotyping. It uses an incremental haplotype reconstruction approach that enables us to generate association rules according to the unreconstructed SNP sites during each round of the algorithm. During each round, the association rule learning module generates rules while constraining the length of the rules and limiting the rules to those that contribute to reconstruction of unreconstructed sites only. The framework begins by generating rules of small size and highly strong. The rule length can increase and/or criteria about strongness of the rule are adjusted gradually, during subsequent rounds, if some SNP sites have remained unreconstructed. This adaptive approach, which uses feedback from haplotype reconstruction module, eliminates generation of rules that do not contribute to haplotype reconstruction aswell asweak rules that may introduce error in the final haplotypes. Extensive experimental analyses on datasets representing diploid organisms demonstrate superiority of ARHap in diploid haplotyping compared to the state-of-the-art algorithms. In part

关键词： DNA Sequential analysis Bioinformatics partitioning algorithms Organisms Measurement uncertainty Linear programming Computational genetics chromosome reconstruction DNA phasing haplotype assembly

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Discretization Using Combination of Heuristics for High Accuracy With Huge Noise Reduction

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2022年第4期34卷 1710-1722页

作者： Pal, Somnath Ghosh, Saptarshi Biswas, Himika Patwari, Mitesh Indian Inst Engn Sci & Technol Dept Comp Sci & Technol Sibpur 711103 W Bengal India Indian Inst Technol Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

Over the years, several algorithms for discretization have been devised, but the problem of efficient, accurate discretization still remains an open problem. This paper proposes a novel discretization algorithm, called SPID5, based on combination of two heuristics, one being local and the other global, both being supervised and their combination resulting in a significant synergy. The local heuristic is the well-known information gain of the continuous attributes, and the global heuristic is a novel concept of iterative reduction of noise in the data set. The reduction of noise is achieved by reducing successively pseudo deletion count of the data set to be discretized. The performance of SPID5 algorithm is compared with that of three well-known and time-tested discretization algorithms, using six state-of-the-art classifiers and 35 real-world data sets from the standard UCI data repository. Performance of SPID5 compares favorably with that of all the three existing discretization algorithms it is compared with, not only in terms of classification accuracy but also in terms of noise reduction in the data sets.

关键词： Prediction algorithms Noise reduction Heuristic algorithms partitioning algorithms Rough sets Knowledge engineering Data engineering Discretization information gain pseudo deletion count noise reduction classification

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Self-Organizing mmWave MIMO Cell-Free Networks With Hybrid Beamforming: A Hierarchical DRL-Based Design

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IEEE TRANSACTIONS ON COMMUNICATIONS 2022年第5期70卷 3169-3185页

作者： Al-Eryani, Yasser Hossain, Ekram Univ Manitoba Dept Elect & Comp Engn Winnipeg MB R3T 2N2 Canada

In a cell-free wireless network, distributed access points (APs) jointly serve all user equipments (UEs) within their coverage area by using the same time/frequency resources. In this paper, we develop a novel downlink cell-free multiple-input multiple-output (MIMO) millimeter wave (mmWave) network architecture that enables all APs and UEs to dynamically self-partition into a set of independent cell-free subnetworks in a time-slot basis. For this, we propose several network partitioning algorithms based on deep reinforcement learning (DRL). Furthermore, to mitigate interference between different cell-free subnetworks, we develop a novel hybrid analog beamsteering-digital beamforming model that zero-forces interference among cell-free subnetworks and at the same time maximizes the instantaneous sum-rate of all UEs within each subnetwork. Specifically, the hybrid beamforming model is implemented by using a novel mixed DRL-convex optimization method in which analog beamsteering between APs and UEs is conducted based on DRL while digital beamforming is modeled and solved as a convex optimization problem. The DRL models for network clustering and hybrid beamsteering are combined into a single hierarchical DRL design that enables exchange of DRL agents' experiences during both network training and operation. We also benchmark the performance of DRL models for clustering and beamsteering in terms of network performance, convergence rate, and computational complexity. Results show a significant rate enhancement due to the proposed hybrid beamforming scheme compared to its conventional all-digital counterpart. This performance enhancement becomes more significant as the number of network partitions increases. For DRL-based network clustering, the policy gradient (PG) algorithm offers the best possible performance in terms of stability and convergence rate while the state-action-reward-state-action (SARSA) algorithm suffers from significant variance, slower convergence, and s

