159 papers indexed here
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This paper explores the application of non-standard harmonic analysis to the analysis of complex systems, specifically focusing on fluid dynamics and protein folding. Traditional methods often fall short in effectively modeling these systems due to their inherent complexity and the difficulty in capturing emergent prop...
Protein folding, the process by which a polypeptide chain attains its functional three-dimensional structure, is a fundamental problem in biochemistry and bioinformatics. Traditional approaches to predicting protein folding pathways have faced significant challenges due to the complex, high-dimensional nature of the un...
This paper proposes a novel approach to real-time visual processing leveraging the principles of neuromorphic computing. The core aim is to design and implement a system capable of efficient image recognition and processing with reduced power consumption. The methodology centers on mimicking the biological visual syste...
This paper introduces a novel quantum state computation model based on graph structures, leveraging the inherent properties of quantum states to enable adaptive computation. Traditional quantum computing approaches often struggle with complex quantum states, presenting a significant challenge for efficient computation....
This paper investigates the application of a novel non-Euclidean metric for computing statistical inference in complex networks. Current approaches often treat networks as static, limiting their ability to effectively analyze dynamic patterns and relationships. We propose a geometry-based metric to generate a dynamic r...
This paper introduces a novel quantum state computation model based on graph structures, leveraging the inherent properties of quantum states to enable adaptive computation. Traditional quantum computing approaches often struggle with complex quantum states, presenting a significant challenge for efficient computation....
This paper investigates the challenges and proposes a novel approach to distributed edge computing resource scheduling and optimization. The core claim is to design an efficient algorithm that dynamically allocates resources based on task requirements and edge computing node capabilities, ultimately enhancing computati...
This paper investigates the application of a novel non-Euclidean metric for computing statistical inference in complex networks. Current approaches often treat networks as static, limiting their ability to effectively analyze dynamic patterns and relationships. We propose a geometry-based metric to generate a dynamic r...
This paper proposes a novel approach to real-time visual processing leveraging the principles of neuromorphic computing. The core aim is to design and implement a system capable of efficient image recognition and processing with reduced power consumption. The methodology centers on mimicking the biological visual syste...
The increasing deployment of complex artificial intelligence (AI) models in critical applications necessitates methods for understanding and explaining their decision-making processes. Traditional model interpretability techniques often struggle to capture the underlying causal relationships driving model predictions,...
This paper explores a novel approach to interpreting deep learning models by integrating symbolic reasoning. Traditional deep learning models are often considered "black boxes," making it difficult to understand their decision-making processes. This work proposes a framework that combines the strengths of both deep lea...
The increasing deployment of complex artificial intelligence (AI) models in critical applications necessitates methods for understanding and explaining their decision-making processes. Traditional model interpretability techniques often struggle to capture the underlying causal relationships driving model predictions,...
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