Permutation Algorithm Research Articles

FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets

BackgroundEstimation of functional connectivity in gene sets derived from genome-wide or other biological experiments is one of the essential tasks of bioinformatics. A promising approach for solving this problem is to compare gene networks built using experimental gene sets with random networks. One of the resources that make such an analysis possible is CrossTalkZ, which uses the FunCoup database. However, existing methods, including CrossTalkZ, do not take into account individual types of interactions, such as protein/protein interactions, expression regulation, transport regulation, catalytic reactions, etc., but rather work with generalized types characterizing the existence of any connection between network members.ResultsWe developed the online tool FunGeneNet, which utilizes the ANDSystem and STRING to reconstruct gene networks using experimental gene sets and to estimate their difference from random networks. To compare the reconstructed networks with random ones, the node permutation algorithm implemented in CrossTalkZ was taken as a basis. To study the FunGeneNet applicability, the functional connectivity analysis of networks constructed for gene sets involved in the Gene Ontology biological processes was conducted. We showed that the method sensitivity exceeds 0.8 at a specificity of 0.95. We found that the significance level of the difference between gene networks of biological processes and random networks is determined by the type of connections considered between objects. At the same time, the highest reliability is achieved for the generalized form of connections that takes into account all the individual types of connections. By taking examples of the thyroid cancer networks and the apoptosis network, it is demonstrated that key participants in these processes are involved in the interactions of those types by which these networks differ from random ones.ConclusionsFunGeneNet is a web tool aimed at proving the functionality of networks in a wide range of sizes of experimental gene sets, both for different global networks and for different types of interactions. Using examples of thyroid cancer and apoptosis networks, we have shown that the links over-represented in the analyzed network in comparison with the random ones make possible a biological interpretation of the original gene/protein sets. The FunGeneNet web tool for assessment of the functional enrichment of networks is available at http://www-bionet.sscc.ru/fungenenet/.

Application of a Deep Neural Network to Metabolomics Studies and Its Performance in Determining Important Variables.

Deep neural networks (DNNs), which are kinds of the machine learning approaches, are powerful tools for analyzing big sets of data derived from biological and environmental systems. However, DNNs are not applicable to metabolomics studies because they have difficulty in identifying contribution factors, e.g., biomarkers, in constructed classification and regression models. In this paper, we describe an improved DNN-based analytical approach that incorporates an importance estimation for each variable using a mean decrease accuracy (MDA) calculation, which is based on a permutation algorithm; this approach is called DNN-MDA. The performance of the DNN-MDA approach was evaluated using a data set of metabolic profiles derived from yellowfin goby that lived in various rivers throughout Japan. Its performance was compared with that of conventional multivariate and machine learning methods, and the DNN-MDA approach was found to have the best classification accuracy (97.8%) among the examined methods. In addition to this, the DNN-MDA approach facilitated the identification of important variables such as trimethylamine N-oxide, inosinic acid, and glycine, which were characteristic metabolites that contributed to the discrimination of the geographical differences between fish caught in the Kanto region and those caught in other regions. As a result, the DNN-MDA approach is a useful and powerful tool for determining the geographical origins of specimens and identifying their biomarkers in metabolomics studies that are conducted in biological and environmental systems.

Learning directed acyclic graph models based on sparsest permutations

We consider the problem of learning a Bayesian network or directed acyclic graph model from observational data. A number of constraint‐based, score‐based and hybrid algorithms have been developed for this purpose. Statistical consistency guarantees of these algorithms rely on the faithfulness assumption, which has been shown to be restrictive especially for graphs with cycles in the skeleton. We here propose the sparsest permutation (SP) algorithm, showing that learning Bayesian networks is possible under strictly weaker assumptions than faithfulness. This comes at a computational price, thereby indicating a statistical‐computational trade‐off for causal inference algorithms. In the Gaussian noiseless setting, we prove that the SP algorithm boils down to finding the permutation of the variables with the sparsest Cholesky decomposition of the inverse covariance matrix, which is equivalent toℓ0‐penalized maximum likelihood estimation. We end with a simulation study showing that in line with the proven stronger consistency guarantees, and the SP algorithm compares favourably to standard causal inference algorithms in terms of accuracy for a given sample size. Copyright © 2018 John Wiley & Sons, Ltd.

