Efficient Computational Solution Research Articles

Medoid splits for efficient random forests in metric spaces

An adaptation of the random forest algorithm for Fréchet regression is revisited, addressing the challenge of regression with random objects in metric spaces. To overcome the limitations of previous approaches, a new splitting rule is introduced, substituting the computationally expensive Fréchet means with a medoid-based approach. The asymptotic equivalence of this method to Fréchet mean-based procedures is demonstrated, along with the consistency of the associated regression estimator. This approach provides a sound theoretical framework and a more efficient computational solution to Fréchet regression, broadening its application to non-standard data types and complex use cases.

Accurate finite element modeling of multilayered flexible piezoelectric energy harvesting devices with strong coupling of structure, piezoelectricity, and circuit

Multilayered flexible piezoelectric energy harvesting devices (FPEDs) are a new future harvesting technology which are highly flexible and lightweight than familiar cantilever piezoelectric energy harvesters. This mechanical vibration driven FPED is a strongly coupled multiphysics phenomena that involve complex natural three-way interaction among the composite piezoelectric structure, the electric charge accumulated in the piezoelectric material, and a controlling electrical circuit attached to it. Efficient and accurate computational solution approaches are essential for analyzing these mechanical vibration-driven FPEDs to capture the main physical aspect of the coupled phenomena and to accurately predict the output voltage. While there are some numerical models for simple familiar cantilever type piezoelectric energy harvester reported in the literature, a fully three-dimensional strongly coupled model for complex material distributed, complex geometry, and multilayered FPED involving strong coupling of structure, piezoelectricity, and circuit phenomena has not yet been developed. A partitioned iterative algorithm is developed using a hierarchical decomposition approach wherein the coupled three fields are solved separately and coupled through loop union integration techniques that provide an efficient and accurate simulation of FPEDs. The simulation results matched the experiment results very well. This study provides a basis for the natural extension of partitioned iterative finite element coupled algorithm to simulate the future piezoelectric energy harvesting technologies involving a strong coupling of structure, piezoelectricity, and circuit phenomenon.

A Cartesian-Based Trajectory Optimization with Jerk Constraints for a Robot

To address the time-optimal trajectory planning (TOTP) problem with joint jerk constraints in a Cartesian coordinate system, we propose a time-optimal path-parameterization (TOPP) algorithm based on nonlinear optimization. The key insight of our approach is the presentation of a comprehensive and effective iterative optimization framework for solving the optimal control problem (OCP) formulation of the TOTP problem in the (s,s˙)-phase plane. In particular, we identify two major difficulties: establishing TOPP in Cartesian space satisfying third-order constraints in joint space, and finding an efficient computational solution to TOPP, which includes nonlinear constraints. Experimental results demonstrate that the proposed method is an effective solution for time-optimal trajectory planning with joint jerk limits, and can be applied to a wide range of robotic systems.

Polynomial time multiplication and normal forms in free bands

We present efficient computational solutions to the problems of checking equality, performing multiplication, and computing minimal representatives of elements of free bands. A band is any semigroup satisfying the identity x2≈x and the free bandFB(k) is the free object in the variety of k-generated bands. Radoszewski and Rytter developed a linear time algorithm for checking whether two words represent the same element of a free band. In this paper we describe an alternate linear time algorithm for the same problem. The algorithm we present utilises a representation of words as synchronous deterministic transducers that lend themselves to efficient (quadratic in the size of the alphabet) multiplication in the free band. This representation also provides a means of finding the short-lex least word representing a given free band element with quadratic complexity.

Finding an Efficient Computational Solution for the Bates Partial Integro-Differential Equation Utilizing the RBF-FD Scheme

This paper proposes a computational solver via the localized radial basis function finite difference (RBF-FD) scheme and the use of graded meshes for solving the time-dependent Bates partial integro-differential equation (PIDE) arising in computational finance. In order to avoid facing a large system of discretization systems, we employ graded meshes along both of the spatial variables, which results in constructing a set of ordinary differential equations (ODEs) of lower sizes. Moreover, an explicit time integrator is used because it can bypass the need to solve the large discretized linear systems in each time level. The stability of the numerical method is discussed in detail based on the eigenvalues of the system matrix. Finally, numerical tests revealed the accuracy and reliability of the presented solver.

