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Checking Phylogenetics Decisiveness in Theory and in Practice

IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2022年第6期19卷 3307-3316页

作者： Parvini, Ghazaleh Braught, Katherine Fernandez-Baca, David Univ Massachusetts Coll Informat & Comp Sci Amherst MA 01003 USA Univ Illinois Dept Comp Sci Urbana IL 61801 USA Iowa State Univ Dept Comp Sci Ames IA 50011 USA

Suppose we aim to build a phylogeny for a set of taxa X using information from a collection of loci, where each locus offers information for only a fraction of the taxa. The question is whether, based solely on the pattern of data availability, called a taxon coverage pattern, one can determine if the data suffices to construct a reliable phylogeny. The problem can be expressed combinatorially as follows. Let us call a taxon coverage pattern decisive if, for any binary phylogenetic tree T for X, the collection of phylogenetic trees obtained by restricting T to the subset of X covered by each locus uniquely determines T. Here we relate the problem of checking whether a taxon coverage pattern is decisive to a hypergraph coloring problem. Using this connection, we (1) show that checking decisiveness is co-NP complete;(2) obtain lower bounds on the amount of coverage needed to achieve decisiveness;(3) devise an exact algorithm for decisiveness;(4) develop problem reduction rules, and use them to obtain efficient algorithms for inputs with few loci;and (5) devise Boolean satisfiability (SAT) and integer linear programming formulations (ILP) of decisiveness that allow us to analyze data sets that arise in practice. For data sets that are not decisive, we use our SATand ILP formulations to obtain decisive subsets of the data.

关键词： Phylogeny partitioning algorithms Color Terminology Steel Software Reliability Phylogenetic tree taxon coverage algorithms no-rainbow hypergraph coloring integer linear programming satisfiability

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PARALLEL algorithms FOR PLANAR GRAPH ISOMORPHISM AND RELATED PROBLEMS

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IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS 1988年第3期35卷 304-311页

作者： JAJA, J KOSARAJU, SR JOHNS HOPKINS UNIV DEPT ELECT ENGN & COMP SCIBALTIMOREMD 21218

Parallel algorithms for planar graph isomorphism and several related problems are presented. Two models of parallel computation are considered: the CREW-PRAM model and the two-dimensional array of processors. The results include O( square root n)-time mesh algorithms for finding a good separating cycle and the triconnected components of a planar graph, and for solving the single-function coarsest partitioning problem.< >

关键词： Parallel algorithms Computational modeling partitioning algorithms Tree graphs Concurrent computing Testing Automata Context modeling Graph theory Phase change random access memory

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Fuzzy Community Detection Based on Elite Symbiotic Organisms Search and Node Neighborhood Information

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2022年第7期30卷 2500-2514页

作者： Xiao, Jing Wang, Yan-Jiao Xu, Xiao-Ke Dalian Minzu Univ Coll Informat & Commun Engn Dalian 116600 Peoples R China Northeast Elect Power Univ Sch Elect Engn Jilin 132012 Jilin Peoples R China

In the last decade, fuzzy community detection has received increasing attention, since it cannot only uncover the community structure of a network, but also reflect the membership degrees of each node to multiple communities. Although some pioneers proposed a few algorithms for finding fuzzy communities, there is still room for further improvement in the quality of detected fuzzy communities. In this study, a metaheuristic-based modularity optimization algorithm, named symbiotic organisms search fuzzy community detection (SOSFCD) is proposed. On the one hand, an improved bioinspired metaheuristic algorithm, elite symbiotic organisms search, is designed as optimization strategy to improve the global convergence of fuzzy modularity optimization. On the other hand, a neighbor-based membership modification operation is proposed to intensify exploitation ability and speed up convergence, by efficiently utilizing local information (i.e., node neighborhood) of network topology. Experimental results on both of synthetic and real-life networks with different scales and characteristics show that SOSFCD can find max-modularity fuzzy partitions and coverings, and outperforms many state-of-the-art algorithms in terms of accuracy and stability.

