Efficient Computational Algorithm Research Articles

Efficient graph edit distance computation using isomorphic vertices

In this paper, we study the problem of graph edit distance (GED) computation. We empirically observe that real graph data contain many isomorphic substructures, which incur redundant computation. Based on this observation, we aim at reducing the cost of GED computation by avoiding redundant computation caused by isomorphic substructures. To detect isomorphic substructures, we precisely define a notion of vertex isomorphism and propose a dynamic programming algorithm that identifies isomorphic vertices in a graph. By taking advantage of isomorphic vertices, we develop an efficient GED computation algorithm. In experiments, we show that isomorphic vertices are effectively used in reducing the search space of GED computation, and as a result our approach improves the performance of GED computation.

Multivariate Birnbaum–Saunders distribution based on a skewed distribution and associated EM-estimation

We develop here a multivariate generalization of Birnbaum–Saunders (BS) distribution based on the multivariate skew-normal distribution. Some distributional characteristics and properties are presented, as well as a simple and efficient EM algorithm for the iterative computation of the maximum likelihood (ML) estimates of model parameters, through the hierarchical representation of the proposed model. The standard errors of the maximum likelihood estimates are calculated from the observed Fisher information matrix. Moreover, by using the tools, we present a log-linear regression model, where the the ML estimates are once again obtained using an EM algorithm. Finally, simulation studies and two applications to real data sets are presented for illustrating the model and the inferential results developed here.

Bayesian multistate modelling of incomplete chronic disease burden data.

A widely-used model for determining the long-term health impacts of public health interventions, often called a "multistate lifetable", requires estimates of incidence, case fatality, and sometimes also remission rates, for multiple diseases by age and gender. Generally, direct data on both incidence and case fatality are not available in every disease and setting. For example, we may know population mortality and prevalence rather than case fatality and incidence. This paper presents Bayesian continuous-time multistate models for estimating transition rates between disease states based on incomplete data. This builds on previous methods by using a formal statistical model with transparent data-generating assumptions, while providing accessible software as an R package. Rates for people of different ages and areas can be related flexibly through splines or hierarchical models. Previous methods are also extended to allow age-specific trends through calendar time. The model is used to estimate case fatality for multiple diseases in the city regions of England, based on incidence, prevalence and mortality data from the Global Burden of Disease study. The estimates can be used to inform health impact models relating to those diseases and areas. Different assumptions about rates are compared, and we check the influence of different data sources.

Planning Wireless Backhaul Links by Testing Line of Sight and Fresnel Zone Clearance

Microwave backhaul links are often used as wireless connections between telecommunication towers, in places where deploying optical fibers is impossible or too expensive. The relatively high frequency of microwaves increases their ability to transfer information at a high rate, but it also makes them susceptible to spatial obstructions and interference. Hence, when deploying wireless links, there are two conflicting considerations. First, the antennas height, selected from the available slots on each tower, should be as low as possible. Second, there should be a line of sight (LoS) between the antennas, and a buffer around the LoS defined by the first Fresnel zone should be clear of obstacles. To compute antenna heights, a planning system for wireless links has to maintain an elevation model, efficiently discover obstacles between towers, and execute Fresnel-zone clearance tests over a 3D model of the deployment area. In this article we present a system and algorithms for computing the height of antennas, by testing LoS and clearance of Fresnel zones. The system handles the following requirements: (1) the need to cover large areas, e.g., all of the USA, (2) big distance between towers, e.g., 100 kilometers, and (3) computing batches of thousands of pairs within a few minutes. We introduce three novel algorithms for efficient computation of antenna heights, we show how to effectively model and manage the large-scale geospatial data needed for the planning, and we present the results of tests over real-world settings.

Fitting additive risk models using auxiliary information.

