Computational Aspects Research Articles

Distance measures for geometric graphs

A geometric graph is a combinatorial graph, endowed with a geometry that is inherited from its embedding in a Euclidean space. Formulation of a meaningful measure of (dis-)similarity in both the combinatorial and geometric structures of two such geometric graphs is a challenging problem in pattern recognition. We study two notions of distance measures for geometric graphs, called the geometric edit distance (GED) and geometric graph distance (GGD). While the former is based on the idea of editing one graph to transform it into the other graph, the latter is inspired by inexact matching of the graphs. For decades, both notions have been lending themselves well as measures of similarity between attributed graphs. If used without any modification, however, they fail to provide a meaningful distance measure for geometric graphs—even cease to be a metric. We have curated their associated cost functions for the context of geometric graphs. Alongside studying the metric properties of GED and GGD, we investigate how the two notions compare. We further our understanding of the computational aspects of GGD by showing that the distance is NP-hard to compute, even if the graphs are planar and arbitrary cost coefficients are allowed.As a computationally tractable alternative, we propose in this paper the Graph Mover's Distance (GMD), which has been formulated as an instance of the earth mover's distance. The computation of the GMD between two geometric graphs with at most n vertices takes only O(n3)-time. The GMD demonstrates extremely promising empirical evidence at recognizing letter drawings.

Computational aspects of real-time auralizations of jazz venues

Historically, architectural acoustics has focused on concert halls for European Classical music, even though other genres, including rock, blues, and Jazz, have large groups of followers as well. Additionally, the acoustic requirements for European Classical music differ fundamentally from Jazz and other music. For example, Jazz venues are typically smaller and dryer than a traditional European Classical concert hall. A recent survey showed that reverberation in these venues plays a secondary role to parameters that describe the affordance of a space to allow communication between improvising musicians. This presentation focuses on the unique computational auralization aspects of Jazz venues, including wave-based approaches for smaller enclosures, low-latency requirements, intelligent accompanying systems that can adapt ad-hoc to jazz soloists, and the use of anechoic jazz recordings as source material. The adequacies of different reproduction systems, from higher-order ambisonics and wave-field synthesis to headphone-based systems, will also be discussed. [Work supported by James West Fellowship, Leo and Gabriella Beranek Scholarship, and NSF HCC-1909229.]

Cloud model-based best-worst method for group decision making under uncertainty

This study aims to enhance computational and analytical aspects of multi-criteria group decision-making (MCGDM) under uncertainty. For this, we use the best-worst method (BWM) and cloud models to develop a more reliable MCGDM algorithm including three stages: first, collecting data through the BWM reference pairwise comparison; second, extracting interval-weights using the BWM bi-level optimisation models and aggregating different opinions via cloud models; and third, using the technique for order of preference by similarity to ideal solution (TOPSIS) to prioritise alternatives. We have also investigated the effectiveness of the proposed approach in a real-life problem of online learning platform selection within the context of the COVID-19 pandemic lockdown. The experiment results demonstrate the superiority of the proposed method over the Bayesian BWM in terms of computational time by 96%. Moreover, the proposed approach outperforms BWM and Bayesian BWM techniques by 33% and 25%, respectively, in terms of conformity to the decision-makers’ intuitive judgments. Our findings also bring important practical implications. Application of the proposed method led to robustness against the number of decision-makers and significantly increased time efficiency in group decision-making. Besides, the computations with the lower inconsistency enhanced the effectiveness of prioritisation in group decision-making.

Statistical Inference for Vaccine Efficacy: A Re-Randomization Procedure to Analyse Poisson Outcomes under Covariate-Adaptive Randomization

Re-randomization inference is used as an alternative approach to more traditional statistical methods. Free from parametric assumptions, its use is particularly suited for studies incorporating covariate-adaptive randomization, and can provide additional evaluation and confirmation of inferences drawn from the original analyses, for example, as a sensitivity analysis. We discuss methodological and computational aspects in the context of a Poisson regression and describe an approach to re-randomization inference. This is tested in a simulation study and then illustrated in a case study in which we evaluate vaccine efficacy data from a previously published influenza vaccine study. Our simulations indicate that re-randomization inference corrects for model misspecification. The case study, which accounted for the minimization factors, shows that the p-value and confidence limits from re-randomization inference agree with the original analysis. In conclusion re-randomization inference is a useful method that can be used to support vaccine clinical development.

Recent developments within the cosmic ray extremely distributed observatory

This contribution presents the recent research developments within the Cosmic Ray Extremely Distributed Observatory in the search for resolution of various scientific puzzles, ranging from fundamental physical questions to applications like the determination of earthquake precursors. The state-of-the art theoretical, numerical and computational aspects of these phenomena are addressed, as well as recent experimental developments for detection.

