Academic Articles & Research Papers

Compositional pretraining improves computational efficiency and matches animal behaviour on complex tasks.

Spatially Constrained Discontinuity Trace Extraction from 3D Point Clouds by Intersecting Boundaries Segmented

Discontinuity trace provides critical geological data for engineering design and construction optimization. However, current extraction methods relying on discontinuity intersection fitting are highly sensitive to the segmentation accuracy ...

Quantum Image Encryption Scheme Using Arnold Transform and S-box Scrambling

The paper proposes a lossless quantum image encryption scheme based on substitution tables (S-box) scrambling, mutation operation and general Arnold transform with keys. First, the key generator builds upon the foundation of SHA-256 hash wi...

Quantum Computing in Transportation Engineering: A Survey

Quantum computing introduces new computational methods that address complex optimization and machine learning challenges in transportation engineering. This literature survey reviews fundamental quantum principles, including qubits, superpo...

Multi-factor association dependence modelling for project risk analysis

This article presents an analytic (non-simulation) dependence model for quantitative project risk analysis. The multi-factor association model (MFAM) accounts for multiple association factors in typical project settings and provides a close...

Comparative Study of CUDA GPU Implementations in Python With the Fast Iterative Shrinkage-Thresholding Algorithm for LASSO

A general-purpose GPU (GPGPU) is employed in a variety of domains, including accelerating the spread of deep natural network models; however, further research into its effective implementation is needed. When using the compute unified devic...

Zero Dynamic Attack Control of Networked Control Systems Based on Ac-tive Elastic Defense Strategy

In this paper, an elastic control strategy for network control systems (NCSs) with zero dynamic attacks is studied，in which an implicit false data injection attack is designed based on input control and data measurement. When the control si...

A local meshless method for the one-dimensional Fisher’s equation

This study presents a novel local meshless approach for solving one-dimensional Fisher’s equation, combining a local scheme, Gaussian radial basis functions (G-RBF), and a collocation technique. The method leverages the Gaussian basis’s non...

PRICING EUROPEAN OPTIONS IN SELECTED STOCHASTIC VOLATILITY MODELS

In this paper four methods of calculating characteristic functions and their application to selected stochastic volatility models are considered. The methods applied are based on the assumption that the prices of European calls are evaluate...

Research on Parameter Influence of Offshore Wind Turbines Based on Measured Data Analysis

Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maint...

Generalized Task‐Driven Design of Soft Robots via Reduced‐Order Finite Element Method‐Based Surrogate Modeling

Task‐driven design of soft robots requires models that are physically accurate and computationally efficient, while remaining transferable across actuator designs and task scenarios. However, existing modeling approaches typically face a fu...

Optimization Strategies in Quantum Machine Learning: A Performance Analysis

This study presents a comprehensive comparison of multiple optimization algorithms applied to a quantum classification model, utilizing the Cleveland dataset. Specifically, the research focuses on three prominent optimizers—COBYLA, L-BFGS-B...

Quantum computing

Computers today become faster by becoming smaller. By reducing the size of transistors, engineers are able to fit more of them on a given size microprocessor, thus increasing the processor's computational power. This process cannot continue...

The Role and Application of Matrices in Artificial Intelligence: Foundations, Methods, and Advancements

Matrices are foundational to artificial intelligence (AI), serving as critical tools for data representation, manipulation, and transformation across various applications. From machine learning algorithms to neural network architectures, ma...

A Novel Slow-Growing Gross Error Detection Method for GNSS/Accelerometer Integrated Deformation Monitoring Based on State Domain Consistency Theory

The accuracy and integrity of structural deformation monitoring can be improved by the GNSS/accelerometer integrated system, and gross error detection is the key to further improving the reliability of GNSS/accelerometer monitoring. Traditi...

Machine Unlearning: A Perspective, Taxonomy, and Benchmark Evaluation

Machine Learning (ML) models trained on large-scale datasets learn useful predictive patterns, but they may also memorize undesired information, leading to risks such as information leakage, bias, copyright violations, and privacy attacks. ...

A Systematic YOLO-Specific Model Selection for Mechanical Fault Identification in High-Voltage Insulators

Regular monitoring of outdoor insulators is crucial to ensure the reliable functioning of the power grid. With recent progress in computer vision technologies, traditional manual and expensive visual inspections can now be replaced by autom...

A Lightweight Reconstruction Model via a Neural Network for a Video Super-Resolution Model

Super-resolution in image and video processing has been a challenge in computer vision, with its progression creating substantial societal ramifications. More specifically, video super-resolution methodologies aim to restore spatial details...

Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state-of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...

Rethinking the Inception Architecture for Computer Vision

Convolutional networks are at the core of most state of-the-art computer vision solutions for a wide variety of tasks. Since 2014 very deep convolutional networks started to become mainstream, yielding substantial gains in various benchmark...