Dual Pricing Optimization for Live Video Streaming in Mobile Edge Computing With Joint User Association and Resource Management

Mobile live video streaming is expected to become mainstream in the fifth generation (5G) mobile networks. To boost the Quality of Experience (QoE) of streaming services, the integration of Scalable Video Coding (SVC) with Mobile Edge Computing (MEC) becomes a natural candidate due to its scalability and the reliable transmission supports for real-time interactions. However, it still takes efforts to integrate MEC into video streaming services to exploit its full potentials. We find that the efficiency of the MEC-enabled cellular system can be significantly improved when the requests of users can be redirected to proper MEC servers through optimal user associations. In light of this observation, we jointly address the caching placement, video quality decision, and user association problem in the live video streaming service. Since the proposed nonlinear integer optimization problem is NP-hard, we first develop a two-step approach from a Lagrangian optimization under the dual pricing specification. Further, to have a computation-efficient solution and less performance loss, we provide a one-step Lagrangian dual pricing algorithm by the convex transformation of non-convex constraints. The simulations show that the service quality of live video streaming can be remarkably enhanced by the proposed algorithms in the MEC-enabled cellular system.

Novel AoD Estimation Algorithms for WSSUS and Non-WSSUS V2V Channel Modeling

In this paper, we propose efficient computational solutions for estimating the statistical properties of wide-sense stationary un-correlative scattering (WSSUS) and non-WSSUS multiple-input multiple-output (MIMO) vehicle-to-vehicle (V2V) channel models. Specifically, in the WSSUS channel models, we first estimate the angle of departure (AoD) for the non-line of sight (NLoS) propagation components. In this manner, the complex channel impulse response (CIR), which are widely used in the existing literature to characterize the wireless channel physical properties, can be estimated on the basis of the estimated AoD and defined model parameters. Conversely, in the non-WSSUS channel models, by estimating the AoD in the initial stage, the real-time complex CIR of the V2V channel model can be estimated on the basis of the estimated AoD and the moving time/velocities/directions of the mobile transmitter (MT) and mobile receiver (MR). In the estimation process of the aforementioned cases, we introduce different solutions to convert the CIRs from the complex domain to the real-value domain, thereby optimizing the computational efficiency for investigating channel characteristics as compared to existing methods. Numerical results of the WSSUS and non-WSSUS MIMO V2V channel characteristics, such as the spatial-temporal (ST) cross-correlation functions (CCFs) and auto-correlation functions (ACFs), are estimated on the basis of the estimated complex CIRs. These results are in agreements with theoretical ones, indicating that the proposed algorithms are practical for estimating the WSSUS and non-WSSUS MIMO V2V channel characteristics.

Joint Clustering and Power Allocation in Coordinated Multipoint Assisted C-NOMA Cellular Networks

We consider a wireless network consisting of two adjacent cells, where the joint transmission (JT) coordinated multipoint (CoMP) is established to assist the user equipments (UEs) located at the edge of each cell. In addition, full-duplex (FD) cooperative non-orthogonal-multiple-access (C-NOMA) is invoked within each cell to improve the data rates of the UEs and to assist those at the cell edge. The UEs are categorized into two groups, namely, cell-center UEs (<inline-formula> <tex-math notation="LaTeX">$CUs$ </tex-math></inline-formula>) and cell-edge UEs (<inline-formula> <tex-math notation="LaTeX">$EUs$ </tex-math></inline-formula>). The <inline-formula> <tex-math notation="LaTeX">$CUs$ </tex-math></inline-formula> are the UEs located around the center of each cell. Meanwhile, the <inline-formula> <tex-math notation="LaTeX">$EUs$ </tex-math></inline-formula> are the UEs located at the edge of each cell, where the JT-CoMP is applied since they have less distinctive received power from two cells. In this paper, a framework to jointly optimize the power control and the UEs clustering of CoMP-assisted FD C-NOMA system is formulated as an optimization problem to maximize the network sum-rate while guaranteeing the required quality-of-service of UEs. The formulated problem is a non-convex mixed-integer non-linear program that cannot be solved in a straightforward manner. To tackle this issue, the formulated problem is decomposed into an inner power allocation problem and an outer UEs clustering problem. For the inner problem, a computational-efficient solution is obtained. Meanwhile, the outer problem is reformulated as a one-to-one three-sided matching game. Then, a low-complexity near-optimal clustering algorithm is proposed. The simulation results demonstrate that 1) the optimality of the power control solution; 2) the CoMP-assisted FD C-NOMA has a superior performance compared to CoMP-assisted half-duplex (HD) C-NOMA and CoMP NOMA schemes for moderate values of self-interference. It has been also shown that the proposed solution achieves around 96.5&#x0025; of the average achievable network sum-rate of the optimal solution.

