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Journal articles on the topic 'High computational cost'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Karim, Rezaul, and Amit Kumar Kundu. "Computational Analysis to Reduce Classification Cost Keeping High Accuracy of the Sparser LPSVM." International Journal of Machine Learning and Computing 9, no. 6 (2019): 728–33. http://dx.doi.org/10.18178/ijmlc.2019.9.6.865.

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Martina Perez, Simon, Heba Sailem, and Ruth E. Baker. "Efficient Bayesian inference for mechanistic modelling with high-throughput data." PLOS Computational Biology 18, no. 6 (2022): e1010191. http://dx.doi.org/10.1371/journal.pcbi.1010191.

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Bayesian methods are routinely used to combine experimental data with detailed mathematical models to obtain insights into physical phenomena. However, the computational cost of Bayesian computation with detailed models has been a notorious problem. Moreover, while high-throughput data presents opportunities to calibrate sophisticated models, comparing large amounts of data with model simulations quickly becomes computationally prohibitive. Inspired by the method of Stochastic Gradient Descent, we propose a minibatch approach to approximate Bayesian computation. Through a case study of a high-

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Kwon, Dongup, Wonsik Lee, Dongryeong Kim, Junehyuk Boo, and Jangwoo Kim. "SmartFVM: A Fast, Flexible, and Scalable Hardware-based Virtualization for Commodity Storage Devices." ACM Transactions on Storage 18, no. 2 (2022): 1–27. http://dx.doi.org/10.1145/3511213.

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A computational storage device incorporating a computation unit inside or near its storage unit is a highly promising technology to maximize a storage server’s performance. However, to apply such computational storage devices and take their full potential in virtualized environments, server architects must resolve a fundamental challenge: cost-effective virtualization . This critical challenge can be directly addressed by the following questions: (1) how to virtualize two different hardware units (i.e., computation and storage), and (2) how to integrate them to construct virtual computational

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Wunschmann, Jurgen, Sebastian Zanker, Christian Gunter, and Albrecht Rothermel. "Reduction of computational cost for high quality video scaling." IEEE Transactions on Consumer Electronics 56, no. 4 (2010): 2584–91. http://dx.doi.org/10.1109/tce.2010.5681144.

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Fukushima, Kunihiko. "Efficient IntVec: High recognition rate with reduced computational cost." Neural Networks 119 (November 2019): 323–31. http://dx.doi.org/10.1016/j.neunet.2019.08.024.

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Carson, Erin, and Zdeněk Strakoš. "On the cost of iterative computations." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2166 (2020): 20190050. http://dx.doi.org/10.1098/rsta.2019.0050.

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With exascale-level computation on the horizon, the art of predicting the cost of computations has acquired a renewed focus. This task is especially challenging in the case of iterative methods, for which convergence behaviour often cannot be determined with certainty a priori (unless we are satisfied with potentially outrageous overestimates) and which typically suffer from performance bottlenecks at scale due to synchronization cost. Moreover, the amplification of rounding errors can substantially affect the practical performance, in particular for methods with short recurrences. In this art

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AyguadÉ, Eduard, Jordi Garcia, MercÉ GironÈs, M. Luz Grande, and JesÚs Labarta. "DDT: A Research Tool for Automatic Data Distribution in High Performance Fortran." Scientific Programming 6, no. 1 (1997): 73–94. http://dx.doi.org/10.1155/1997/780152.

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This article describes the main features and implementation of our automatic data distribution research tool. The tool (DDT) accepts programs written in Fortran 77 and generates High Performance Fortran (HPF) directives to map arrays onto the memories of the processors and parallelize loops, and executable statements to remap these arrays. DDT works by identifying a set of computational phases (procedures and loops). The algorithm builds a search space of candidate solutions for these phases which is explored looking for the combination that minimizes the overall cost; this cost includes data

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Mahdi, Naghibi, Anvari Reza, Forghani Ali, and Minaei Behrouz. "Test-cost-sensitive Convolutional Neural Networks with Expert Branches." Signal & Image Processing: An International Journal (SIPIJ) 10, no. 5 (2019): 15–27. https://doi.org/10.5281/zenodo.3541564.

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It has been proven that deeper convolutional neural networks (CNN) can result in better accuracy in many problems, but this accuracy comes with a high computational cost. Also, input instances have not the same difficulty. As a solution for accuracy vs. computational cost dilemma, we introduce a new test-cost-sensitive method for convolutional neural networks. This method trains a CNN with a set of auxiliary outputs and expert branches in some middle layers of the network. The expert branches decide to use a shallower part of the network or going deeper to the end, based on the difficulty of i

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Valkov, Ivan, Phil Trinder, and Natalia Chechina. "Reliable distribution of computational load in robot teams." Autonomous Robots 45, no. 3 (2021): 351–69. http://dx.doi.org/10.1007/s10514-021-09967-8.

