Computational Aspects Research Articles

Computational Aspects of Psychometric Methods with R

Computational Aspects of Psychometric Methods with R

Analysis of the Vibration Effect on the Trailing Edge Noise for Wind Turbine Applications

The noise emitted is a factor in determining the location for installing wind turbines. It can be a concern for people living near wind farms due to the nuisance and health problems caused by noise emissions. Therefore, noise estimation is one of the most relevant aspects of wind turbine design. Aerodynamic noise can be caused by air motion around the turbine blades. This can create a swooshing or whooshing sound as the blades pass through the air. Aerodynamic noise tends to be more prominent at higher wind speeds when the turbine is operating at full capacity. The trailing edge noise is the main source of wind turbine aerodynamic noise. In this work, semi-analytical solutions for modeling this effect are used. The Ffowcs Williams and Hawkings (FW-H) acoustic analogy is used to predict far-field noise generated by wind turbine blades moving in the air. The equations of the rotor–tower–nacelle structure are obtained by the classical finite element method for a typical onshore wind turbine to predict the blade vibration signal. This new formulation is applied to rotating sources and statistical analysis of broadband trailing edge noise. The novelty of this work is to model the acoustic–structure interaction between the wind flow and the blade structure vibration. In this context, the acoustic far-field pressure emissions are modified by source vibration. In this point, this work compares the acoustic pressure values from rigid and flexible sources. Once the relationship between acoustic emission and wind turbine structural vibration is determined, the potential application of this research is, for example, to provide valuable information about the health and integrity of the blades. Thus, the goal of this work is to predict the aerodynamic noise of a typical large wind turbine taking into account its structural behavior. Therefore, Formulation 1A is used to calculate the far-field noise radiated from the trailing edge of wind turbine blades in a low Mach number flow. The vibration effects are input variables to evaluate the intensity and acoustic pressure values. Some computational aspects are discussed and the numerical model is clarified. The acoustic predictions are validated with analytical results and experimental measurements that are available in the scientific literature. The numerical results of the FW-H acoustic analogy demonstrate that the aerodynamic noise value is predominantly low frequency in the sections closest to the source.

Accuracy Assessment of an Image-Based Cloud-Based Indoor Mapping Platform as an Alternative for Desktop Applications

Abstract. The generation of 3D spatial datasets, especially in indoor space, has continually been a challenge in developing spatial applications that are aimed for the realistic representation of the real world. Despite their potential, the creation of 3D spatial data remains a challenge, particularly in terms of cost and complexity associated with traditional methods. While this method proves to be a reliable and accurate method of creating such data, this approach entails challenges in economic, computational, and human resource aspects. Alternatively, omnidirectional images have emerged as cost-effective alternatives for representing 3D spaces, offering a comprehensive field of view and facilitating the generation of structure and appearance representations through mesh-based 3D maps. This study evaluates the geometric accuracy of a cloud-based image-based mapping platform for generating 3D representations of indoor spaces, aiming to assess its suitability as an alternative to desktop-based platforms. We obtained 30 sample lines and compare respective measurements from each of the generated meshes to a ground truth value. Results show that while there are no significant differences in the mean errors, the mesh generated from a cloud-based platform produced minimal errors. This demonstrates the potential of this platform for generating 3D representations of indoor space, acceptable in both geometric accuracy and visualization capabilities.

The Logic and Application of Greedy Algorithms

Abstract. Being easy to implement and offering faster solutions, the greedy algorithms have widely been used in solving combinatorial optimization problems in computer science and many practical applications, such as resource allocation and data compression. This paper aims at analyzing the definitions, theory, and application of greedy algorithms in computer science as well as their logic and efficacy in numerous situations. In this analysis, concepts such as the greedy choice property and optimal substructure are examined as essential to understanding how greedy algorithms operate. The provided analysis mainly assumes prior knowledge of concepts and problems like the Fractional Knapsack problem and the Shortest Path problem solution as well as some practical problems, for example, resource allocation and the exact coin change problem. Analytical data and real applications are used to analyze the performance of greedy algorithms vis-a-vis other categories, such as the dynamic programming paradigm. The results reveal that greedy algorithms have the potential for great efficiency while also being restricted by certain parameters. They are ideal in the following aspects pertaining to computational aspects, that is, speed, ease of implementation, and applicability to large datasets. But they do not necessarily promise strong optimality in certain situations. The conclusion notes that greedy algorithms deserve further applications when the specified conditions are met and states that the effectiveness of these conditions needs to be studied.