关键词： partitioning algorithms Optimization Symbiosis Organisms Q measurement Convergence Tools Fuzzy community detection fuzzy covering fuzzy partition modularity optimization symbiotic organisms search (SOS)

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Online Anomaly Detection With Bandwidth Optimized Hierarchical Kernel Density Estimators

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第9期32卷 4253-4266页

作者： Kerpicci, Mine Ozkan, Huseyin Kozat, Suleyman Serdar Georgia Inst Technol Sch Elect & Comp Engn Atlanta GA 30332 USA Sabanci Univ Fac Engn & Nat Sci TR-34956 Istanbul Turkey Bilkent Univ Dept Elect & Elect Engn TR-06800 Ankara Turkey

We propose a novel unsupervised anomaly detection algorithm that can work for sequential data from any complex distribution in a truly online framework with mathematically proven strong performance guarantees. First, a partitioning tree is constructed to generate a doubly exponentially large hierarchical class of observation space partitions, and every partition region trains an online kernel density estimator (KDE) with its own unique dynamical bandwidth. At each time, the proposed algorithm optimally combines the class estimators to sequentially produce the final density estimation. We mathematically prove that the proposed algorithm learns the optimal partition with kernel bandwidths that are optimized in both region-specific and time-varying manner. The estimated density is then compared with a data-adaptive threshold to detect anomalies. Overall, the computational complexity is only linear in both the tree depth and data length. In our experiments, we observe significant improvements in anomaly detection accuracy compared with the state-of-the-art techniques.

关键词： Bandwidth Anomaly detection Kernel Estimation partitioning algorithms Data models Real-time systems Anomaly detection bandwidth selection kernel density estimation online regret analysis

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Compositional Verification Using Geodesic Distance via Assume-Guarantee Reasoning

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IEEE ACCESS 2024年 12卷 92612-92621页

作者： Liu, Xiaoyan Henan Polytech Univ Sch Software Jiaozuo 454000 Peoples R China

Assume-guarantee reasoning is indeed an effective compositional verification technique that can help mitigate the state explosion problem in model checking. However, the biggest challenge of applying assume-guarantee reasoning is how to best decompose a system. This paper presents a novel compositional verification frame based on geodesic distance. The proposed algorithm introduces novel techniques to decompose system components into groups. The algorithm's effectiveness and efficiency are evaluated through a comparative analysis with four state-of-the-art methods commonly used in the field. The results of the comparison consistently demonstrate that the proposed algorithm outperforms the state-of-the-art methods. This implies that the new algorithm exhibits superior performance in terms of decomposition quality and verification efficiency.

关键词： partitioning algorithms Model checking Clustering algorithms Analytical models Cognition Logic WebRTC compositional verification assume-guarantee reasoning group technology combinatorial testing geodesic distance partition

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PERFORMANCE ANALYSIS OF A CONVERGED SINGLE-LAYER PERCEPTRON FOR NONSEPARABLE DATA MODELS WITH BIAS TERMS

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IEEE TRANSACTIONS ON SIGNAL PROCESSING 1994年第1期42卷 175-188页

作者： BERSHAD, NJ SHYNK, JJ UNIV CALIF SANTA BARBARA DEPT ELECT & COMP ENGNCTR INFORMAT PROC RESSANTA BARBARACA 93106

Rosenblatt's algorithm is a recursive method used to adjust the weights of a single-layer perceptron. It is capable of partitioning the input. signal space into two regions that are separated by a hyperplane boundary. Thus, when the values of the input signal are linearly separable, the algorithm will converge to a stable stationary point that yields zero mean-square error. In this paper, we examine the stationary points of Rosenblatt's algorithm when the data is not linearly separable. A system identification model is used to generate the data. The model incorporates the effects of bias terms so that the hyperplane boundaries do not necessarily pass through the origin of the signal space. An expression, is also derived for the probability of an incorrect classification of the output signal when the weights are converged at a stationary point.