Virtual LiDAR Simulation as a High Performance Computing Challenge: Toward HPC HELIOS++

The software HELIOS++ simulates the laser scanning of a given virtual scene that can be composed of different spatial primitives and 3D meshes with distinct granularity. The high computational cost of this type of simulation software demands efficient computational solutions. Classical solutions based on GPU are not well suited when irregular geometries compose the scene combining different primitives and physics models because they lead to different computation branches. In this paper, we explore the usage of parallelization strategies based on static and dynamic workload balancing and heuristic optimization strategies to speed up the ray tracing process based on a k-dimensional tree (KDT). Using HELIOS++ as our case study, we analyze the performance of our algorithms on different parallel computers, including the CESGA FinisTerrae-II supercomputer. There is a significant performance boost in all cases, with the decrease in computation time ranging from 89.5% to 99.4%. Our results show that the proposed algorithms can boost the performance of any software that relies heavily on a KDT or a similar data structure, as well as those that spend most of the time computing with only a few synchronization barriers. Hence, the algorithms presented in this paper improve performance, whether computed on personal computers or supercomputers.

Coupled heat transfer model for the combustion and steam characteristics of coal-fired boilers

This paper presents a coupled heat transfer model for coal-fired boilers that integrates boiler’s fire side computational fluid dynamics (CFD) solution with the boiler’s steam cycle, and thus, can predict the boiler steam temperatures subject to different boiler design and operation conditions. In this coupled model, the heat transfer submodels describing the gas-steam heat exchange for each part of boiler heating surfaces are integrated by exchanging the steam temperatures iteratively. This coupled heat transfer model is self-consistent since it ensures the fire side heat transfer solution to always coincide with the steam temperature changes across each boiler heating surface. This model is specifically designed to provide efficient computational solutions to engineering boiler problems in which the changes of boiler steam temperatures subject to different boiler design and operation conditions are of the major concerns. This model is applied to a 320 MW subcritical boiler to evaluate the impacts of coal switching and boiler operation adjustment on boiler’s heat transfer distributions and steam temperatures. The simulation results demonstrate that this model can effectively capture the complex coupled heat transfer behavior between the fire and steam sides of boiler and serve as an efficient predictive tool for engineering boiler problems.

A Heuristic Algorithm for solving Integer Linear Programming Problem and Unveiling the Applications

There is a growing need for integer solutions in industries, production units, etc. Specifically, there is an increasing demand to develop precise methods for solving integer-programming problems (IPPs). In this paper, we propose a new algorithm for solving IPPs in a general form by combining two decomposition techniques: Benders decomposition (BD) and decomposition-based pricing methods (DBP). Moreover, we generate some conditions for solving problems having either infeasible or unbounded solutions. In addition, we present an application and evaluation of a solution method for solving IPPs, while also giving a brief description of the different classical decomposition methods, namely the Dantzig-Wolfe decomposition (DWD), decomposition-based pricing (DBP), Benders decomposition (BD), and recently proposed improved decomposition (ID) methods for solving IPPs.We also discuss the use of the decomposition methods for solving IPPS to develop a heuristic algorithm, describe the limitations of the classical algorithms, and present extensions enabling its application to a broader range of problems. To illustrate the decomposition procedures, we will provide corresponding models and numerical results for two standard mathematical programs: the Fixed Charge Problem (FCP) and the Facility Location Problem (FLP). Our findings from this study suggest that our algorithm produces the most efficient computational solutions of IPPs.&#x0D; Journal of Bangladesh Academy of Sciences, Vol. 44, No. 1, 13-31, 2020

Behavior of circular concrete-filled double steel tubular slender beam-columns including preload effects

The hollow circular steel tubes support the permanent and construction loads of several upper floors during the construction of a high-rise building structure before the concrete is filled into the steel tubes to form circular concrete-filled double steel tubular (CFDST) columns. The preloads acting on the steel tubes may cause a marked reduction in the performance of slender CFDST columns. This paper presents a numerical modeling technique underlying the method of fiber elements for quantifying the behavior of circular slender CFDST beam-columns loaded eccentrically with preloads acting on the hollow steel tubes. The numerical model considers the concrete confinement, second-order effects, initial geometric imperfection, deformations induced by preloads as well as geometric and material nonlinearities. The dynamic nonlinear equilibrium functions are solved using efficient computational solution algorithms developed. The validation of the numerical modeling technique is made by comparing computations with existing results obtained by experiments and finite element analyses. The influences of preloads on the structural behavior of slender CFDST beam-columns with various important parameters are investigated by means of utilizing the developed computer model. It is shown that the numerical modeling method is an efficient computational simulation and design tool for slender CFDST columns including preloads.

Chromosome-level hybrid de novo genome assemblies as an attainable option for nonmodel insects.