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AbstractModern multi-robot systems often need to solve computationally intensive tasks but operate with limited compute resources and in the presence of failures. Cooperating to share computational tasks between robots at the edge reduces execution time. We introduce and evaluate a new computation load management technology for teams of robots: Reliable Autonomous Mobile Programs (RAMPs). RAMPs use information about the computational resources available in the team and a cost model to decide where to execute. RAMPs are implemented in ROS on a collection of Raspberry Pi-based robots. The perfor

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Noh, Won-Jun, Ki-Ryum Moon, and Byoung-Dai Lee. "SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation." Applied Sciences 14, no. 22 (2024): 10143. http://dx.doi.org/10.3390/app142210143.

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Human pose estimation identifies and classifies key joints of the human body in images or videos. Existing pose estimation methods can precisely capture human movements in real time but require significant computational time and resources, which restricts their usage in specific conditions. Thus, we propose a lightweight pose estimation model—SMS-Net—based on the sequentially stacked structure of the hourglass network. The proposed model uses various lightweight techniques to enable high-speed pose estimation while requiring minimal storage space and computation. Specifically, a shuffle-gated

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Wedi, Nils P., Mats Hamrud, and George Mozdzynski. "A Fast Spherical Harmonics Transform for Global NWP and Climate Models." Monthly Weather Review 141, no. 10 (2013): 3450–61. http://dx.doi.org/10.1175/mwr-d-13-00016.1.

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Abstract Very high-resolution spectral transform models are believed to become prohibitively expensive because of the relative increase in computational cost of the Legendre transforms compared to the gridpoint computations. This article describes the implementation of a practical fast spherical harmonics transform into the Integrated Forecast System (IFS) at ECMWF. Details of the accuracy of the computations, of the parallelization, and memory use are discussed. Results are presented that demonstrate the cost effectiveness and accuracy of the fast spherical harmonics transform, successfully m

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Tikhonov, Maksim Konstantinovich. "Integrating federated learning and YOLOv11 for object detection in autonomous vehicles." Research Result. Information technologies 9, no. 4 (2024): 58–64. https://doi.org/10.18413/2518-1092-2024-9-4-0-7.

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Autonomous vehicles (AVs) require high-performance systems for environmental perception, especially for accurate object detection. Systems based on deep neural networks perform well, but often face challenges due to the high computational cost and the need for centralised data collection. This paper proposes the integration of federated learning with the YOLOv11 model to create more efficient and scalable ATS solutions. The FLYolo11 model is presented, which optimises object detection in computationally constrained environments, improving accuracy and performance without the need for centralis

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Chiaradia, Tiago Raimundo, Gabriel Ferraz Marcondes de Carvalho, Alex Mendonça Bimbato, and Luiz Antonio Alcântara Pereira. "A Contribution to the Temperature Particles Method—Implementation of a Large-Eddy Simulation (LES) Model for the Temperature Field." Applied Sciences 15, no. 8 (2025): 4122. https://doi.org/10.3390/app15084122.

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This paper introduces a numerical methodology for the investigation of two-dimensional, incompressible and unsteady flows. The analyses involve Fluid–Structure Interaction (FSI) over solid boundaries of known shape with effects of mixed convection heat transfer. The main contribution is the implementation of a Large-Eddy Simulation (LES) model for the energy equation. LES is a mathematical model for simulating turbulent flows. The Boussinesq approximation links the vorticity transport equation with the energy equation to include buoyancy forces. The methodology consists of discretizing the vor

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Zheng, Jun, Hao Bo Qiu, and Xiao Lin Zhang. "Variable-Fidelity Multidisciplinary Design Optimization Based on Analytical Target Cascading Framework." Advanced Materials Research 544 (June 2012): 49–54. http://dx.doi.org/10.4028/www.scientific.net/amr.544.49.

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ATC provides a systematic approach in solving decomposed large scale systems that has solvable subsystems. However， complex engineering system usually has a high computational cost , which result in limiting real-life applications of ATC based on high-fidelity simulation models. To address these problems, this paper aims to develop an efficient approximation model building techniques under the analytical target cascading (ATC) framework, to reduce computational cost associated with multidisciplinary design optimization problems based on high-fidelity simulations. An approximation model buildin

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Chandler-wilde, Simon, Stephen Langdon, and Oliver Phillips. "Towards high frequency boundary element methods for multiple scattering." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 2 (2023): 5319–25. http://dx.doi.org/10.3397/in_2022_0775.