Synthesis, Theoretical Gaussian Quantum Chemical Calculations, and Spectroscopic Elucidation of N‐(4‐bromophenyl)‐10H‐phenothiazine‐10‐carboxamide

AbstractPhenothiazine derivative N‐(4‐bromophenyl)‐10H‐phenothiazine‐10‐carboxamide (BPC), a novel compound is selected for the present study based on the reported effectiveness of phenothiazine towards neurodegenerative diseases. Synthesis and characterization of BPC are carried out in the present work. To comprehend the electronic and structural aspects of BPC, quantum chemical studies are performed. Computational aspects using the density functional theory method using B3LYP/6–31 G ++(d,p) as a basis set are studied for the geometry optimization of the molecule. To analyze the reactivity, stability, and intramolecular interactions of the compound frontier orbital molecular analysis, molecular electrostatic potential distribution, and Fukui function are implemented. Calculations have been made to determine the electrophilic and nucleophilic descriptors of the title compounds by Fukui function evaluations on atomic charges. To visualize and quantify the localization of electrons and molecular orbitals in the molecule, electron localization function, localized orbital locator analysis and orbital localization analysis are carried out for a better understanding of chemical bonding. Basin Analysis is used for analysis of the nature of chemical bonds and interactions. To understand bond strengths, hyperconjugation, and reactivity natural bond orbital analysis is performed.

On the representation and computational aspects of the distribution of a linear combination of independent noncentral chi-squared random variables

Thiss article presents two new results relevant to a linear combination of χ2 random variables. The first result expresses the cumulative distribution function as a simple multiple of a certain tilted density. The second result shows that in important cases the inversion integral for the density may be expressed as a sum of relatively simple real integrals which, using suitable numerical methods, are straightforward to compute quickly and accurately.

Continuous integration of heading and goal directions guides steering.

Navigating animals must integrate a diverse array of sensory cues into a single locomotor decision. Insects perform intricate navigational feats using a brain region termed the central complex in which an animal's heading direction is transformed through several layers of circuitry to elicit goal-directed locomotion. These transformations occur mostly in the fan-shaped body (FB), a major locus of multi-sensory integration in the central complex. Key aspects of these sensorimotor computations have been extensively characterized by functional studies, leveraging the genetic tools available in the fruit fly. However, our understanding of how neuronal activity in the FB dictates locomotor behaviors during navigation remains enigmatic. Here, we manipulate the activity of two key neuronal populations that input into the FB-the PFN a and PFN d neurons-used to encode the direction of two complex navigational cues: wind plumes and optic flow, respectively. We find that flies presented with unidirectional optic flow steer along curved walking trajectories, but silencing PFN d neurons abolishes this curvature. We next use optogenetic activation to introduce a fictive heading signal in the PFNs to establish the causal relationship between their activity and steering behavior. Our studies reveal that the central complex guides locomotion by summing the PFN-borne directional signals and shifting movement trajectories left or right accordingly. Based on these results, we propose a model of central complex-mediated locomotion wherein the fly achieves fine-grained control of sensory-guided steering by continuously integrating its heading and goal directions over time.

Computing the Commonalities of Clusters in Resource Description Framework: Computational Aspects

Clustering is a very common means of analysis of the data present in large datasets, with the aims of understanding and summarizing the data and discovering similarities, among other goals. However, despite the present success of the use of subsymbolic methods for data clustering, a description of the obtained clusters cannot rely on the intricacies of the subsymbolic processing. For clusters of data expressed in a Resource Description Framework (RDF), we extend and implement an optimized, previously proposed, logic-based methodology that computes an structure—called a Common Subsumer—describing the commonalities among all resources. We tested our implementation with two open, and very different, datasets: one devoted to public procurement, and the other devoted to drugs in pharmacology. For both datasets, we were able to provide reasonably concise and readable descriptions of clusters with up to 1800 resources. Our analysis shows the viability of our methodology and computation, and paves the way for general cluster explanations to be provided to lay users.

Risk measures based on weak optimal transport

In this paper, we study convex risk measures with weak optimal transport penalties. In a first step, we show that these risk measures allow for an explicit representation via a nonlinear transform of the loss function. In a second step, we discuss computational aspects related to the nonlinear transform as well as approximations of the risk measures using, for example, neural networks. Our setup comprises a variety of examples, such as classical optimal transport penalties, parametric families of models, divergence risk measures, uncertainty on path spaces, moment constraints, and martingale constraints. In a last step, we show how to use the theoretical results for the numerical computation of worst-case losses in an insurance context and no-arbitrage prices of European contingent claims after quoted maturities in a model-free setting.