There has been a growing interest in incorporating auxiliary summary information from external studies into the analysis of internal individual-level data. In this paper, we propose an adaptive estimation procedure for an additive risk model to integrate auxiliary subgroup survival information via a penalized method of moments technique. Our approach can accommodate information from heterogeneous data. Parameters to quantify the magnitude of potential incomparability between internal data and external auxiliary information are introduced in our framework while nonzero components of these parameters suggest a violation of the homogeneity assumption. We further develop an efficient computational algorithm to solve the numerical optimization problem by profiling out the nuisance parameters. In an asymptotic sense, our method can be as efficient as if all the incomparable auxiliary information is accurately acknowledged and has been automatically excluded from consideration. The asymptotic normality of the proposed estimator of the regression coefficients is established, with an explicit formula for the asymptotic variance-covariance matrix that can be consistently estimated from the data. Simulation studies show that the proposed method yields a substantial gain in statistical efficiency over the conventional method using the internal data only, and reduces estimation biases when the given auxiliary survival information is incomparable. We illustrate the proposed method with a lung cancer survival study.

Эффективные массово-параллельные алгоритмы восстановления данных в многомерном случае

A data recovery problem in multidimensional case by given values in the uniform nodes is considered. An efficient computational data recovery algorithm for a bulk parallelism model is proposed. The algorithm is based on transformations applied to summatory on V. A. Steklov’s kernels operator. The dependency between computational complexity and the problem parameters is shown. A software implementation of the algorithm is carried out in Open CL standard widely used for general-purpose computing on graphic processor units. Examples of surfaces reconstruction by the software are given.

ANFIS Model for Cost Analysis in a Dual Source Multi-Destination System

Managers face uncertainties while making allocation decisions, especially in dual or multi-source multi-destination inventory systems. Regrettably, many studies focus on classical methods as a technique for product distribution, which has never guaranteed a satisfactory solution. Real-life problems are non-deterministic polynomial-time hard (NP-hard), and solving such problems is relatively challenging. Such complicated problems need an efficient and robust computational hybrid algorithm. This study emphasises the need for a hybrid intelligent technique for effective product distribution. Soft computing hybrid algorithm, ANFIS was applied to product distribution in a double source multi-destination system. Rules were developed from available input datasets. Distributing products from dual manufacturing plants to fifteen available depots using the creative algorithm resulted in an overall 13.5% decrease in cost compared to the existing method adopted by the company. The result showed that the proposed method is relatively satisfactory and adequate for cost modelling. In addition, it is easy to use and outperforms the classical approach.

A Computationally-Efficient Algorithm to Enable Joint Optimization of Connected Automated Vehicles’ Trajectories and Signal Phasing and Timing in Coordinated Arterials

A Computationally-Efficient Algorithm to Enable Joint Optimization of Connected Automated Vehicles’ Trajectories and Signal Phasing and Timing in Coordinated Arterials

A Perception-Augmented Hidden Markov Model for Parent–Child Relations in Families of Youth with Type 1 Diabetes

In youth with Type 1 diabetes, adherence to medical treatment regimens requires the involvement of both parent and child. A clinic-integrated behavioral intervention in the Family Management of Diabetes (FMOD) trial was shown to be effective in controlling deterioration in glycemic level; yet the mechanism remains unknown. It is possible that the effectiveness is through improved parent–child relation. To investigate whether the intervention improves parent–child relations, we proposed a novel approach that allows differential perceptions of parent and child toward the unobserved parent–child relationship. Leveraging manifesto data collected from both parent and child in the FMOD trial, the proposed approach extended a standard hidden Markov model by inserting a layer of parent- and child-specific hidden states. We took a Bayesian perspective to estimation and developed an efficient computational algorithm to sample from the joint posterior distribution. Extensive simulations were conducted to demonstrate the performance of the proposed modeling framework. Application to the FMOD trial data reveals that families in the intervention arm are more likely to stay in the Harmonious parent–child relation state and less likely to transition from Harmonious to Indifferent state. Compared to parent, child tends to have a more heterogeneous perception of the parent–child relation.

Numerical Solution of Shrödinger Equations Based on the Meshless Methods

In this work, two-dimensional time-dependent quantum equation problems are studied. We introduce a numerical algorithm for solving the two-dimensional nonlinear complex quantum system with MLS and FDM methods. An efficient and accurate computational algorithm based on both, the moving least squares (MLS) and the finite difference (FDM) methods is proposed for solving it. The results demonstrate that the proposed algorithm is a robust algorithm with good accuracy. This is developed on MLS and FDM methods using numerical simulation for solving these kind of problems.