Decompositions of optimal averaged Gauss quadrature rules

Optimal averaged Gauss quadrature rules provide estimates for the quadrature error in Gauss rules, as well as estimates for the error incurred when approximating matrix functionals of the form uTf(A)v with a large matrix A∈RN×N by low-rank approximations that are obtained by applying a few steps of the symmetric or nonsymmetric Lanczos processes to A; here u,v∈RN are vectors. The latter process is used when the measure associated with the Gauss quadrature rule has support in the complex plane. The symmetric Lanczos process yields a real tridiagonal matrix, whose entries determine the recursion coefficients of the monic orthogonal polynomials associated with the measure, while the nonsymmetric Lanczos process determines a nonsymmetric tridiagonal matrix, whose entries are recursion coefficients for a pair of sets of bi-orthogonal polynomials. Recently, it has been shown, by applying the results of Peherstorfer, that optimal averaged Gauss quadrature rules, which are associated with a nonnegative measure with support on the real axis, can be expressed as a weighted sum of two quadrature rules. This decomposition allows faster evaluation of optimal averaged Gauss quadrature rules than the previously available representation. The present paper provides a new self-contained proof of this decomposition that is based on linear algebra techniques. Moreover, these techniques are generalized to determine a decomposition of the optimal averaged quadrature rules that are associated with the tridiagonal matrices determined by the nonsymmetric Lanczos process. Also, the splitting of complex symmetric tridiagonal matrices is discussed. The new splittings allow faster evaluation of optimal averaged Gauss quadrature rules than the previously available representations. Computational aspects are discussed.

NeighborNet: improved algorithms and implementation.

NeighborNet constructs phylogenetic networks to visualize distance data. It is a popular method used in a wide range of applications. While several studies have investigated its mathematical features, here we focus on computational aspects. The algorithm operates in three steps. We present a new simplified formulation of the first step, which aims at computing a circular ordering. We provide the first technical description of the second step, the estimation of split weights. We review the third step by constructing and drawing the network. Finally, we discuss how the networks might best be interpreted, review related approaches, and present some open questions.

Future Potential of Quantum Computing and Simulations in Biological Science.

The review article presents the recent progress in quantum computing and simulation within the field of biological sciences. The article is designed mainly into two portions: quantum computing and quantum simulation. In the first part, significant aspects of quantum computing was illustrated, such as quantum hardware, quantum RAM and big data, modern quantum processors, qubit, superposition effect in quantum computation, quantum interference,quantum entanglement, and quantum logic gates. Simultaneously, in the second part, vital features of the quantum simulation was illustrated, such as the quantum simulator, algorithms used in quantum simulations, and the use of quantum simulation in biological science. Finally, the review provides exceptional views to future researchers about different aspects of quantum simulation in biological science.

Editorial for the Special Issue “Computational Aspects and Software in Psychometrics II”

There has been tremendous progress in statistical software in the field of psychometrics in providing open-source solutions [...]

Bayesian structural identification using Gaussian Process discrepancy models

Bayesian model updating based on Gaussian Process (GP) models has received attention in recent years, which incorporates kernel-based GPs to provide enhanced fidelity response predictions. Although most kernel functions provide high fitting accuracy in the training data set, their out-of-sample predictions can be highly inaccurate. This paper investigates this problem by reformulating the problem on a consistent probabilistic foundation, reviewing common choices of kernel covariance functions, and proposing a new Bayesian model selection for kernel function selection, aiming to create a balance between fitting accuracy, generalizability, and model parsimony. Computational aspects are addressed via Laplace approximation and sampling techniques, providing detailed algorithms and strategies. Numerical and experimental examples are included to demonstrate the accuracy and robustness of the proposed framework. As a result, an exponential-trigonometric covariance function is characterized and justified based on the Bayesian model selection approach and observations of the sample autocorrelation function of the response discrepancies.

Computational aspects of entropy measures for metal organic frameworks

ABSTRACT A form of porous polymeric material known as metal-organic frameworks is made up of metal ions connected by organic bridging ligands. Entropy is used as a complexity indicator for graphs and is regarded as the graph's structural information content. The graph entropies have evolved into information-theoretic variables for computing the structure information of chemical graphs. The potential use in chemistry of the graph entropy portion has gained the attention of the research community. In this article, we investigate the explicit expressions of graph entropies for two categories of metal organic frameworks (MOFs) named as, M 1 TPyP − M 2 (TPyP= 5, 10, 15, 20-tetrakis (4-pyridyl) porphyrin and M 1 , M 2 = Fe and Co) MOFs and butylated hydroxytoluene-(BHT-) based metal-organic (M= Co, Fe, Mn, Cr) (MBHT) frameworks. we compute some numeric values for certain graph entropies in Tables. In the last, we present a comprehensive graphic comparison of our new results.