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Standard Boundary Element Methods (BEM) for time-harmonic acoustics, using piecewise polynomial finite-element type approximation spaces, have a computational cost that grows rapidly with frequency, to ensure at least a fixed number of degrees of freedom per wavelength. Hybrid Numerical-Asymptotic (HNA) BEMs, based on enriched approximation spaces consisting of the products of piecewise polynomials with carefully chosen oscillatory functions, have a computational cost that is almost frequency-independent for some problem classes (e.g. Chandler-Wilde, Graham, Langdon, Spence, Acta Numerica 2012

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Sharma, Prasen Kumar, Sujoy Ghosh, and Arijit Sur. "High-quality Frame Recurrent Video De-raining with Multi-contextual Adversarial Network." ACM Transactions on Multimedia Computing, Communications, and Applications 17, no. 2 (2021): 1–24. http://dx.doi.org/10.1145/3444974.

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In this article, we address the problem of rain-streak removal in the videos. Unlike the image, challenges in video restoration comprise temporal consistency besides spatial enhancement. The researchers across the world have proposed several effective methods for estimating the de-noised videos with outstanding temporal consistency. However, such methods also amplify the computational cost due to their larger size. By way of analysis, incorporating separate modules for spatial and temporal enhancement may require more computational resources. It motivates us to propose a unified architecture t

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Wu, Linfeng, Huajun Wang, and Huiqing Wang. "A Lightweight Conditional Convolutional Neural Network for Hyperspectral Image Classification." Photogrammetric Engineering & Remote Sensing 89, no. 7 (2023): 413–23. http://dx.doi.org/10.14358/pers.22-00130r2.

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Deep learning (dl), especially convolutional neural networks (cnns), has been proven to be an excellent feature extractor and widely applied to hyperspectral image (hsi) classification. However, dl is a computationally demanding algorithm with many parameters and a high computational burden, which seriously restricts the deployment of dl-based hsi classification algorithms on mobile and embedded systems. In this paper, we propose an extremely lightweight conditional three-dimensional (3D) hsi with a double-branch structure to solve these problems. Specifically, we introduce a lightweight condi

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Don, Wai-Sun, Antonio de Gregorio, Jean-Piero Suarez, and Gustaaf B. Jacobs. "Assessing the Performance of a Three Dimensional Hybrid Central-WENO Finite Difference scheme with Computation of a Sonic Injector in Supersonic Cross Flow." Advances in Applied Mathematics and Mechanics 4, no. 06 (2012): 719–36. http://dx.doi.org/10.4208/aamm.12-12s03.

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AbstractA hybridization of a high order WENO-Zfinite difference scheme and a high order central finite difference method for computation of the two-dimensional Euler equations first presented in [B. Costa and W. S. Don, J. Comput. Appl. Math., 204(2) (2007)] is extended to three-dimensions and for parallel computation. The Hybrid scheme switches dynamically from a WENO-Zscheme to a central scheme at any grid location and time instance if the flow is sufficiently smooth and vice versa if the flow is exhibiting sharp shock-type phenomena. The smoothness of the flow is determined by a high order

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Jasa, John, Pietro Bortolotti, Daniel Zalkind, and Garrett Barter. "Effectively using multifidelity optimization for wind turbine design." Wind Energy Science 7, no. 3 (2022): 991–1006. http://dx.doi.org/10.5194/wes-7-991-2022.

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Abstract. Wind turbines are complex multidisciplinary systems that are challenging to design because of the tightly coupled interactions between different subsystems. Computational modeling attempts to resolve these couplings so we can efficiently explore new wind turbine systems early in the design process. Low-fidelity models are computationally efficient but make assumptions and simplifications that limit the accuracy of design studies, whereas high-fidelity models capture more of the actual physics but with increased computational cost. This paper details the use of multifidelity methods f

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Dongarra, Jack, Laura Grigori, and Nicholas J. Higham. "Numerical algorithms for high-performance computational science." Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences 378, no. 2166 (2020): 20190066. http://dx.doi.org/10.1098/rsta.2019.0066.