An Algorithm for Finding Feedback in a Problem with Constraints for One Class of Nonlinear Control Systems

We consider the construction of a feedback according to the Kalman algorithm for a continuous nonlinear control system on a finite time interval with control constraints where the right-hand side of the dynamics equations is linear in control and linearizable in the vicinity of the zero equilibrium position. The solution of an auxiliary optimal control problem with a quadratic functional is used for this task by analogy with the SDRE approach. Because this approach is used in the literature to find suboptimal synthesis in optimal control problems with a quadratic functional with formally linear systems where all coefficient matrices in differential equations and criteria can contain state variables, on a finite time interval it becomes necessary to solve a complicated matrix differential Riccati equations with state-dependent coefficient matrices. Due to the nonlinearity of the system this issue significantly increases the number of calculations for obtaining the coefficients of the gain matrix in the feedback and for obtaining synthesis with a given accuracy in comparison with the Kalman algorithm for linear-quadratic problems. The proposed synthesis construction algorithm is constructed using the extension principle proposed by V.F. Krotov and developed by V.I. Gurman and allows one not only to expand the scope of the SDRE approach to nonlinear control problems with control constraints in the form of closed inequalities, but also to propose a more efficient computational algorithm for finding the matrix of feedback gains in control problems on a finite interval. This article establishes the correctness of the application of the extension principle by introducing analogs of the Lagrange multipliers, which depend on the state and time, and also derives a formula for the suboptimal value of the quality criterion. The presented theoretical results are illustrated by calculating suboptimal feedbacks in the problems of managing three-sector economic systems.

Quantum transport of strongly interacting fermions in one dimension far out of equilibrium

In the study of quantum transport, much has been known about dynamics near thermal equilibrium. However, quantum transport far away from equilibrium is much less well understood---the linear response approximation does not hold for physics far out of equilibrium in general. In this work, motivated by recent cold atom experiments on probing quantum many-body dynamics of a one-dimensional XXZ spin chain, where a transition from ballistic to diffusive dynamics has been established by increasing the interaction strengths, we study the strong interaction limit of the one-dimensional spinless fermion model, which is dual to the XXZ spin chain. We develop a highly efficient computation algorithm for simulating the nonequilibrium dynamics of this system exactly, and examine the nonequilibrium dynamics starting from a density modulation quantum state. We find ballistic transport in this strongly correlated setting and show that a plane-wave description emerges at long-time evolution. We also observe a sharp distinction between transport velocities in short and long times as induced by interaction effects and provide a quantitative interpretation for the long-time transport velocity. We expect our results to shed light on the understanding of the dynamics of the XXZ spin chain in the strong interaction regime.

Dynamic spatio-temporal zero-inflated Poisson models for predicting capelin distribution in the Barents Sea

We consider modeling and prediction of Capelin distribution in the Barents Sea based on zero-inflated count observation data that vary continuously over a specified survey region. The model is a mixture of two components; a one-point distribution at the origin and a Poisson distribution with spatio-temporal intensity, where both intensity and mixing proportions are modeled by some auxiliary variables and unobserved spatio-temporal effects. The spatio-temporal effects are modeled by a dynamic linear model combined with the predictive Gaussian process. We develop an efficient posterior computational algorithm for the model using a data augmentation strategy. The performance of the proposed model is demonstrated through simulation studies, and an application to the number of Capelin caught in the Barents Sea from 2014 to 2019.