Analysis of Students' Computational Thinking Ability in Solving Contextual Problems

This study aims to examine students' computational thinking skills in solving contextual problems in the matter of a system of two-variable linear equations. This type of research uses descriptive qualitative approach. The subjects used in this study were 25 class VIII students of State Middle School in the 2022/2023 academic year on SPLDV material (System of Two-Variable Linear Equations). Data collection techniques and instruments in this study were written tests and interviews. Data analysis was carried out by first classifying the data, then presenting the data and ending by concluding the results of computational thinking indicators. The results showed that the percentage of students' computational thinking aspects, namely the decomposition aspect, was 70.30% in the good category, pattern recognition 58.63% sufficient category, abstraction 58.30% sufficient category, and thinking algorithm 50.47% sufficient category. So that students' computational thinking skills are included in the sufficient category. Meanwhile, based on the categorization of high-level students they have done well. However, there were several steps that were missed and not written down in the answer sheets, such as the decomposition and pattern recognition aspects which were not carried out properly. For the medium category, all stages of computational thinking have been carried out very well starting from decomposition, pattern recognition, abstraction, and thinking algorithms. For the low category, this category has not been able to carry out the stages of computational thinking properly.

First-Principles Study on the Mechanical Properties of Gd-Doped BCZT Ceramics

Due to their remarkable piezoelectric characteristics, (BaCa)(ZrTi)O3 (BCZT) ceramics exhibit vast potential for being employed in cutting-edge electromechanical apparatus. Extensive experimental studies have been conducted to better meet the practical needs of BCZT-based materials, focusing on their mechanical performance. However, there is a serious lack of research on the theoretical computational aspects. Here, first-principles calculations were utilized to evaluate the mechanical properties of BCZT-xGd ceramics. The structural models were established using the virtual crystal approximation (VCA) method. The investigated compounds demonstrate structural and mechanical strength, as evidenced by their negative formation energies and adherence to the Born stability criteria. Compared to pure BCZT, the substitution of Gd leads to a significant enhancement in the system’s elasticity and stiffness. The BCZT-0.05Gd with B-site doping demonstrates the highest level of Vicker’s hardness (HV), with the noteworthy observation that the inclusion of Gd concomitantly augments its machinability performance. Upon the incorporation of the Gd element, the anisotropic elasticity in the systems gradually transitions into isotropic elasticity, which favors a more uniform stress distribution and consequently reduces sensitivity to the formation and propagation of microcracks. These results indicate that BCZT-xGd exhibits potential for application in electromechanical systems.

On computing discretized Ricci curvatures of graphs: Local algorithms and (localized) fine-grained reductions

Characterizing shapes of high-dimensional objects via Ricci curvatures plays a critical role in many research areas in mathematics and physics. However, even though several discretizations of Ricci curvatures for discrete combinatorial objects such as networks have been proposed and studied by mathematicians, the computational complexity aspects of these discretizations have escaped the attention of theoretical computer scientists to a large extent. In this paper, we study one such discretization, namely the Ollivier-Ricci curvature, from the perspective of efficient computation by fine-grained reductions and local query-based algorithms. Our main contributions are the following.▹We relate our curvature computation problem to minimum weight perfect matching problem on complete bipartite graphs via fine-grained reduction.▹We formalize the computational aspects of the curvature computation problems in suitable frameworks so that they can be studied by researchers in local algorithms.▹We provide the first known lower and upper bounds on queries for query-based algorithms for the curvature computation problems in our local algorithms framework. En route, we also illustrate a localized version of our fine-grained reduction. We believe that our results bring forth an intriguing set of research questions, motivated both in theory and practice, regarding designing efficient algorithms for curvatures of geometrical objects.

Computational complexity aspects of super domination

Let G be a graph. A dominating set D⊆V(G) is a super dominating set if for every vertex x∈V(G)∖D there exists y∈D such that NG(y)∩(V(G)∖D))={x}. The cardinality of a smallest super dominating set of G is the super domination number of G. An exact formula for the super domination number of a tree T is obtained, and it is demonstrated that a smallest super dominating set of T can be computed in linear time. It is proved that it is NP-complete to decide whether the super domination number of a graph G is at most a given integer if G is a bipartite graph of girth at least 8. The super domination number is determined for all k-subdivisions of graphs. Interestingly, in half of the cases the exact value can be efficiently computed from the obtained formulas, while in the other cases the computation is hard. While obtaining these formulas, II-matching numbers are introduced and proved that they are computationally hard to determine.

