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Reversal and Indel Distance With Intergenic Region Information

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023年第3期20卷 1628-1640页

作者： Alexandrino, Alexsandro Oliveira Brito, Klairton Lima Oliveira, Andre Rodrigues Dias, Ulisses Dias, Zanoni Univ Estadual Campinas Inst Comp Campinas Brazil Univ Estadual Campinas Sch Technol Limeira Brazil

Recent works on genome rearrangements have shown that incorporating intergenic region information along with gene order in models provides better estimations for the rearrangement distance than using gene order alone. The reversal distance is one of the main problems in genome rearrangements. It has a polynomial time algorithm when only gene order is used to model genomes, assuming that repeated genes do not exist and that gene orientation is known, even when the genomes have distinct gene sets. The reversal distance is NP-hard and has a 2-approximation algorithm when incorporating intergenic regions. However, the problem has only been studied assuming genomes with the same set of genes. In this work, we consider the variation that incorporates intergenic regions and that allows genomes to have distinct sets of genes, a scenario that leads us to include indels operations (insertions and deletions). We present a 2.5-approximation algorithm using the labeled intergenic breakpoint graph, which is based on the well-known breakpoint graph structure. We also present an experimental analysis of the proposed algorithm using simulated data, which showed that the practical approximation factor is considerably less than 2.5. Furthermore, we used the algorithm in real genomes to construct a phylogenetic tree.

关键词： Genomics Bioinformatics approximation algorithms Sorting Computational modeling Transforms Estimation Genome rearrangements reversals indels intergenic regions

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Efficient Sparse Representation for Learning With High-Dimensional Data

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2023年第8期34卷 4208-4222页

作者： Chen, Jie Yang, Shengxiang Wang, Zhu Mao, Hua Sichuan Univ Coll Comp Sci Chengdu 610065 Peoples R China De Montfort Univ Sch Comp Sci & Informat Leicester LEI 9BH Leics England Sichuan Univ Law Sch Chengdu 610065 Peoples R China Northumbria Univ Dept Comp & Informat Sci Newcastle Upon Tyne NE1 8ST Tyne & Wear England

Due to the capability of effectively learning intrinsic structures from high-dimensional data, techniques based on sparse representation have begun to display an impressive impact on several fields, such as image processing, computer vision, and pattern recognition. Learning sparse representations isoften computationally expensive due to the iterative computations needed to solve convex optimization problems in which the number of iterations is unknown before convergence. Moreover, most sparse representation algorithms focus only on determining the final sparse representation results and ignore the changes in the sparsity ratio (SR) during iterative computations. In this article, two algorithms are proposed to learn sparse representations based on locality-constrained linear representation learning with probabilistic simplex constraints. Specifically, the first algorithm, called approximated local linear representation (ALLR), obtains a closed-form solution from individual locality-constrained sparse representations. The second algorithm, called ALLR with symmetric constraints (ALLR $_{ SC}$ ), further obtains a symmetric sparse representation result with a limited number of computations;notably, the sparsity and convergence of sparse representations can be guaranteed based on theoretical analysis. The steady decline in the SR during iterative computations is a critical factor in practical applications. Experimental results based on public datasets demonstrate that the proposed algorithms perform better than several state-of-the-art algorithms for learning with high-dimensional data.

关键词： Sparse matrices Optimization Iterative algorithms Dictionaries Convex functions approximation algorithms Convergence Linear representation low-dimensional structures probabilistic simplex sparse representation

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Graphical and Numerical Study of a Newly Developed Root-Finding Algorithm and Its Engineering Applications

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IEEE ACCESS 2023年 11卷 2375-2383页

作者： Naseem, Amir Rehman, M. A. Qureshi, Sania Ide, Nasr Al Din Univ Management & Technol Dept Math Lahore 54770 Pakistan Mehran Univ Engn & Technol Dept Basic Sci & Related Studies Jamshoro 76062 Pakistan Near East Univ Dept Math TR-99138 Mersin Turkiye Aleppo Univ Fac Sci Dept Math Aleppo Syria