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A number of features of today’s high-performance computers make it challenging to exploit these machines fully for computational science. These include increasing core counts but stagnant clock frequencies; the high cost of data movement; use of accelerators (GPUs, FPGAs, coprocessors), making architectures increasingly heterogeneous; and multi- ple precisions of floating-point arithmetic, including half-precision. Moreover, as well as maximizing speed and accuracy, minimizing energy consumption is an important criterion. New generations of algorithms are needed to tackle these challenges. We

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Fernandes, Flaviano Williams. "HICOLM: High-Performance Platform of Physical Simulations by Using Low Computational Cost Methods." Revista de Informática Teórica e Aplicada 26, no. 3 (2019): 90–103. http://dx.doi.org/10.22456/2175-2745.92486.

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For decades, computational simulation models have been used by scientists in search for new materials with technological applications in several areas of knowledge. For this, software based on several theoretical-computational models were developed in order to obtain an analysis of the physical properties at atomic levels. The objective of this work is proposing a widely functional software to analyze the physical properties of nanostructures based on carbon and condensed systems using theories of low computational cost. Therefore, a Fortran language computational program called HICOLM was dev

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Sun, S., S. T. Khu, Z. Kapelan, and S. Djordjević. "A fast approach for multiobjective design of water distribution networks under demand uncertainty." Journal of Hydroinformatics 13, no. 2 (2010): 143–52. http://dx.doi.org/10.2166/hydro.2010.033.

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Water distribution system (WDS) design has received much attention lately from the point of view of uncertainties. Designers are generally interested in the Pareto optimal cost-robustness trade off curve. This paper aims to find a solution to the multiobjective problem in a computationally time-efficient way in comparison to previous methods from the literature. A parameter θ, which is linked to the system robustness through a derived analytic formula, is introduced. The robustness of the WDS can be approximated by one single model simulation; consequently a large amount of computational time

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Jamroz, Benjamin F., and Robert Klöfkorn. "Asynchronous communication in spectral-element and discontinuous Galerkin methods for atmospheric dynamics – a case study using the High-Order Methods Modeling Environment (HOMME-homme_dg_branch)." Geoscientific Model Development 9, no. 8 (2016): 2881–92. http://dx.doi.org/10.5194/gmd-9-2881-2016.

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Abstract. The scalability of computational applications on current and next-generation supercomputers is increasingly limited by the cost of inter-process communication. We implement non-blocking asynchronous communication in the High-Order Methods Modeling Environment for the time integration of the hydrostatic fluid equations using both the spectral-element and discontinuous Galerkin methods. This allows the overlap of computation with communication, effectively hiding some of the costs of communication. A novel detail about our approach is that it provides some data movement to be performed

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SAGHEER, ALAA, NAYOUKI TSURUTA, and RIN-ICHIRO TANIGUCHI. "P1DSOM — A FAST SEARCH ALGORITHM FOR HIGH-DIMENSIONAL FEATURE SPACE PROBLEMS." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 02 (2014): 1459005. http://dx.doi.org/10.1142/s0218001414590058.

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The self-organizing map (SOM) is a traditional neural network algorithm used to achieve feature extraction, clustering, visualization and data exploration. However, it is known that the computational cost of the traditional SOM, used to search for the winner neuron, is expensive especially in case of treating high-dimensional data. In this paper, we propose a novel hierarchical SOM search algorithm which significantly reduces the expensive computational cost associated with traditional SOM. It is shown here that the computational cost of the proposed approach, compared to traditional SOM, to s

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Mahmmod, Basheera M., Alaa M. Abdul-Hadi, Sadiq H. Abdulhussain, and Aseel Hussien. "On Computational Aspects of Krawtchouk Polynomials for High Orders." Journal of Imaging 6, no. 8 (2020): 81. http://dx.doi.org/10.3390/jimaging6080081.

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Discrete Krawtchouk polynomials are widely utilized in different fields for their remarkable characteristics, specifically, the localization property. Discrete orthogonal moments are utilized as a feature descriptor for images and video frames in computer vision applications. In this paper, we present a new method for computing discrete Krawtchouk polynomial coefficients swiftly and efficiently. The presented method proposes a new initial value that does not tend to be zero as the polynomial size increases. In addition, a combination of the existing recurrence relations is presented which are

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Narander, Kumar, Ranjan Parth, and Kumar Surendra. "Efficient Cost Optimization using Transportation Mechanisms for Cloud Computing Environment." International Journal of Engineering and Advanced Technology (IJEAT) 9, no. 4 (2020): 1630–39. https://doi.org/10.35940/ijeat.D7812.049420.