关键词： Performance analysis Data models Convergence partitioning algorithms System identification Vectors Signal processing algorithms Multilayer perceptrons Neural networks Signal generators

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Efficient Utilization of Multi-Threading Parallelism on Heterogeneous Systems for Sparse Tensor Contraction

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2024年第6期35卷 889-900页

作者： Xiao, Guoqing Yin, Chuanghui Chen, Yuedan Duan, Mingxing Li, Kenli Hunan Univ Coll Comp Sci & Elect Engn Changsha 410082 Peoples R China Hunan Univ Shenzhen Inst Shenzhen 518063 Peoples R China Cent South Univ Big Data Inst Changsha 410083 Peoples R China

Many fields of scientific simulation, such as chemistry and condensed matter physics, are increasingly eschewing dense tensor contraction in favor of sparse tensor contraction. In this work, we center around binary sparse tensor contraction (SpTC) which has the challenges of index matching and accumulation. To address these difficulties, we present GSpTC, an efficient element-wise SpTC framework on CPU-GPU heterogeneous systems. GSpTC first introduces a fine-grained partitioning strategy based on element-wise tensor contraction. By analyzing and selecting appropriate dimension partitioning strategies, we can efficiently utilize the multi-threading parallelism on GPUs and optimize the overall performance of GSpTC. In particular, GSpTC leverages multi-threading parallelism on GPUs for the contraction phase and merging phase, which greatly accelerates the computation phase in sparse tensor contraction computations. Furthermore, GSpTC employs parallel pipeline technology to hide the data transmission time between the host and the device, further enhancing its performance. As a result, GSpTC achieves an average performance improvement of 267% compared to the previous state-of-the-art framework Sparta.

关键词： Tensors Parallel processing Performance evaluation Graphics processing units Sparse matrices partitioning algorithms Sorting Heterogeneous parallel computing multi-threading parallelism optimization parallel pipeline sparse tensor contraction

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Typical Characteristic-Based Type-2 Fuzzy C-Means Algorithm

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IEEE TRANSACTIONS ON FUZZY SYSTEMS 2021年第5期29卷 1173-1187页

作者： Yang, Xiyang Yu, Fusheng Pedrycz, Witold Quanzhou Normal Univ Key Lab Intelligent Comp & Informat Proc Fujian P Quanzhou 362000 Peoples R China Beijing Normal Univ Sch Math Sci Beijing 100875 Peoples R China Univ Alberta Dept Elect & Comp Engn Edmonton AB T6R 2V4 Canada King Abdulaziz Univ Dept Elect & Comp Engn Fac Engn Jeddah 21589 Saudi Arabia Polish Acad Sci Syst Res Inst PL-01447 Warsaw Poland

Type-2 fuzzy sets provide an efficient vehicle for handling uncertainties of real-world problems, including noisy observations. Bringing type-2 fuzzy sets to clustering algorithms offers more flexibility to handle uncertainties associated with membership concepts caused by a noisy environment. However, the existing type-2 fuzzy clustering algorithms suffer from a time-consuming type-reduction process, which not only hampers the clustering performance but also increases the burden of understanding the clustering results. In order to alleviate the problem, this article introduces a set of typical characteristics of type-2 fuzzy sets and establishes a characteristic-based type-2 fuzzy clustering algorithm. Being different from the objective function used in the fuzzy C-means (FCM) algorithm that produces cluster centers and type-1 memberships, the objective function in the proposed algorithm contains additional characteristics of type-2 membership grades, namely, centers of gravity and cardinalities of the secondary fuzzy sets. The derived iterative formulas used for these parameters are much more efficient than the interval type-2 FCM algorithm. The experiments carried out in this study show that the proposed typical characteristic-based type-2 FCM algorithm has an ability of detecting noise as well as assigning suitable membership degrees to the individual data.