Treatment and effect of noise modelling in Bayesian operational modal analysis

Operational modal analysis (OMA) identifies the modal properties, e.g., natural frequencies, damping ratios and mode shapes, of a structure using ‘output-only’ ambient vibration data. Instrument noise need not be negligible in ambient vibration data, and it is often modelled statistically. Simple noise models, e.g., independent and identically distributed (i.i.d.) among data channels, are often used and are found to give reasonable results in typical applications, although there may be concerns for data with, e.g., low signal-to-noise (S/N) ratio, large difference in noise intensities or significant correlation among data channels. This work aims at investigating the effect of noise models on OMA performed with a Bayesian approach in the frequency domain. In addition to modal identification results, noise models are also assessed from a Bayesian evidence perspective. To enable the study, algorithms for efficient calculation of Bayesian statistics (most probable value and covariance matrix) are developed to account for general noise models that have not been considered in existing algorithms. As a further contribution to OMA theory, it is shown that, by a suitable transformation of data, an OMA problem with general noise model can be converted to one with i.i.d. noise model. Based on this analogy, asymptotic formulae for identification uncertainty of modal parameters, i.e., ‘uncertainty law’, have been developed. The theory reveals a definition for the modal S/N ratio that is an intuitive yet nontrivial generalisation of the existing formula for i.i.d. noise. The proposed objectives and methodology are investigated in a comprehensive study through synthetic, laboratory and field data.

Genetically Designed Wire Bundle Superscatterers

Experimental demonstration of superdirectivity and superscattering phenomena is among the long-standing challenges in electromagnetic theory. Efficient computational algorithms can contribute to this endeavor by suggesting new designs bypassing commonly accepted limitations. Here, we demonstrate a rectangular wire bundle superscatterer designed using a stochastic optimization algorithm. The structure encompassing wires of different lengths demonstrates superior scattering capabilities, bypassing the single channel dipole limit by an order of magnitude. The subwavelength wire bundle supports several resonant higher-order multipoles, which constructively contribute to the scattering, as we demonstrate experimentally. A new generation of genetically designed superscatterers may be used in a range of wireless applications, including point-to-point communications, smart beacons, and radar targets.

Ultra-High Dimensional Quantile Regression for Longitudinal Data: An Application to Blood Pressure Analysis

Despite major advances in research and treatment, identifying important genotype risk factors for high blood pressure remains challenging. Traditional genome-wide association studies (GWAS) focus on one single nucleotide polymorphism (SNP) at a time. We aim to select among over half a million SNPs along with time-varying phenotype variables via simultaneous modeling and variable selection, focusing on the most dangerous blood pressure levels at high quantiles. Taking advantage of rich data from a large-scale public health study, we develop and apply a novel quantile penalized generalized estimating equations (GEE) approach, incorporating several key aspects including ultra-high dimensional genetic SNPs, the longitudinal nature of blood pressure measurements, time-varying covariates, and conditional high quantiles of blood pressure. Importantly, we identify interesting new SNPs for high blood pressure. Besides, we find blood pressure levels are likely heterogeneous, where the important risk factors identified differ among quantiles. This comprehensive picture of conditional quantiles of blood pressure can allow more insights and targeted treatments. We provide an efficient computational algorithm and prove consistency, asymptotic normality, and the oracle property for the quantile penalized GEE estimators with ultra-high dimensional predictors. Moreover, we establish model-selection consistency for high-dimensional BIC. Simulation studies show the promise of the proposed approach. Supplementary materials for this article are available online.

An efficient hardware supported and parallelization architecture for intelligent systems to overcome speculative overheads

In the last few decades, technology advancements have paved the way for the creation of intelligent and autonomous systems that utilize complex calculations which are both time-consuming and central processing unit intensive. As a consequence, parallel processing systems are gaining popularity to enhance overall computer performance. Programmers should be able to efficiently utilize available hardware resources with parallelization in an ideal world. Through the automatic parallelization of sequential code, multithreading can be executed without extra supervision. However, a wide range of software dependencies prevents this from being feasible. An architectural framework for speculative parallelization along with an efficient memory analysis and computational algorithms for the code generation are proposed that can provide optimal performance. Furthermore, a suitable support of hardware design as a runtime library to the proposed architectural framework is presented which can be used to recover misspeculated results during execution to minimize speculative parallelism overhead. The implementation makes use of the Low-Level Virtual Machine compiler infrastructure and is tested on numerous benchmarks, thus making it highly scalable in terms of programming languages and architectures. According to our experimental results, there is significant potential for speedup increase. In comparison to the overall function speedup, that is, geomean speedup of 5.2× approximately when using the proposed architecture without hardware support, the proposed architectural framework and algorithm with hardware support give an average geomean speedup of 7.0× approximately on the given benchmark which is written in C/C++.