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Efficient computations are increasing now a day, so their need is very high in the world. Infrastructure and computation techniques are not as much as efficient in conventionally or in present scenario, therefore the cloud computing is new to deal this type of problems. Sequencing of hardware and software technologies, for giving scalable and low cost computational understandings in cloud computing. The major focus of this research is to diminish the transportation cost of resource allocation along with various virtual machines in cloud computing environment. In this research paper, implementa

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Garcia, Luís P. F., Adriano Rivolli, Edesio Alcoba, Ana C. Lorena, and André C. P. L. F. de Carvalho. "Boosting meta-learning with simulated data complexity measures." Intelligent Data Analysis 24, no. 5 (2020): 1011–28. http://dx.doi.org/10.3233/ida-194803.

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Meta-Learning has been largely used over the last years to support the recommendation of the most suitable machine learning algorithm(s) and hyperparameters for new datasets. Traditionally, a meta-base is created containing meta-features extracted from several datasets along with the performance of a pool of machine learning algorithms when applied to these datasets. The meta-features must describe essential aspects of the dataset and distinguish different problems and solutions. However, if one wants the use of Meta-Learning to be computationally efficient, the extraction of the meta-feature

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D, Dr Girish Kumar. "High Performance Mining of Covid-19." International Journal for Research in Applied Science and Engineering Technology 10, no. 7 (2022): 3137–41. http://dx.doi.org/10.22214/ijraset.2022.45702.

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Abstract: The COVID-19 global pandemic is an unprecedented health crisis. Many researchers around the world have produced an extensive collection of literature since the outbreak. Analysing this information to extract knowledge and provide meaningful insights in a timely manner requires a considerable amount of computational power. Cloud platforms are designed to provide this computational power in an on-demand and elastic manner. Specifically, hybrid clouds, composed of private and public data centers, are particularly well suited to deploy computationally intensive workloads in a cost-effici

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Ullah, Insaf, Ali Alkhalifah, Muhammad Asghar Khan, and Samih M. Mostafa. "Cost-Effective Proxy Signcryption Scheme for Internet of Things." Mobile Information Systems 2021 (November 30, 2021): 1–10. http://dx.doi.org/10.1155/2021/2427434.

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The Internet of things (IoT) has emerged into a revolutionary technology that enables a wide range of features and applications given the proliferation of sensors and actuators embedded in everyday objects, as well as the ubiquitous availability of high-speed Internet. When nearly everything is connected to the Internet, security and privacy concerns will become more significant. Furthermore, owing to the resource-constrained nature of IoT devices, they are unable to perform standard cryptographic computations. As a result, there is a critical need for efficient and secure lightweight cryptogr

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Kataoka, Shotaro, Tetsuro Ito, Genki Iwaka, Masashi Oba, and Hirofumi Nonaka. "Improving work detection by segmentation heuristics pre-training on factory operations video." PLOS ONE 17, no. 6 (2022): e0267457. http://dx.doi.org/10.1371/journal.pone.0267457.

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The measurement of work time for individual tasks by using video has made a significant contribution to a framework for productivity improvement such as value stream mapping (VSM). In the past, the work time has been often measured manually, but this process is quite costly and labor-intensive. For these reasons, automation of work analysis at the worksite is needed. There are two main methods for computing spatio-temporal information: by 3D-CNN, and by temporal computation using LSTM after feature extraction in the spatial domain by 2D-CNN. These methods has high computational cost but high m

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Ku, Yu-Te, Feng-Hao Liu, Chih-Fan Hsu, et al. "Optimizing Encrypted Neural Networks: Model Design, Quantization and Fine-Tuning Using FHEW/TFHE." Proceedings on Privacy Enhancing Technologies 2025, no. 4 (2025): 1075–91. https://doi.org/10.56553/popets-2025-0172.

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Third-generation Fully Homomorphic Encryption (FHE), particularly the FHEW/TFHE schemes, is recognized for its balanced security requirements, small parameters, and low memory usage, though the current methods in the scenarios of Deep Neural Network (DNN) inference still have high computational costs, limiting the practical applicability. This work demonstrates how to improve practicality of the third-generation technologies for DNN tasks while preserving its key advantages. Our work focuses on two main contributions. First, we developed a computational architecture called FHE-Neuron, which re

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Jakeman, John D., Mauro Perego, D. Thomas Seidl, et al. "An evaluation of multi-fidelity methods for quantifying uncertainty in projections of ice-sheet mass change." Earth System Dynamics 16, no. 2 (2025): 513–44. https://doi.org/10.5194/esd-16-513-2025.