Backtest overfitting in the machine learning era: A comparison of out-of-sample testing methods in a synthetic controlled environment

We present a comprehensive framework to assess these methods, considering the unique characteristics of financial data like non-stationarity, autocorrelation, and regime shifts. Through our analysis, we unveil the marked superiority of the Combinatorial Purged (CPCV) method in mitigating overfitting risks, outperforming traditional methods as evidenced by its lower Probability of Backtest Overfitting (PBO) and superior Deflated Sharpe Ratio (DSR) test statistic. Walk-Forward, by contrast, exhibits notable shortcomings in false discovery prevention, characterized by increased temporal variability and weaker stationarity. This contrasts with CPCV’s demonstrable stability and efficiency. We introduce novel variants of CPCV, including Bagged CPCV and Adaptive CPCV, which enhance robustness through ensemble approaches and dynamic adjustments based on market conditions. Our empirical validation using historical SP 500 data confirms these advanced cross-validation methods’ practical applicability and resilience. The analysis also suggests that choosing between Purged K-Fold and K-Fold necessitates caution due to their comparable performance and potential impact on the robustness of training data in out-of-sample testing. Our investigation utilizes a Synthetic Controlled Environment incorporating advanced models like the Heston Stochastic Volatility, Merton Jump Diffusion, and Drift-Burst Hypothesis alongside regime-switching models. This approach provides a nuanced simulation of market conditions, offering new insights into evaluating cross-validation techniques. We also address the computational aspects of these methods, demonstrating that parallelization significantly improves efficiency, making them feasible for large-scale financial datasets. Our study underscores the necessity of specialized validation methods in financial modeling, especially in the face of growing regulatory demands and complex market dynamics.

Computationally efficient deep learning models for diabetic retinopathy detection: a systematic literature review

Diabetic retinopathy, often resulting from conditions like diabetes and hypertension, is a leading cause of blindness globally. With diabetes affecting millions worldwide and anticipated to rise significantly, early detection becomes paramount. The survey scrutinizes existing literature, revealing a noticeable absence of consideration for computational complexity aspects in deep learning models. Notably, most researchers concentrate on employing deep learning models, and there is a lack of comprehensive surveys on the role of vision transformers in enhancing the efficiency of these models for DR detection. This study stands out by presenting a systematic review, exclusively considering 84 papers published in reputable academic journals to ensure a focus on mature research. The distinctive feature of this Systematic Literature Review (SLR) lies in its thorough investigation of computationally efficient approaches and models for DR detection. It sheds light on the incorporation of vision transformers into deep learning models, highlighting their significant contribution to improving accuracy. Moreover, the research outlines clear objectives related to the identified problem, giving rise to specific research questions. Following an assessment of relevant literature, data is extracted from digital archives. Additionally, in light of the results obtained from this SLR, a taxonomy for the detection of diabetic retinopathy has been presented. The study also highlights key research challenges and proposes potential avenues for further investigation in the field of detecting diabetic retinopathy.

Philological Studies: Computer Aspects of Stylometry Automation

Philological Studies: Computer Aspects of Stylometry Automation

A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability

AbstractGraph neural networks (GNNs) have made rapid developments in the recent years. Due to their great ability in modeling graph-structured data, GNNs are vastly used in various applications, including high-stakes scenarios such as financial analysis, traffic predictions, and drug discovery. Despite their great potential in benefiting humans in the real world, recent study shows that GNNs can leak private information, are vulnerable to adversarial attacks, can inherit and magnify societal bias from training data and lack interpretability, which have risk of causing unintentional harm to the users and society. For example, existing works demonstrate that attackers can fool the GNNs to give the outcome they desire with unnoticeable perturbation on training graph. GNNs trained on social networks may embed the discrimination in their decision process, strengthening the undesirable societal bias. Consequently, trust-worthy GNNs in various aspects are emerging to prevent the harm from GNN models and increase the users’ trust in GNNs. In this paper, we give a comprehensive survey of GNNs in the computational aspects of privacy, robustness, fairness, and explainability. For each aspect, we give the taxonomy of the related methods and formulate the general frameworks for the multiple categories of trustworthy GNNs. We also discuss the future research directions of each aspect and connections between these aspects to help achieve trustworthiness.