Improved Computing Performance for Listing Combinatorial Algorithms Using Multi-Processing MPI and Thread Library

This study builds up two parallel algorithms to improve computing performance for two listing binary and listing permutation algorithms. The problems are extremely interesting and practically applicable in many fields in our daily life. To parallel execution, we divide the data set input and allocate them to the processors. The article focuses on (i) the analysis of the research situation of the related works to compare and evaluate the existing problems of previous works, (ii) the analysis of the input data structure to divide data for the sub processors, (iii) the construction of parallel algorithms - proof of correctness and analysis of computing complexity, and (iv) experiments in multi-processing MPI and Thread library. Then the comparison of the results of the parallel algorithm with the sequential algorithm and the comparison of the execution time on different sub processors is discussed.

Optimization and experimental realization of the quantum permutation algorithm

The quantum permutation algorithm provides computational speed-up over classical algorithms for determining the parity of a given cyclic permutation. For its $n$-qubit implementations, the number of required quantum gates scales quadratically with $n$ due to the quantum Fourier transforms included. We show here for the $n$-qubit case that the algorithm can be simplified so that it requires only $O(n)$ quantum gates, which theoretically reduces the complexity of the implementation. To test our results experimentally, we utilize IBM's 5-qubit quantum processor to realize the algorithm by using the original and simplified recipes for the 2-qubit case. It turns out that the latter results in a significantly higher success probability which allows us to verify the algorithm more precisely than the previous experimental realizations. We also verify the algorithm for the first time for the 3-qubit case with a considerable success probability by taking the advantage of our simplified scheme.

Planning Electric-Rolling-Stock Maintenance in Conditions of Limited Resources

Planning electric-rolling-stock (ERS) maintenance in conditions of limited resources can be carried out based on the following criteria of efficiency of construction of the cycle diagram of the electric rolling stock: meeting the requirements of the railway-traffic safety provided by adjusting the planned movement time of the electric rolling stock for the purpose of not allowing an excessive lapse of time between the maintenance over that permissible and uniformity of maintenance. The solution of the set problem using the graph theory allows obtaining the whole set of the permissible values of maintenance and selecting a value that, on the one hand, corresponds to the planned train time schedule (PTTS) and, on the other hand, differs minimally from the optimal with respect to the selected criterion. This takes a significant amount of time. The problem can be quickly solved using a genetic algorithm. The introduction of a new criterion—total excess time lapse between maintenance works over the permissible interval—allows obtaining the solution with any initial data, which is not always achievable when using the uniform-maintenance criterion. The crossover and permutation algorithm implemented within the genetic algorithm is adapted taking into account considering the peculiarities of the agents engaged in solving the problem that has been set out. We have studied the possibility of using various types of crossover and permutation to construct the cycle diagrams and influence of the parameters of the genetic algorithm on the results. The obtained analytical results are tested for the conditions of the Moscow subway.

Sparsity Independent Sub-Nyquist Rate Wideband Spectrum Sensing on Real-Time TV White Space

Wideband spectrum sensing is a highly desirable feature in cognitive radio systems when the aim is to increase the probability of exploring spectral opportunities. Sub-Nyquist sampling has attracted significant interest for wideband spectrum sensing, while existing algorithms can only work with a sparse spectrum. In this paper, we propose a sub-Nyquist wideband spectrum sensing algorithm that achieves wideband sensing independent of signal sparsity without sampling at full bandwidth by using the low-speed analog-to-digital converters (ADCs) based on sparse fast Fourier transform. To lower signal spectrum sparsity while maintaining the channel state information, we preprocess the received signal through a proposed permutation and filtering algorithm. The proposed wideband spectrum sensing algorithm subsamples the time-domain signal and then directly estimates its frequency spectrum. We derive and verify the proposed algorithm by numerical analysis and test it on real-world TV white space signals. The results show that the proposed algorithm achieves high detection performance on sparse and nonsparse wideband signals with reduced runtime and implementation complexity in comparison with the conventional wideband spectrum sensing algorithms.