Some Theoretical and Computational Aspects of the Truncated Multivariate Skew-Normal/Independent Distributions

In this article, we derive a closed-form expression for computing the probabilities of p-dimensional rectangles by means of a multivariate skew-normal distribution. We use a stochastic representation of the multivariate skew-normal/independent distributions to derive expressions that relate their probability density functions to the expected values of positive random variables. We also obtain an analogous expression for probabilities of p-dimensional rectangles for these distributions. Based on this, we propose a procedure based on Monte Carlo integration to evaluate the probabilities of p-dimensional rectangles through multivariate skew-normal/independent distributions. We use these findings to evaluate the probability density functions of a truncated version of this class of distributions, for which we also suggest a scheme to generate random vectors by using a stochastic representation involving a truncated multivariate skew-normal random vector. Finally, we derive distributional properties involving affine transformations and marginalization. We illustrate graphically several of our methodologies and results derived in this article.

Theoretical and computational aspects of configurational forces in three-dimensional crack problems

Mechanics in material space yields configurational forces (CFs) as an appropriate means to describe driving forces acting at defects or inhomogeneities in magnitude and direction. A thermodynamically based approach to obtain a weak formulation of CFs with virtual defect displacements as test functions is introduced first, starting from a global variational energy balance instead of from the currently employed local configurational balance equation. A theoretical basis and issues of three-dimensional implementation are presented to calculate vectors of CFs along a crack front, providing information on local loading and deflection angles. Spurious nodal CFs are separated from physical ones, inter alia requiring surface integrals along the crack faces. Within a linear elastic framework, these are efficiently replaced by an algebraic expression, providing CFs at crack front nodes in a very good approximation, which is eventually demonstrated by means of analytical and numerical examples.

Review of Methods to Solve Desiccation Cracks in Clayey Soils

This paper reviews numerical methods used to simulate desiccation cracks in clayey soils. It examines five numerical approaches: Finite Element (FEM), Lattice Boltzmann (LBM), Discrete Element (DEM), Cellular Automaton (CAM), and Phase Field (PFM) Methods. The paper presents a simplified description of the methods, including their basic numerical formulations. Several factors such as the multiphase nature of soils, heterogeneity, nonlinearities, coupling, scales of analysis, and computational aspects are discussed. The review highlights the characteristics, strengths, and limitations of each method. FEM shows a good capacity to deal with the thermo-hydromechanical behavior of clays when drying that complement well with the ability of DEM to deal with particle interactions as well as LBM, PFM, and CAM to deal with complex crack patterns. The article concludes by reviewing the integration of multiple numerical methods to enhance the simulation of desiccation cracks in clayey soils and proposing what is the best option to continue improving the study of this problem.

Preface: 5th International Conference on Mechanics, Simulation and Control (ICMSC 2023)

The 2023 5th International Conference on Mechanics, Simulation and Control (ICMSC 2023) was held during June 24-25, 2023 in San Jose, USA. This conference aims to bring together academic scientists, leading engineers, industry researchers and scholar students to exchange and share their experiences and research results, and will discuss the practical challenges encountered and the solutions adopted. The conference provided delegates with the opportunity to meet face-to-face with new ideas and application experiences, build business or research relationships, and find global partners for future collaboration.
 ICMSC 2023 has been deliberated to showcase research accomplishments and to enrich the educational experience in about all aspects of mechanical engineering, applied mechanics, computer simulation, mathematical modeling, intelligent computing, automation and control engineering. The conference organizing committee has invited three keynote speakers. The two-day conference with nearly 28 oral presentations created the heart of the conference and provided ample opportunity for discussion during the conference.
 We have many people to thank and would like to put the appreciation of their contribution on record. In this regard, the largest burden fell upon the experienced shoulders of keynote speakers, session chairs, and our technical reviewers along with substantial assistance received from active student volunteers.
 The ICMSC Conference Committee
 San Jose, USA

A stochastic log-logistic diffusion process: Statistical computational aspects and application to real data

This article introduces a new stochastic diffusion process based on the theory of diffusion processes whose mean function is proportional to the log-logistic growth curve. The main characteristics of the process are analyzed, including the transition probability density function, the mean functions and in particular, the auto-correlation function between two times of the process. The parameters of the process are estimated by maximum likelihood method using discrete sampling. The simulated annealing algorithm is applied after bounding the parametric space by a strategy procedure to solve the likelihood equations. The behavior of the diffusion process here derived is finally applied to study an example for the growth data of a microorganism culture.