Developing novel wormhole metrics in finsler-randers geometry using the barthel connection and osculating-riemannian method

In this research, we focus on modelling a wormhole using Finsler-Randers geometry. By applying the Barthel connection from the range of Finslerian connections, we have developed a new wormhole metric, termed the osculating Barthel-Finsler-Randers metric, through the osculating-Riemannian method within the Finsler-Randers framework. The fundamental field equations are formulated in terms of the obtained metric tensor characterised by η(r), which plays a pivotal role in characterizing the anisotropy of the model. To explore the application of Finsler geometry in addressing the existence of a wormhole, we adopt an exponential shape function. Further, in order to streamline the computational aspects of our analysis, and to discuss the effect of anisotropy effect of Finsler Rander geometry, we adopt a specific functional form for the anisotropic parameter η(r)=arn, where α is a constant with implications for the anisotropy, among various possibilities for η(r) a non-zero analytical component of vector field in Randers metric. In addition, we consider three distinct values for the exponent n = −1, 0, and 1 and subsequently, we proceed to develop two distinct models for each of these three values of n with constant and non-constant redshift function. We analyse the physical implications of η(r) on the energy conditions. Our results demonstrate the crucial impact of anisotropy on the stability and structure of wormholes, emphasizing the requirement of exotic matter under specific conditions. This research deepens the understanding of wormhole physics within Finsler geometry, shedding light on the theoretical frameworks essential for the existence and stability of these cosmic structures.

Deep SVBRDF Acquisition and Modelling: A Survey

AbstractHand in hand with the rapid development of machine learning, deep learning and generative AI algorithms and architectures, the graphics community has seen a remarkable evolution of novel techniques for material and appearance capture. Typically, these machine‐learning‐driven methods and technologies, in contrast to traditional techniques, rely on only a single or very few input images, while enabling the recovery of detailed, high‐quality measurements of bi‐directional reflectance distribution functions, as well as the corresponding spatially varying material properties, also known as Spatially Varying Bi‐directional Reflectance Distribution Functions (SVBRDFs). Learning‐based approaches for appearance capture will play a key role in the development of new technologies that will exhibit a significant impact on virtually all domains of graphics. Therefore, to facilitate future research, this State‐of‐the‐Art Report (STAR) presents an in‐depth overview of the state‐of‐the‐art in machine‐learning‐driven material capture in general, and focuses on SVBRDF acquisition in particular, due to its importance in accurately modelling complex light interaction properties of real‐world materials. The overview includes a categorization of current methods along with a summary of each technique, an evaluation of their functionalities, their complexity in terms of acquisition requirements, computational aspects and usability constraints. The STAR is concluded by looking forward and summarizing open challenges in research and development toward predictive and general appearance capture in this field. A complete list of the methods and papers reviewed in this survey is available at computergraphics.on.liu.se/star_svbrdf_dl/.

Experimental and computational aspects of molecular frustrated Lewis pairs for CO2 hydrogenation: en route for heterogeneous systems?

Catalysis plays a crucial role in advancing sustainability. The unique reactivity of frustrated Lewis pairs (FLPs) is driving an ever-growing interest in the transition metal-free transformation of small molecules like CO2 into valuable products. In this area, there is a recent growing incentive to heterogenize molecular FLPs into porous solids, merging the benefits of homogeneous and heterogeneous catalysis - high activity, selectivity, and recyclability. Despite the progress, challenges remain in preventing deactivation, poisoning, and simplifying catalyst-product separation. This review explores the expanding field of FLPs in catalysis, covering existing molecular FLPs for CO2 hydrogenation and recent efforts to design heterogeneous porous systems from both experimental and theoretical perspectives. Section 2 discusses experimental examples of CO2 hydrogenation by molecular FLPs, starting with stoichiometric reactions and advancing to catalytic ones. It then examines attempts to immobilize FLPs in porous matrices, including siliceous solids, metal-organic frameworks (MOFs), covalent organic frameworks, and disordered polymers, highlighting current limitations and challenges. Section 3 then reviews computational studies on the mechanistic details of CO2 hydrogenation, focusing on H2 splitting and hydride/proton transfer steps, summarizing efforts to establish structure-activity relationships. It also covers the computational aspects on grafting FLPs inside MOFs. Finally, Section 4 summarizes the main design principles established so far, while addressing the complexities of translating computational approaches into the experimental realm, particularly in heterogeneous systems. This section underscores the need to strengthen the dialogue between theoretical and experimental approaches in this field.