The primary objective of this paper is to develop a new method for root-finding by combining forward and finite-difference techniques in order to provide an efficient, derivative-free algorithm with a lower processing cost per iteration. This will be accomplished by combining forward and finite-difference techniques. We also detail the convergence criterion that was devised for the root-finding approach, and we show that the method that was recommended is quintic-order convergent. We addressed a few engineering issues in order to illustrate the validity and application of the developed root-finding algorithm. The quantitative results justified the constructed root-finding algorithm's robust performance in comparison to other quintic-order methods that can be found in the literature. For the graphical analysis, we make use of the newly discovered method to plot some novel polynomiographs that are attractive to the eye, and then we evaluate these new plots in relation to previously established quintic-order root-finding strategies. The graphic analysis demonstrates that the newly created method for root-finding has better convergence with the larger area than the other comparable methods do.

关键词： Mathematical models Iterative methods Convergence approximation algorithms Costs Taylor series Software algorithms Computational algorithms convergence-order non-linearity Halley's scheme dynamical aspects

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Southbound Message Delivery With Virtual Network Topology Awareness in Clouds

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IEEE-ACM TRANSACTIONS ON NETWORKING 2023年第1期31卷 248-263页

作者： Zhao, Gongming Luo, Luyao Xu, Hongli Chung, Chun-Jen Xie, Liguang Univ Sci & Technol Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China Univ Sci & Technol China Suzhou Inst Adv Res Suzhou 215123 Jiangsu Peoples R China Futurewei Technol Inc Bellevue WA 98004 USA

Southbound message delivery from the control plane to the data plane is one of the essential issues in multi-tenant clouds. A natural method of southbound message delivery is that the control plane directly communicates with compute nodes in the data plane. However, due to the large number of compute nodes, this method may result in massive control overhead. The Message Queue (MQ) model can solve this challenge by aggregating and distributing messages to queues. Existing MQ-based solutions often perform message aggregation based on the physical network topology, which do not align with the fundamental requirements of southbound message delivery, leading to high message redundancy on compute nodes. To address this issue, we design and implement VITA, the first-of-its-kind work on virtual network topology-aware southbound message delivery. However, it is intractable to optimally deliver southbound messages according to the virtual attributes of messages. Thus, we design two algorithms, submodular-based approximation algorithm and simulated annealing-based algorithm, to solve different scenarios of the problem. Both experiment and simulation results show that VITA can reduce the total traffic amount of redundant messages by 45%-75% and reduce the control overhead by 33%-80% compared with state-of-the-art solutions.

关键词： Cloud computing Servers Computational modeling Redundancy Network topology approximation algorithms Protocols Southbound message delivery message queue virtual network topology virtual private cloud

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Level approximation Algorithm for RMVSC Based on Double Weighting Factors 3

Level Approximation Algorithm for RMVSC Based on Double Weig...

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3rd New Energy and Energy Storage System Control Summit Forum, NEESSC 2023

作者： Han, Xiangyue Luo, Zhenpeng Yang, Baofeng College of Electric Power Inner Mongolia University of Technology Hohhot China

ISBN: (纸本)9798350309348

Aiming at the problem of excessive ripple voltage of traditional low frequency cyclic triggering of RMVSC (Reinjection Multi-level VSC), combining with the voltage characteristics of the converter, based on the idea of MMC (Multi-level Modular Converter) bubble sorting voltage equalization, a new type of RMVSC level approximation algorithm with double weighting factors is constructed, and the model of RMVSC is constructed on the PSCAD/EMTDC simulation platform. The simulation results show that the introduction of double weighting factors can optimize the cascaded H-bridge ripple voltage, effectively alleviate the cascaded H-bridge capacitor voltage imbalance, and follow the principle of unnecessary unordered switching action, which significantly reduces the system switching loss. And the dynamic performance is good under STATCOM conditions, and the ideal waveform can be output without filter to realize the soft switching technology. ©2023 IEEE.