The emergence of third-generation sequencing (3GS; long-reads) is bringing closer the goal of chromosome-size fragments in de novo genome assemblies. This allows the exploration of new and broader questions on genome evolution for a number of nonmodel organisms. However, long-read technologies result in higher sequencing error rates and therefore impose an elevated cost of sufficient coverage to achieve high enough quality. In this context, hybrid assemblies, combining short-reads and long-reads, provide an alternative efficient and cost-effective approach to generate de novo, chromosome-level genome assemblies. The array of available software programs for hybrid genome assembly, sequence correction and manipulation are constantly being expanded and improved. This makes it difficult for nonexperts to find efficient, fast and tractable computational solutions for genome assembly, especially in the case of nonmodel organisms lacking a reference genome or one from a closely related species. In this study, we review and test the most recent pipelines for hybrid assemblies, comparing the model organism Drosophila melanogaster to a nonmodel cactophilic Drosophila, D. mojavensis. We show that it is possible to achieve excellent contiguity on this nonmodel organism using the dbg2olc pipeline.

Analyzing Decomposition Procedures in LP and Unraveling for the Two Person Zero Sum Game and Transportation Problems

This paper studies some decomposition methods, including Dantzig-Wolfe decomposition (DWD), decomposition-based pricing (DBP), Benders decomposition (BD), and a recently proposed improved decomposition (ID) method for solving linear programs (LPs). The authors then develop a new decomposition algorithm for solving LPs in a general form, allowing authors to combine the concept of Benders decomposition and decomposition-based pricing methods. The authors generate conditions for solving problems that have either infeasible or unbounded solutions. As an illustration, the authors give the corresponding models and numerical results for two standard mathematical programs: the two-person zero-sum game and the transportation problem. The authors compare several procedures and identify which one produces the best solution by giving the authors the smallest iteration number. This study reveals that the algorithm along with Benders decomposition produce the most efficient computational solutions of LPs.

Learning Activation Functions in Deep (Spline) Neural Networks

We develop an efficient computational solution to train deep neural networks (DNN) with free-form activation functions. To make the problem well-posed, we augment the cost functional of the DNN by adding an appropriate shape regularization: the sum of the second-order total-variations of the trainable nonlinearities. The representer theorem for DNNs tells us that the optimal activation functions are adaptive piecewise-linear splines, which allows us to recast the problem as a parametric optimization. The challenging point is that the corresponding basis functions (ReLUs) are poorly conditioned and that the determination of their number and positioning is also part of the problem. We circumvent the difficulty by using an equivalent B-spline basis to encode the activation functions and by expressing the regularization as an $\ell _1$ -penalty. This results in the specification of parametric activation function modules that can be implemented and optimized efficiently on standard development platforms. We present experimental results that demonstrate the benefit of our approach.

Uniform Sampling Methodology to Construct Projection Matrices for Angle-of-Arrival Estimation Applications

This manuscript firstly proposes a reduced size, low-complexity Angle of Arrival (AoA) approach, called Reduced Uniform Projection Matrix (RUPM). The RUPM method applies a Uniform Sampling Matrix (USM) criterion to sample certain columns from the obtained covariance matrix in order to efficiently find the directions of the incident signals on an antenna array. The USM methodology is applied to reduce the dependency between the adjacent sampled columns within a covariance matrix; then, the sampled matrix is used to construct the projection matrix. The size of the obtained projection matrix is reduced to minimise the computational complexity in the searching grid stage. A theoretical analysis is presented to demonstrate that the USM methodology can increase the Degrees of Freedom (DOFs) with the same aperture size and number of sampled columns compared to the classical sampling criterion. Then, a polynomial root is constructed as an alternative efficient computational solution of the UPM method in a one-dimensional (1D) array spectrum peak searching problem. It is found that this distribution increases the number of produced nulls and enhances noise immunity. The advantage of the RUPM method is that it is appropriate to apply for any array configuration while the Root-UPM offers better estimation accuracy with less execution time under a uniform linear array condition. A computer simulation based on various scenarios is performed to demonstrate the theoretical claims. The proposed direction-finding methods are compared with several AoA methods in terms of the required execution time, Signal-to-Noise Ratio (SNR) and different numbers of data measurements. The results verify that the new methods can achieve significantly better performance with reduced computational demands.