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Abstract. This study investigated the computational benefits of using multi-fidelity statistical estimation (MFSE) algorithms to quantify uncertainty in the mass change of Humboldt Glacier, Greenland, between 2007 and 2100 using a single climate change scenario. The goal of this study was to determine whether MFSE can use multiple models of varying cost and accuracy to reduce the computational cost of estimating the mean and variance of the projected mass change of a glacier. The problem size and complexity were chosen to reflect the challenges posed by future continental-scale studies while s

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Zhang, Meng, and Kyosuke Yamamoto. "Optimizing Computational Process of High-Order Taylor Discontinuous Galerkin Method for Solving the Euler Equations." Applied Sciences 15, no. 7 (2025): 4047. https://doi.org/10.3390/app15074047.

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Solving the Euler equations often requires expensive computations of complex, high-order time derivatives. Although Taylor Discontinuous Galerkin (TDG) schemes are renowned for their accuracy and stability, directly evaluating third-order tensor derivatives can significantly reduce computational efficiency, particularly for large-scale, intricate flow problems. To overcome this difficulty, this paper presents an optimized numerical procedure that combines Taylor series time integration with the Discontinuous Galerkin (DG) approach. By replacing cumbersome tensor derivatives with simpler time d

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Meena, V., Obulaporam Gireesha, Kannan Krithivasan, and V. S. Shankar Sriram. "Fuzzy simplified swarm optimization for multisite computational offloading in mobile cloud computing." Journal of Intelligent & Fuzzy Systems 39, no. 6 (2020): 8285–97. http://dx.doi.org/10.3233/jifs-189148.

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Mobile Cloud Computing (MCC)’s rapid technological advancements facilitate various computational-intensive applications on smart mobile devices. However, such applications are constrained by limited processing power, energy consumption, and storage capacity of smart mobile devices. To mitigate these issues, computational offloading is found to be the one of the promising techniques as it offloads the execution of computation-intensive applications to cloud resources. In addition, various kinds of cloud services and resourceful servers are available to offload computationally intensive tasks. H

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Giladi, Ran, Ephraim Korach, and Rony Ohayon. "Path selection under multiple QoS constraints – a practical approach." Journal of High Speed Networks 13, no. 2 (2004): 155–67. https://doi.org/10.3233/hsn-2004-241.

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Path selection under multiple additive QoS constraints in high‐speed networks is an NP‐complete problem. Most of the algorithms proposed for QoS routing suffer from either excessive computational complexities or low performance. In this paper, we propose two practical heuristic algorithms that are based on iterative shortest‐path computations with a dynamic cost function. The cost function comprises a combination of several QoS constraints and a single optimization metric. A simulation program evaluates the performance of the algorithms. The simulation results indicate that our algorithms give

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Singh, Jitender. "Cost-Efficient Secure Hybrid RAG Assessment." Advances in Machine Learning & Artificial Intelligence 6, no. 1 (2025): 01–08. https://doi.org/10.33140/amlai.06.01.03.

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This paper presents a cost-effective and scalable hybrid methodology for evaluating retrieval-augmented generation (RAG) systems using specialized pretrained models and advanced metrics, designed for critical domains like healthcare and finance. LLM judge-based evaluation approaches are hindered by significant score inconsistencies across identical input runs (up to 35% variations) and high computational costs while traditional NLP approaches, overly reliant on entity or phrase matching, lack a multi-faceted perspective and fail to capture deeper semantic understanding. Our methodology address

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Zhang, Minghui. "A Zero-Cost Darts Base on Multi-Step Optimization." Frontiers in Computing and Intelligent Systems 5, no. 3 (2023): 33–35. http://dx.doi.org/10.54097/fcis.v5i3.13841.

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DARTS has achieved great result in Image classification field, the accuracy predictor and computation costs are the key of DNAS algorithm. Searching for a high-performance architecture always costs Large amount of computation. With a gradient-based bi-level optimization, DARTS using one-step optimization which makes the process available within a few GPU day, because of the one-step optimization , there exists a great gap between the architectures in search and evaluation. In this paper, we propose a zero-cost DARTS method which using multi-step optimization to address the above issues. To fur

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Gao, Yanbo, Hejia Pan, Yajuan Sheng, Rui Wen, Yuanhao Zheng, and Lin Yang. "A Low-Cost Computational Spectrometer Based on a Trained Sparse Base Matrix." Sensors 25, no. 3 (2025): 953. https://doi.org/10.3390/s25030953.