关键词： Clustering algorithms Fuzzy sets Noise measurement Phase change materials Uncertainty Linear programming partitioning algorithms Cardinality center of gravity (COG) characteristics fuzzy clustering type-2 fuzzy sets (T2FS)

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DYNAMIC MEASUREMENT OF COMPUTER GENERATED IMAGE SEGMENTATIONS

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 1985年第2期7卷 155-164页

作者： LEVINE, MD NAZIF, AM UNIV CAIRO DEPT ELECT ENGNCAIROEGYPT

This paper introduces a general purpose performance measurement scheme for image segmentation algorithms. Performance parameters that function in real-time distinguish this method from previous approaches that depended on an a priori knowledge of the correct segmentation. A low level, context independent definition of segmentation is used to obtain a set of optimization criteria for evaluating performance. Uniformity within each region and contrast between adjacent regions serve as parameters for region analysis. Contrast across lines and connectivity between them represent measures for line analysis. Texture is depicted by the introduction of focus of attention areas as groups of regions and lines. The performance parameters are then measured separately for each area. The usefulness of this approach lies in the ability to adjust the strategy of a system according to the varying characteristics of different areas. This feedback path provides the means for more efficient and error-free processing. Results from areas with dissimilar properties show a diversity in the measurements that is utilized for dynamic strategy setting.","doi":"10.1109/TPAMI.1985.4767640","publicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","startPage":"155","endPage":"164","rightsLink":"http://***/AppDispatchServlet?publisherName=ieee&publication=0162-8828&title=Dynamic+Measurement+of+Computer+Generated+Image+Segmentations&isbn=&publicationDate=March+1985&author=Martin+D.+Levine&ContentID=10.1109/TPAMI.1985.4767640&orderBeanReset=true&startPage=155&endPage=164&volumeNum=PAMI-7&issueNum=2","displayPublicationTitle":"IEEE Transactions on Pattern Analysis and Machine Intelligence","pdfPath":"/iel5/34/4767634/***","keywords":[{"type":"IEEE Keywords","kwd":["Image generation","Image segmentation","Humans","Area measurement","Feedback","Layout","partitioning algorithms","Focusing","Expert systems","Image analysis"]},{"type":"Author Keywords ","kw

关键词： Image generation Image segmentation Humans Area measurement Feedback Layout partitioning algorithms Focusing Expert systems Image analysis Author Keywords region analysis Edge detection expert system image contrast image segmentation image uniformity partial segmentation

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Research on an Ensemble Classification Algorithm Based on Differential Privacy

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IEEE ACCESS 2020年 8卷 93499-93513页

作者： Jia, Junjie Qiu, Wanyong Northwest Normal Univ Coll Comp Sci & Engn Lanzhou 730070 Peoples R China

In the field of information security, privacy protection based on machine learning is currently a hot topic. Combining differential privacy protection with AdaBoost, a machine learning ensemble classification algorithm, this paper proposes a scheme under differential privacy named CART-DPsAdaBoost (CART-Differential privacy structure of AdaBoost). In the process of boosting, the algorithm combines the idea of bagging, and uses a classification and regression tree (CART) stump as the base learner for ensemble learning. Applying feature perturbation, based on a random subspace algorithm, the exponential mechanism is used to select the splitting point for continuous attributes. We use the Gini index to find the optimal binary partitioning point for discrete attributes and add noise according to the Laplace mechanism. Throughout the process, a privacy budget is allocated in order to meet the appropriate differential privacy protection needs for the current application. Unlike similar algorithms, this method does not require discretization during preprocessing of the data. Experimental results with the Census Income, Digit Recognizer, and Adult Data Set show that while protecting private information, the scheme has little impact on classification accuracy and can effectively address large-scale and high-dimensional data classification problems.

关键词： Classification algorithms Privacy Decision trees Machine learning partitioning algorithms Privacy protection differential privacy machine learning AdaBoost

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