关键词： approximation algorithms

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Reconfigurable Intelligent Surface-Assisted Secondary Communication System Coexisting With Multiple Primary Networks

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IEEE TRANSACTIONS ON COGNITIVE COMMUNICATIONS AND NETWORKING 2023年第1期9卷 170-184页

作者： Tian, Zhong Chen, Zhengchuan Wang, Min Jia, Yunjian Wen, Wanli Jin, Shi Chongqing Univ Sch Microelect & Commun Engn Chongqing 400044 Peoples R China Xidian Univ State Key Lab Integrated Serv Networks Xian 710071 Peoples R China Chongqing Univ Posts & Telecommun Sch Optoelect Engn Chongqing 400065 Peoples R China Southeast Univ Natl Mobile Commun Res Lab Nanjing 211189 Peoples R China

In a secondary communication system (SCS) coexisting with multiple primary networks, the coupling interference between the SCS and primary networks would deteriorate the spectral efficiency (SE) of the SCS and impede the spectrum access of massive secondary devices. In this paper, a well-designed reconfigurable intelligent surface (RIS) is employed to control wireless channels, which can achieve a high SE of the SCS and relieve the scarcity of the spectrum resource. Specifically, the SE enhancement problem is decomposed into two subproblems which are a second-order cone programming and a fractional programming of the convex quadratic form (CQFP), respectively, to optimize alternatively the beamforming vector at the secondary access point and the reflecting coefficients at the RIS. The CQFP subproblem is solved by the domain and envelope shrinking (DES) algorithm, providing the best SE performance. Besides, a low-complexity method of gradient-based linearization with domain (GLD) is proposed for obtaining a sub-optimal reflecting coefficients for fast deployment. Moreover, a nearest point searching with penalty method is also developed to realize the discretization of the phase shifts at the RIS in practice. The simulation results validate the effectiveness of the DES algorithm and show that the proposed GLD algorithm improves the SE performance significantly.

关键词： Array signal processing Transceivers Programming Optimization approximation algorithms Antennas Symbols Reconfigurable intelligent surface spectral efficiency reflecting coefficients optimization

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Efficient and Optimal algorithms for Tree Summarization With Weighted Terminologies

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2023年第3期35卷 2500-2514页

作者： Zhu, Xuliang Huang, Xin Choi, Byron Xu, Jianliang Cheung, William K. Zhang, Yanchun Liu, Jiming Hong Kong Baptist Univ Dept Comp Sci Hong Kong Peoples R China Guangzhou Univ Guangzhou 510006 Peoples R China Victoria Univ Footscray Vic 3011 Australia

Data summarization that presents a small subset of a dataset to users has been widely applied in numerous applications and systems. Many datasets are coded with hierarchical terminologies, e.g., gene ontology, disease ontology, to name a few. In this paper, we study the weighted tree summarization. We motivate and formulate our kWTS-problem as selecting a diverse set of k nodes to summarize a hierarchical tree T with weighted terminologies. We first propose an efficient greedy tree summarization algorithm GTS. It solves the problem with (1 -1/e)-approximation guarantee. Although GTS achieves quality-guaranteed answers approximately, but it is still not optimal. To tackle the problem optimally, we further develop a dynamic programming algorithm OTS to obtain optimal answers for kWTS-problem in O(nhk(3)) time, where n, h are the node size and height in tree T. The algorithm complexity and correctness of OTS are theoretically analyzed. In addition, we propose a useful optimization technique of tree reduction to remove useless nodes with zero weights and shrink the tree into a smaller one, which ensures the efficiency acceleration of both GTS and OTS in real-world datasets. Moreover, we illustrate one useful application of graph visualization based on the answer of k-sized tree summarization and show it in a novel case study. Extensive experimental results on real-world datasets show the effectiveness and efficiency of our proposed approximate and optimal algorithms for tree summarization. Furthermore, we conduct a usability evaluation of attractive topic recommendation on ACM Computing Classification System dataset to validate the usefulness of our model and algorithms.

关键词： Diseases Heuristic algorithms Terminology approximation algorithms Ontologies Dynamic programming Optimization Hierarchy tree data summarization optimal algorithm top-k

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Local Fitness Landscape Exploration Based Genetic algorithms

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IEEE ACCESS 2023年 11卷 3324-3337页

作者： Dubey, Rahul Hickinbotham, Simon Price, Mark Tyrrell, Andy Univ York Dept Elect Engn York YO10 5DD England Queens Univ Belfast Sch Mech & Aerosp Engn Belfast BT9 5AH North Ireland

Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, "Fitness Landscape Exploration based Genetic Algorithm " (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.