Computing similarity distances between rankings

We address the problem of computing distances between permutations that take into account similarities between elements of the ground set dictated by a graph. The problem may be summarized as follows: Given two permutations and a positive cost function on transpositions that depends on the similarity of the elements involved, find a smallest cost sequence of transpositions that converts one permutation into another. Our focus is on costs that may be described via special metric-tree structures. The presented results include a linear-time algorithm for finding a minimum cost decomposition for simple cycles and a linear-time 4∕3-approximation algorithm for permutations that contain multiple cycles. The proposed methods rely on investigating a newly introduced balancing property of cycles embedded in trees, cycle-merging methods, and shortest path optimization techniques.

A permutation algorithm based on dynamic time warping in speech frequency-domain blind source separation

Frequency-Domain Blind Source Separation (FD-BSS) is an efficient way to analyze convolutive mixed speech. To improve the quality of the separated speech, a permutation algorithm based on Dynamic Time Warping (DTW) is proposed in this paper. Because signals in adjacent frequency bins have high similarity, DTW technology is used to compare them and generate adjustment matrices to solve the permutation ambiguity. Our approach is evaluated through simulated and practical experiments. Using Signal to Distortion Ratio (SDR), Signal to Interference Ratio (SIR), Signal to Artifacts Ratio (SAR), and Perceptual Estimation of the Speech Quality (PESQ) for measurements. To examine the quality of the separated speech in a practical acoustic environment, we adopt the accuracy ratio of Automatic Speech Recognition (ASR). In the experiments, we compare our approach with other classical permutation criteria such as K-L divergence distance, envelope correlation and higher-order statistics. The experimental results show that the proposed algorithm performs permutation alignment more accurately and improves the acoustic quality of separation.

Colourwave: some variations on a mathematical schema

ABSTRACTUsing colour to track paths of traveling elements in permutation algorithms can not only help visualize what is happening from a mathematical perspective, but it also produces aesthetically pleasing configurations that can be the starting point for compelling artwork. In this paper, I walk the reader through the process that produced a series of art works and point out some of their mathematical properties.

Realization of quantum permutation algorithm in high dimensional Hilbert space**Project supported by the Fundamental Research Funds for the Central Universities and the National Natural Science Foundation of China (Grant Nos. 11374008, 11374238, 11374239, and 11534008).

Quantum algorithms provide a more efficient way to solve computational tasks than classical algorithms. We experimentally realize quantum permutation algorithm using light’s orbital angular momentum degree of freedom. By exploiting the spatial mode of photons, our scheme provides a more elegant way to understand the principle of quantum permutation algorithm and shows that the high dimension characteristic of light’s orbital angular momentum may be useful in quantum algorithms. Our scheme can be extended to higher dimension by introducing more spatial modes and it paves the way to trace the source of quantum speedup.

Performance Analysis on Different Permutation Algorithms for Security Applications

Performance Analysis on Different Permutation Algorithms for Security Applications

Effective Iterated Greedy Algorithm for Flow-Shop Scheduling Problems with Time lags

Flow shop scheduling problem with time lags is a practical scheduling problem and attracts many studies. Permutation problem(PFSP with time lags) is concentrated but non-permutation problem(non-PFSP with time lags) seems to be neglected. With the aim to minimize the makespan and satisfy time lag constraints, efficient algorithms corresponding to PFSP and non-PFSP problems are proposed, which consist of iterated greedy algorithm for permutation(IGTLP) and iterated greedy algorithm for non-permutation (IGTLNP). The proposed algorithms are verified using well-known simple and complex instances of permutation and non-permutation problems with various time lag ranges. The permutation results indicate that the proposed IGTLP can reach near optimal solution within nearly 11% computational time of traditional GA approach. The non-permutation results indicate that the proposed IG can reach nearly same solution within less than 1% computational time compared with traditional GA approach. The proposed research combines PFSP and non-PFSP together with minimal and maximal time lag consideration, which provides an interesting viewpoint for industrial implementation.