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Computational spectrometers based on coded measurement and computational reconstruction have great application prospects. This paper proposes a computational spectrometer that has a low cost, is easy to implement in hardware, and has high reconstruction accuracy. The proposed computational spectrometer uses low-cost but highly correlated polymethyl methacrylate (PMMA) material as broadband encoding filters, which could affect spectral reconstruction accuracy. To alleviate this issue, we decoupled the sensing matrix, which is the product of the measurement matrix and sparse base matrix, and sub

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Achal Vijay Navaloor, Kumudavalli M V, and Aliya Hassan. "Efficiency Enhancement Architecture of SQL Queries Through Hierarchical Cache." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 520–25. https://doi.org/10.47392/irjaem.2025.0082.

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Secure Multi-Party Computation (MPC) enables privacy-preserving collaborative data analysis but suffers from high computational overhead, communication latency, and inefficient query execution. Traditional caching mechanisms, such as SMPCache, attempt to optimize performance but face limitations due to high storage overhead, low cache hit rates, and inefficient cache lookup strategies. This study suggests a Hierarchical SMPCache (H-SMPCache) The approach to address these issues, inspired by CPU caching, introduces a multi-level caching structure (L1, L2, L3) to efficiently manage SQL queries i

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de Lima, Camila, and Elias Salomão Helou. "Fast projection/backprojection and incremental methods applied to synchrotron light tomographic reconstruction." Journal of Synchrotron Radiation 25, no. 1 (2018): 248–56. http://dx.doi.org/10.1107/s1600577517015715.

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Iterative methods for tomographic image reconstruction have the computational cost of each iteration dominated by the computation of the (back)projection operator, which take roughlyO(N3) floating point operations (flops) forN×Npixels images. Furthermore, classical iterative algorithms may take too many iterations in order to achieve acceptable images, thereby making the use of these techniques unpractical for high-resolution images. Techniques have been developed in the literature in order to reduce the computational cost of the (back)projection operator toO(N2logN) flops. Also, incremental a

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Fouladinejad, Nariman, Nima Fouladinejad, Mohamad Kasim Abdul Jalil, and Jamaludin Mohd Taib. "Decomposition-Assisted Computational Technique Based on Surrogate Modeling for Real-Time Simulations." Complexity 2017 (2017): 1–14. http://dx.doi.org/10.1155/2017/1686230.

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The development of complex simulation systems is extremely costly as it requires high computational capability and expensive hardware. As cost is one of the main issues in developing simulation components, achieving real-time simulation is challenging and it often leads to intensive computational burdens. Overcoming the computational burden in a multidisciplinary simulation system that has several subsystems is essential in producing inexpensive real-time simulation. In this paper, a surrogate-based computational framework was proposed to reduce the computational cost in a high-dimensional mod

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Liu, Yanli, Hongtao Wu, Yuxuan Yang, Shangyuan Zou, Xuexiang Zhang, and Yaoyao Wang. "Symmetrical Workspace of 6-UPS Parallel Robot Using Tilt and Torsion Angles." Mathematical Problems in Engineering 2018 (June 21, 2018): 1–10. http://dx.doi.org/10.1155/2018/6412030.

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For the fast and efficient closed-loop real-time feedback control of 6-UPS parallel robot (6-UPS), a novel high efficiency calculation of the workspace is proposed and investigated. As a typical Nearly General Platform (NGP), 6-UPS has good symmetries. The symmetries effectively reduce computational cost and improve computational efficiency in the kinematics, singularity, dynamics, and optimization. To scrupulously demonstrate the symmetries of workspace, a novel algorithm is proposed. The modified Euler angles (T&T angles) are employed to represent the orientation matrix of 6-UPS, the inv

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Yue, Min, Liangyu Li, Run Zou, Tiexiong Su, Nana Wang, and Huan Wen. "Optimization of Combustion Chamber Structure in a Rotary Engine Based on a Fuzzy Neural Network and a Genetic Algorithm." Processes 13, no. 1 (2025): 122. https://doi.org/10.3390/pr13010122.

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In order to solve the problems of the method involving the optimization of the traditional combustion chamber structure, which has a long computation cycle, high computation cost, and can easily fall into the local optimal solution, this paper refers to the concept of a fuzzy neural network in machine learning. This study proposes a method of combustion chamber structure optimization that uses a fuzzy neural network to prejudge the results of the fitness function before calculating it in order to reduce the periodicity of computation and improve computational accuracy. The validation results s

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Ju, Yaping, Geoff Parks, and Chuhua Zhang. "A bisection-sampling-based support vector regression–high-dimensional model representation metamodeling technique for high-dimensional problems." Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 231, no. 12 (2016): 2173–86. http://dx.doi.org/10.1177/0954406216629504.