关键词： Genetic algorithms Search problems approximation algorithms Optimization Flexible printed circuits Genomics Bioinformatics fitness landscape approximation multi-objective optimization evolutionary search

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Joint AMP-SBL algorithms for Device Activity Detection and Channel Estimation in Massive MIMO mMTC Systems

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IEEE TRANSACTIONS ON COMMUNICATIONS 2023年第4期71卷 2136-2152页

作者： Rajoriya, Anupama Budhiraja, Rohit IIT Kanpur Dept Elect Engn Kanpur 208016 India

We consider the problem of detecting active devices and estimating their channels in the uplink of a massive machine type communication (mMTC) network. The base station (BS) in this mMTC system is equipped with massive multiple input multiple output (mMIMO) technology, and the channels across its antennas are correlated, an aspect ignored by most of the existing mMTC works. We propose three Bayesian learning algorithms which exploit channel spatial correlation, and comprehensively outperform several existing state-of-the-art algorithms, which do not exploit it. The proposed algorithms perform better in terms of the normalized mean squared error, activity error rate, and spectral efficiency (SE). The first correlated vector approximate message passing (AMP) algorithm has the best performance, but fails when the BS does not have knowledge of channel large scale fading and device activity probability. The second block sparse Bayesian learning (B-SBL) algorithm overcomes these limitations, but has a high complexity. The third combined AMP-BSBL algorithm retains the advantages of B-SBL, but with a much reduced complexity. We show that all three channel estimators can be represented as plug-in linear minimum mean squared estimators. This crucially helps us in deriving a common lower bound on the SE of mMIMO mMTC systems.

关键词： Fading channels Channel estimation Correlation Antennas Bayes methods Performance evaluation approximation algorithms Approximate message passing (AMP) device activity detection sparse Bayesian learning (SBL)

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PIANO: Influence Maximization Meets Deep Reinforcement Learning

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IEEE TRANSACTIONS ON COMPUTATIONAL SOCIAL SYSTEMS 2023年第3期10卷 1288-1300页

作者： Li, Hui Xu, Mengting Bhowmick, Sourav S. Rayhan, Joty Shafiq Sun, Changsheng Cui, Jiangtao Xidian Univ Sch Comp Sci & Technol Xian 710071 Peoples R China Nanyang Technol Univ Sch Comp Sci & Engn Singapore 639798 Singapore Natl Univ Singapore Sch Comp Singapore 119077 Singapore

Since its introduction in 2003, the influence maximization (IM) problem has drawn significant research attention in the literature. The aim of IM, which is NP-hard, is to select a set of k users known as seed users who can influence the most individuals in the social network. The state-of-the-art algorithms estimate the expected influence of nodes based on sampled diffusion paths. As the number of required samples has been recently proven to be lower bounded by a particular threshold that presets tradeoff between the accuracy and the efficiency, the result quality of these traditional solutions is hard to be further improved without sacrificing efficiency. In this article, we present an orthogonal and novel paradigm to address the IM problem by leveraging deep reinforcement learning (RL) to estimate the expected influence. In particular, we present a novel framework called deeP reInforcement leArning-based iNfluence maximizatiOn (PIANO) that incorporates network embedding and RL techniques to address this problem. In order to make it practical, we further present PIANO-E and PIANO@⟨angle d⟩, both of which can be applied directly to answer IM without training the model from scratch. Experimental study on real-world networks demonstrates that PIANO achieves the best performance with respect to efficiency and influence spread quality compared to state-of-the-art classical solutions. We also demonstrate that the learned parametric models generalize well across different networks. Besides, we provide a pool of pretrained PIANO models such that any IM task can be addressed by directly applying a model from the pool without training over the targeted network.

关键词： Training approximation algorithms Social networking (online) Peer-to-peer computing Task analysis Heuristic algorithms Computational modeling Deep reinforcement learning (RL) graph embedding influence maximization (IM) social network

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