Speech Scrambling Based on Chaotic Mapping and Random Permutation for Modern Mobile Communication Systems

The expanding significance of securing data over the network has promoted growth of strong encryption algorithms. To enhance the information protection in network communications, this paper presents a Random permutation, chaotic mapping and pseudo random binary scrambling. It involves transforming the intelligible speech signal into an unintelligible form to protect it from interrupters. In this report, suggest a simple and secure procedure to secure the speech signal. The speech scrambling process makes use of two Permutations. In the first step, Random permutation algorithm is used to swap the rows of the original speech followed by swapping of rows using chaotic Bernoulli mapping. This produces an intermediary scrambled speech. In the second measure, pseudo random binary generator is used to make the final scrambled signal. Various analysis tests are then executed to determine the quality of the encrypted image. The test results determine the efficiency of the proposed speech scrambling process.

Optimal automated path planning for infinitesimal and real-sized particle assemblies

The present article introduces an algorithm for path planning and assembly of infinitesimal and real-sized particles by using a distance and path based permutation algorithm. The main objective is to define non-overlapping particle paths subject to minimal total path length during particles positioning and assembly. Thus, a local minimum is sought with a low computational cost. For this reason, an assignment problem, to be specific Euclidean bipartite matching problem, is presented, where the particles in the initial (random selection) and final (particle assembly) configurations are in one-to-one correspondence. The cost function for particle paths is defined through Euclidean distance of each particle between the initial and final configurations. Principally, a cost flow problem is formed and solved by determining an optimal permutation subject to the total Euclidean distance of the particles and their non-overlapping paths. Monte Carlo simulations are carried out for non-overlapping paths; thus, non-colliding particles, and then total path distances of the obtained sets are minimized, resulting in an optimal solution which may not be necessarily the global optimum. Case studies on basic and complex shaped infinitesimal and real-sized particle assemblies are shown with their total costs, i.e., path lengths. It is believed that the present study contributes to the current efforts in optical trapping automation for particle assemblies with possible applications, e.g., in the areas of micro-manufacturing, microfluidics, regenerative medicine and biotechnology.

Consolidation for compact constraints and Kendall tau LP decodable permutation codes

Invented in the 1960s, permutation codes have reemerged in recent years as a topic of great interest because of properties making them attractive for certain modern technological applications, especially flash memory. In 2011 a polynomial time algorithm called linear programming (LP) decoding was introduced for a class of permutation codes where the feasible set of codewords was a subset of the vertex set of a code polytope. In this paper we investigate a new class of linear constraints for matrix polytopes with no fractional vertices through a new concept called “consolidation.” We then introduce a necessary and sufficient condition for which LP decoding methods, originally designed for the Euclidean metric, may be extended to provide an efficient decoding algorithm for permutation codes with the Kendall tau metric.

Log-Lists and their applications to sorting by transpositions, reversals and block-interchanges

Link-cut trees have been introduced by D.D. Sleator and R.E. Tarjan [12] with the aim of efficiently maintaining a forest of vertex-disjoint dynamic rooted trees under cut and link operations. These operations respectively disconnect a subtree from a tree, and join two trees by an edge. Additionally, link-cut trees allow to change the root of a tree and to perform a number of updates and queries on cost values defined on the arcs of the trees. All these operations are performed in O(log⁡n) amortized or worst-case time, depending on the implementation, where n is the total size of the forest.In this paper, we show that a list of elements implemented using link-cut trees (we call it a log-list) allows us to obtain a common running time of O(log⁡n) for the classical operations on lists, but also for some other essential operations that usually take linear time on lists. Such operations require to find the minimum/maximum element in a sublist defined by its endpoints, the position of a given element in the list or the element placed at a given position in the list; or they require to add a value a, or to multiply by −1, all the elements in a sublist.Furthermore, we use log-lists to implement several existing algorithms for sorting permutations by transpositions and/or reversals and/or block-interchanges, and obtain O(nlog⁡n) running time for all of them. In this way, the running time of several algorithms is improved, whereas in other cases our algorithms perform as well as the best existing implementations.