Fiber element simulation of interaction behavior of local and global buckling in axially loaded rectangular concrete-filled steel tubular slender columns under fire exposure

Abstract Slender rectangular thin-walled concrete-filled steel tubular (CFST) columns in composite building structures exposed to fire may experience the interaction of local and global buckling. Numerical investigations on the interaction buckling responses of such columns under fire exposure have been rarely reported. This paper describes a fiber-based computational model for the prediction of the fire-resistance and interaction responses of local and global buckling of concentrically-loaded slender CFST columns made of rectangular sections exposed to fire. The thermal analysis is undertaken to calculate the distribution of temperatures in the column cross-section considering the effects of the air gap between concrete and steel, exposure surface emissivity as well as moisture content in concrete. The local and post-local buckling models proposed previously for steel tube walls at elevated temperatures are incorporated in the inelastic analysis of cross-sections to model the progressive post-local buckling. The global buckling analysis of slender CFST columns exposed to fire accounts for the effects of material and geometric nonlinearities as well as local buckling. Efficient computational procedure and solution algorithms are developed to solve the nonlinear equilibrium dynamic functions of loaded slender CFST columns exposed to fire. Independent experimental and numerical results on slender CFST columns are utilized to validate the computational model. The interaction behavior of local and global buckling and fire-resistance of slender rectangular CFST columns are investigated. It is shown that the developed computational model provides a reasonably accurate and efficient method for the prediction of the interaction buckling responses as well as the fire-resistance of slender CFST columns subjected to axial loading and fire.

SpliceTAPyR — An Efficient Method for Transcriptome Alignment

RNA-Seq is a Next-Generation Sequencing (NGS) protocol for sequencing the messenger RNA in a cell and generates millions of short sequence fragments, reads, in a single run. These reads can be used to measure levels of gene expression and to identify novel splice variants of genes. One of the critical steps in an RNA-Seq experiment is mapping NGS reads to the reference genome. Because RNA-Seq reads can span over more than one exon in the genome, this task is challenging. In the last decade, tools for RNA-Seq alignment have emerged, but most of them run in two phases. First, the pipeline only maps reads that have a direct match in the reference, and the remaining are set aside as initially unmapped reads. Then, they use heuristics based approaches, clustering or even annotations, to decide where to align the later. This work presents an efficient computational solution for the problem of transcriptome alignment, named SpliceTAPyR. It identifies signals of splice junctions and relies on compressed full-text indexing methods and succinct data structures to efficiently align RNA-Seq reads in a single phase. This way it achieves the same or better accuracy than other tools while using considerably less memory and time to the most competitive tools.

High-Resolution Rapid Refresh Model Data Analytics Derived on the Open Science Grid to Assist Wildland Fire Weather Assessment

AbstractTerabytes of weather data are generated every day by gridded model simulations and in situ and remotely sensed observations. With this accelerating accumulation of weather data, efficient computational solutions are needed to process, archive, and analyze the massive datasets. The Open Science Grid (OSG) is a consortium of computer resources around the United States that makes idle computer resources available for use by researchers in diverse scientific disciplines. The OSG is appropriate for high-throughput computing, that is, many parallel computational tasks. This work demonstrates how the OSG has been used to compute a large set of empirical cumulative distributions from hourly gridded analyses of the High-Resolution Rapid Refresh (HRRR) model run operationally by the Environmental Modeling Center of the National Centers for Environmental Prediction. These cumulative distributions derived from a 3-yr HRRR archive are computed for seven variables, over 1.9 million grid points, and each hour of the calendar year. The HRRR cumulative distributions are used to evaluate near-surface wind, temperature, and humidity conditions during two wildland fire episodes—the North Bay fires, a wildfire complex in Northern California during October 2017 that was the deadliest and costliest in California history, and the western Oklahoma wildfires during April 2018. The approach used here illustrates ways to discriminate between typical and atypical atmospheric conditions forecasted by the HRRR model. Such information may be useful for model developers and operational forecasters assigned to provide weather support for fire management personnel.

Machine learning meets genome assembly.

With the recent advances in DNA sequencing technologies, the study of the genetic composition of living organisms has become more accessible for researchers. Several advances have been achieved because of it, especially in the health sciences. However, many challenges which emerge from the complexity of sequencing projects remain unsolved. Among them is the task of assembling DNA fragments from previously unsequenced organisms, which is classified as an NP-hard (nondeterministic polynomial time hard) problem, for which no efficient computational solution with reasonable execution time exists. However, several tools that produce approximate solutions have been used with results that have facilitated scientific discoveries, although there is ample room for improvement. As with other NP-hard problems, machine learning algorithms have been one of the approaches used in recent years in an attempt to find better solutions to the DNA fragment assembly problem, although still at a low scale. This paper presents a broad review of pioneering literature comprising artificial intelligence-based DNA assemblers-particularly the ones that use machine learning-to provide an overview of state-of-the-art approaches and to serve as a starting point for further study in this field.