关键词： Array signal processing Downlink MIMO communication Uplink Network architecture partitioning algorithms Convergence Cell-free MIMO networks mmWave MIMO systems downlink hybrid beamforming deep reinforcement learning (DRL) DRL-based clustering and beamforming convex optimization

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Coverage and Connectivity Aware Energy Charging Mechanism Using Mobile Charger for WRSNs

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IEEE SYSTEMS JOURNAL 2022年第3期16卷 3993-4004页

作者： Kan, Yuanping Chang, Chih-Yung Kuo, Chin-Hwa Roy, Diptendu Sinha Fujian Polytech Normal Univ Fujian 350117 Peoples R China Tamkang Univ New Taipei 25137 Taiwan Natl Inst Technol Shillong 793003 Meghalaya India

Wireless recharging using a mobile charger has been widely discussed in recent years. Most of them considered that all sensors were equally important and aimed to maximize the number of recharged sensors. The purpose of energy recharging is to extend the lifetime of sensors whose major work is to maximize the surveillance quality. In a randomly deployed wireless rechargeable sensor network, the surveillance quality highly depends on the contributions of coverage and network connectivity of each sensor. Instead of considering maximizing the number of recharged sensors, this article further takes into consideration the contributions of coverage and network connectivity of each sensor when making the decision of recharging schedule, aiming to maximize the surveillance quality and improve the number of data collected from sensors to the sink node. This article proposes an energy recharging mechanism, called an energy recharging mechanism for maximizing the surveillance quality of a given WRSNs (ERSQ), which partitions the monitoring region into several equal-sized grids and considers the important factors, including coverage contribution, network connectivity contribution, the remaining energy as well as the path length cost of each grid, aiming to maximize surveillance quality for a given wireless sensor network. Performance studies reveal that the proposed ERSQ outperforms existing recharging mechanisms in terms of the coverage, the number of working sensors as well as the effectiveness index of working sensors.

关键词： Sensors Wireless sensor networks Monitoring Automobiles partitioning algorithms Task analysis Energy consumption Connectivity coverage mobile charger recharging wireless sensor networks (WSNs)

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Partitioned Real-Time Scheduling for Preventing Information Leakage

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IEEE ACCESS 2022年 10卷 22712-22723页

作者： Kang, Donghwa Jung, Inhwa Lee, Kilho Baek, Hyeongboo Incheon Natl Univ INU Dept Comp Sci & Engn Incheon 22012 South Korea Soongsil Univ SSU Sch AI Convergence Seoul 06978 South Korea

Recent real-time systems have been gradually connected externally through the Internet. As each subsystem constituting a system has been developed from different vendors, the number of cases of security vulnerabilities in communication between subsystems has increased. In particular, real-time systems are vulnerable to timing inference attacks, such as cache-based side-channel attacks. Among the various methods for handling security problems of these systems, the flush task (FT) method is a simple and effective method to reduce the probability of information leakage by conditionally initializing the state of resources shared by tasks before a given scheduling. However, existing FT methods are for global scheduling only, and techniques for partitioned scheduling have not yet been studied. In this paper, we propose an FT-aware bin-packing (FT-bin-packing) algorithm that effectively allocates real-time tasks to identical multiprocessors to reduce the number of FTs invoked during the schedule of tasks in each processor. Based on the experimental results, we analyze the properties and performance of the FT-bin-packing algorithm. These results indicate that it can improve the performance of existing heuristic algorithms by up to 96.8% depending on the experimental setting.

关键词： Task analysis Real-time systems Security Processor scheduling partitioning algorithms Timing Side-channel attacks Real-time system flush task method partitioned scheduling

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Reducing the Impact of Interjob Interference in Dragonfly Networks Using Virtual Partitions

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IEEE MICRO 2022年第3期42卷 50-56页

作者： Maglione-Mathey, German Escudero-Sahuquillo, Jesus Javier Garcia, Pedro Quiles, Francisco J. Univ Castilla La Mancha Albacete 02071 Spain

In high-performance computing systems, the interference among applications that share network resources is one of the major causes of performance degradation and increase variability among different executions of the same application. In this article, we propose a virtual partitioning scheme for dragonfly networks that combines a job-allocation policy with a virtual channel (VC) assignment. Our experiments confirm that our proposal offers a better performance than the established schemes, also reducing variability.

关键词： Interference Routing partitioning algorithms Resource Management Proposals Network Topology High Performance Computing Interconnections Interconnection Architectures High Speed Network Communications Network Topology

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