Preserving the Number of Cycles of Length $k$ in a Growing Uniform Permutation

The goal of this work is to describe a uniform generation tree for permutations which preserves the number of $k$-cycles between any permutation (except for a small unavoidable subset of optimal size) of the tree and its direct children. Moreover, the tree we describe has the property that if the number of $k$-cycles does not change during any $k$ consecutive levels, then any further random descent will always yield permutations with that same number of $k$-cycles. This specific additional property yields interesting applications for exact sampling. We describe a new random generation algorithm for permutations with a fixed number of $k$-cycles in $n+\mathcal{O}(1)$ expected calls to a random integer sampler. Another application is a combinatorial algorithm for exact sampling from the Poisson distribution with parameter $1/k$.

Lightweight Security Algorithm For Wireless Node Connected with IoT

Objective: The enhancement of wireless technology and sensory device has brought to the introduction towards IoT (Internet of Things). Here security is taken as one of the major issues, as they operate in unlicensed bandwidth. Since most of the device is resource constrained the lightweight security is driven to these devices. Implementation of security is also tedious and selective as the conventional security algorithm degrades the network performance due to the high computational complexity and inherent delays that are incurred for execution of these algorithms. Method: In this work, a novel approach is made with new hybrid lightweight security algorithm named PRESENT-GRP which is derived in Intel Galileo Gen 2 board. This methodology helps to secure data during transmission and control process. Findings: This hybrid algorithm is an enhancement of the GRP permutation algorithm because it lags an S-box. This proposed algorithm is for low power devices which consumes relatively less memory. Thus, in Internet of Things (IoT) applications this algorithm may help to provide end to end privacy to share data securely. Improvement: In future works, this algorithm can be improved with still more security level by having a trade off between performance and memory usage. It can also be enhanced by implementing it in real time military related and health related applications for end to end security for smaller size datum.

Scalable metagenomics alignment research tool (SMART): a scalable, rapid, and complete search heuristic for the classification of metagenomic sequences from complex sequence populations.

BackgroundNext generation sequencing technology has enabled characterization of metagenomics through massively parallel genomic DNA sequencing. The complexity and diversity of environmental samples such as the human gut microflora, combined with the sustained exponential growth in sequencing capacity, has led to the challenge of identifying microbial organisms by DNA sequence. We sought to validate a Scalable Metagenomics Alignment Research Tool (SMART), a novel searching heuristic for shotgun metagenomics sequencing results.ResultsAfter retrieving all genomic DNA sequences from the NCBI GenBank, over 1 × 1011 base pairs of 3.3 × 106 sequences from 9.25 × 105 species were indexed using 4 base pair hashtable shards. A MapReduce searching strategy was used to distribute the search workload in a computing cluster environment. In addition, a one base pair permutation algorithm was used to account for single nucleotide polymorphisms and sequencing errors. Simulated datasets used to evaluate Kraken, a similar metagenomics classification tool, were used to measure and compare precision and accuracy. Finally using a same set of training sequences we compared Kraken, CLARK, and SMART within the same computing environment. Utilizing 12 computational nodes, we completed the classification of all datasets in under 10 min each using exact matching with an average throughput of over 1.95 × 106 reads classified per minute. With permutation matching, we achieved sensitivity greater than 83 % and precision greater than 94 % with simulated datasets at the species classification level. We demonstrated the application of this technique applied to conjunctival and gut microbiome metagenomics sequencing results. In our head to head comparison, SMART and CLARK had similar accuracy gains over Kraken at the species classification level, but SMART required approximately half the amount of RAM of CLARK.ConclusionsSMART is the first scalable, efficient, and rapid metagenomics classification algorithm capable of matching against all the species and sequences present in the NCBI GenBank and allows for a single step classification of microorganisms as well as large plant, mammalian, or invertebrate genomes from which the metagenomic sample may have been derived.