An Improved Penalty-Based Excited-State Variational Monte Carlo Approach with Deep-Learning Ansatzes.

We introduce several improvements to the penalty-based variational quantum Monte Carlo (VMC) algorithm for computing electronic excited states of Entwistle et al. [Nat. Commun. 14, 274 (2023)] and demonstrate that the accuracy of the updated method is competitive with other available excited-state VMC approaches. A theoretical comparison of the computational aspects of these algorithms is presented, where several benefits of the penalty-based method are identified. Our main contributions include an automatic mechanism for tuning the scale of the penalty terms, an updated form of the overlap penalty with proven convergence properties, and a new term that penalizes the spin of the wave function, enabling the selective computation of states with a given spin. With these improvements, along with the use of the latest self-attention-based ansatz, the penalty-based method achieves a mean absolute error below 1 kcal/mol for the vertical excitation energies of a set of 26 atoms and molecules, without relying on variance matching schemes. Considering excited states along the dissociation of the carbon dimer, the accuracy of the penalty-based method is on par with that of natural-excited-state (NES) VMC, while also providing results for additional sections of the potential energy surface, which were inaccessible with the NES method. Additionally, the accuracy of the penalty-based method is improved for a conical intersection of ethylene, with the predicted angle of the intersection agreeing well with both NES-VMC and multireference configuration interaction.

Deformation twinning as a displacive transformation: computational aspects of the phase-field model coupled with crystal plasticity

AbstractSpatially-resolved modeling of deformation twinning and its interaction with plastic slip is achieved by coupling the phase-field method and crystal plasticity theory. The intricate constitutive relations arising from this coupling render the resulting computational model prone to inefficiencies and lack of robustness. Accordingly, together with the inherent limitations of the phase-field method, these factors may impede the broad applicability of the model. In this paper, our recent phase-field model of coupled twinning and crystal plasticity is the subject of study. We delve into the incremental formulation and computational treatment of the model and run a thorough investigation into its computational performance. We focus specifically on evaluating the efficiency of the finite-element discretization employing various element types, and we examine the impact of mesh density. Since the micromorphic regularization is an important part of the finite-element implementation, the effect of the micromorphic regularization parameter is also studied.

Predictive digital twin driven trust model for cloud service providers with Fuzzy inferred trust score calculation

Cloud computing has become integral to modern computing infrastructure, offering scalability, flexibility, and cost-effectiveness. Trust is a critical aspect of cloud computing, influencing user decisions in selecting Cloud Service Providers (CSPs). This paper provides a comprehensive review of existing trust models in cloud computing, including agreement-based, SLA-based, certificate-based, feedback-based, domain-based, prediction-based, and reputation-based models. Building on this foundation, we propose a novel methodology for creating a trust model in cloud computing using digital twins for CSPs. The digital twin is augmented with a fuzzy inference system, which computes the trust score of a CSP based on trust-related parameters. The architecture of the digital twin with the fuzzy inference system is detailed, outlining how it processes security parameter values obtained through penetration testing mechanisms. These parameter values are transformed into crisp values using a linear ridge regression function and then fed into the fuzzy inference system to calculate a final trust score for the CSP. The paper also presents the outputs of the fuzzy inference system, demonstrating how different security parameter inputs yield various trust scores. This methodology provides a robust framework for assessing CSP trustworthiness and enhancing decision-making processes in cloud service selection.

Tensor-Train Format Hierarchical Equations of Motion Formalism: Charge Transfer in Organic Semiconductors via Dissipative Holstein Models.

Hierarchical Equations of Motion (HEOM) in the Tensor-Train (TT) representation is applied to study the charge-transfer dynamics in organic semiconductors (OSCs). The theoretical formulation as well as the basic computational aspects of HEOM-TT are discussed in detail. Charge transfer in OSCs is modeled using dissipative polaronic models that incorporate the effects of both high- and low-frequency molecular vibrations, and it is simulated in a fully quantum and nonperturbative manner, which has not been studied intensively. The capability of treating complex electron-vibrational systems is examined by analyzing and comparing the numerical behavior of the time-dependent variational approach and the time-Alternating Minimal Energy methods and by calculating the current autocorrelation function and diffusivity across various models. Our results indicate that the HEOM-TT framework offers a robust tool for the detailed analysis of complex polaronic systems, suggesting its potential for broader applications.