Numerical study of microwave impact on gas hydrate plugs in a pipeline

Purpose. Development of a technique for the numerical study on the decomposition of gas hydrate plugs in deep-water pipelines under microwave radiation using a coaxial source. Theoretical efficiency evaluation of using such an impact to unblock the pipelines. Methodology. Mathematical modeling and computational experiment. Findings. An original mathematical model is proposed to describe heat transfer processes during the decomposition of gas hydrates in a pipeline under the action of heat sources distributed over the volume. The non-stationary problem of heat transfer was considered in a one-dimensional formulation. An algorithm for numerical computation is proposed. A mathematical expression is obtained for distributed heat sources generated by the microwave radiation from a coaxially located SHF antenna. Parametric numerical studies on temperature fields and decomposition dynamics of a gas hydrate plug are performed for specified parameters of pipe and microwave radiation power. The boundaries of the decomposition area and the dynamics of change in this area are determined. The decomposition time of a gas hydrate plug with a diameter of 0.3 m was determined using a 300 W microwave source. The complete decomposition took approximately 40 hours. Originality. The task of thermal decomposition of a cylindrical gas hydrate plug in a pipeline due to microwave heating using a coaxial microwave power source has been considered for the first time. The process is viewed as a sequence of several stages: heating, heating and decomposition, decomposition after complete heating of the gas hydrate layer. To model the volumetric dissociation of gas hydrate, it was proposed to use special functions that characterize the amount of decomposed gas hydrate. The introduction of such functions makes it possible to construct an efficient computational algorithm taking into account the action of volumetric sources in the decomposition area. The known models mainly consider only surface thermal effect or microwave impact on gas hydrate in porous mediums. The presented model allows describing the decomposition during volumetric heating of a solid hydrate adequately. Practical value. Blocking plugs may occur due to hydrate formation when transporting gas through deep-water pipelines or through pipelines in cold environments. The elimination of such complications is a complex technical task. In particular, a special source of microwave radiation, which was proposed by the authors in previous works, can be used to unblock the pipeline. The device that makes the microwave radiation is located along the pipe axis. The results of this work allow us to evaluate the effectiveness of the microwave method for eliminating the gas hydrate plug. The mathematical model and computational method can be used in the development of appropriate technologies using a coaxial microwave heating source.

Computation of distributions of statistics by means of Markov chains

Abstract An approach to the construction of efficient algorithms for the exact computation of distributions of statistics by means of the Markov chains is described. The Pearson statistic, the number of empty cells for random allocations of particles, and the Kolmogorov – Smirnov statistic are considered as examples. Possibilities of extending the approach are discussed, in particular to the computation of the joint distributions of statistics.

Mixed-Integer Nonlinear Programming for Energy-Efficient Container Handling: Formulation and Customized Genetic Algorithm

Energy consumption is expected to be reduced while maintaining high productivity for container handling. This paper investigates a new energy-efficient scheduling problem of automated container terminals, in which quay cranes (QCs) and lift automated guided vehicles (AGVs) cooperate to handle inbound and outbound containers. In our scheduling problem, operation times and task sequences are both to be determined. The underlying optimization problem is mixed-integer nonlinear programming (MINLP). To deal with its computational intractability, a customized and efficient genetic algorithm (GA) is developed to solve the studied MINLP problem, and lexicographic and weighted-sum strategies are further considered. An <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\epsilon $ </tex-math></inline-formula> -constraint algorithm is also developed to analyze the Pareto frontiers. Comprehensive experiments are tested on a container handling benchmark system, and the results show the effectiveness of the proposed lexicographic GA, compared to results obtained with two commonly-used metaheuristics, a commercial MINLP solver, and two state-of-the-art methods.