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A major challenge of metamodeling in simulation-based engineering design optimization is to handle the “curse of dimensionality,” i.e. the exponential growth of computational cost with increase of problem dimensionality. Encouragingly, it has been reported recently that a high-dimensional model representation assisted by a radial basis function is capable of deriving high-dimensional input–output relationships at dramatically reduced computational cost. In this article, support vector regression is employed as an alternative to be coupled with high-dimensional model representation for the meta

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Löer, Katrin, Andrew Curtis, and Giovanni Angelo Meles. "Relating source-receiver interferometry to an inverse-scattering series to derive a new method to estimate internal multiples." GEOPHYSICS 81, no. 3 (2016): Q27—Q40. http://dx.doi.org/10.1190/geo2015-0330.1.

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We have evaluated an explicit relationship between the representations of internal multiples by source-receiver interferometry and an inverse-scattering series. This provides a new insight into the interaction of different terms in each of these internal multiple prediction equations and explains why amplitudes of estimated multiples are typically incorrect. A downside of the existing representations is that their computational cost is extremely high, which can be a precluding factor especially in 3D applications. Using our insight from source-receiver interferometry, we have developed an alte

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Fassois, S. D., K. F. Eman, and S. M. Wu. "A Suboptimum Maximum Likelihood Approach to Parametric Signal Analysis." Journal of Dynamic Systems, Measurement, and Control 111, no. 2 (1989): 153–59. http://dx.doi.org/10.1115/1.3153031.

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A computationally efficient approach to stochastic ARMA modeling of wide-sense stationary signals is proposed. The discrete estimator minimizes a modified version of the likelihood function by using exclusively linear techniques, and thus circumventing the high computational complexity of the Maximum Likelihood (ML) method. The proposed approach is thus easy to implement, requires no explicit second order statistical information, and is shown to produce high quality estimates at a very modest computational cost. A recursive version of the algorithm, suitable for on-line implementation, is also

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Giles, Michael B. "Multilevel Monte Carlo methods." Acta Numerica 24 (April 27, 2015): 259–328. http://dx.doi.org/10.1017/s096249291500001x.

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Monte Carlo methods are a very general and useful approach for the estimation of expectations arising from stochastic simulation. However, they can be computationally expensive, particularly when the cost of generating individual stochastic samples is very high, as in the case of stochastic PDEs. Multilevel Monte Carlo is a recently developed approach which greatly reduces the computational cost by performing most simulations with low accuracy at a correspondingly low cost, with relatively few simulations being performed at high accuracy and a high cost.In this article, we review the ideas beh

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Nesser, Hannah, Daniel J. Jacob, Joannes D. Maasakkers, et al. "Reduced-cost construction of Jacobian matrices for high-resolution inversions of satellite observations of atmospheric composition." Atmospheric Measurement Techniques 14, no. 8 (2021): 5521–34. http://dx.doi.org/10.5194/amt-14-5521-2021.

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Abstract. Global high-resolution observations of atmospheric composition from satellites can greatly improve our understanding of surface emissions through inverse analyses. Variational inverse methods can optimize surface emissions at any resolution but do not readily quantify the error and information content of the posterior solution. The information content of satellite data may be much lower than its coverage would suggest because of failed retrievals, instrument noise, and error correlations that propagate through the inversion. Analytical solution of the inverse problem provides closed-

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Giacomini, Matteo, Karen Veroy, and Pedro Díez. "Special Issue on Credible High‐Fidelity and Low‐Cost Simulations in Computational Engineering." International Journal for Numerical Methods in Engineering 121, no. 23 (2020): 5151–52. http://dx.doi.org/10.1002/nme.6551.

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Lopez-Cabrejos, Josue, Thuanne Paixão, Ana Beatriz Alvarez, and Diodomiro Baldomero Luque. "An Efficient and Low-Complexity Transformer-Based Deep Learning Framework for High-Dynamic-Range Image Reconstruction." Sensors 25, no. 5 (2025): 1497. https://doi.org/10.3390/s25051497.

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High-dynamic-range (HDR) image reconstruction involves creating an HDR image from multiple low-dynamic-range images as input, providing a computational solution to enhance image quality. This task presents several challenges, such as frame misalignment, overexposure, and motion, which are addressed using deep learning algorithms. In this context, various architectures with different approaches exist, such as convolutional neural networks, diffusion networks, generative adversarial networks, and Transformer-based architectures, with the latter offering the best quality but at a high computation

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