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 properties. We propose a novel mathematical framework based on the incorporation of non-local interactions and time-dependent dynamics within harmonic functions. This framework aims to provide a more robust and insightful approach to understanding and predicting the behavior of these systems. The core claim is that this new approach enables the prediction of emergent properties, offering a significant advancement in the field of complex system analysis.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 underlying data. This work introduces a novel methodology leveraging Topological Data Analysis (TDA) to address this challenge. We hypothesize that underlying topological features within protein folding data – specifically, the presence and connectivity of loops, cavities, and other topological structures – can be used to predict and understand the pathways proteins take to fold. This paper outlines the theoretical framework, describes the application of TDA techniques (persistent homology, Mapper, etc.) to protein folding data, and presents preliminary results demonstrating the potential of this approach. We demonstrate how TDA can extract meaningful insights from complex protein folding landscapes that are often missed by conventional methods. The core claim is that understanding protein folding pathways is a major challenge in bioinformatics, and our approach provides a new lens through which to examine this problem. This work contributes to the development of more accurate and efficient protein folding prediction tools.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 system's neural network structure and dynamics through neuromorphic models. These models are then implemented utilizing hardware acceleration techniques to achieve real-time performance. The system's architecture is designed to overcome the limitations of traditional von Neumann architectures in image processing by exploiting inherent parallelism and energy efficiency found in biological neural systems. This research introduces a new paradigm for visual processing, offering a potentially transformative solution for applications demanding low-latency and low-power operation, such as autonomous robotics, surveillance, and edge computing. The system's performance is evaluated through simulation and theoretical analysis, demonstrating its potential for achieving significant improvements in processing speed and energy efficiency compared to conventional approaches.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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. Our model proposes a framework that utilizes graph structures to represent quantum states, enabling dynamic modifications to the state's attributes through graph manipulation. This approach offers a potentially transformative path toward enhanced quantum simulation and algorithm development. The core mechanism involves representing quantum states as graphs, where nodes represent states and edges represent relationships between them, allowing for iterative and adaptive computational processes. This research explores the design of such a model and its potential to overcome limitations of existing quantum computation techniques. The paper details the model's architecture, demonstrates its ability to perform specific quantum operations, and discusses future research directions with a focus on scalability and practical applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 representation of the network, enabling the calculation of statistical properties through a fundamentally different approach. The core mechanism centers around dynamically updating node and edge relationships to capture evolving network structure and behavior. This research explores the potential of this approach to provide a more robust and insightful analysis of complex networks compared to traditional methods. We demonstrate the efficacy of this method through a series of examples, showcasing its ability to capture intricate patterns and relationships within the data.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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. Our model proposes a framework that utilizes graph structures to represent quantum states, enabling dynamic modifications to the state's attributes through graph manipulation. This approach offers a potentially transformative path toward enhanced quantum simulation and algorithm development. The core mechanism involves representing quantum states as graphs, where nodes represent states and edges represent relationships between them, allowing for iterative and adaptive computational processes. This research explores the design of such a model and its potential to overcome limitations of existing quantum computation techniques. The paper details the model's architecture, demonstrates its ability to perform specific quantum operations, and discusses future research directions with a focus on scalability and practical applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 computational efficiency. The proposed method utilizes a reinforcement learning (RL)-based resource scheduling algorithm, learning an optimal resource allocation policy through iterative interaction with the environment. The paper details the algorithm's architecture, the RL framework employed, and presents a theoretical analysis of its performance. The objective is to address the complexities inherent in managing heterogeneous edge environments, optimizing resource utilization, and minimizing latency for applications. Simulation results demonstrate the algorithm's effectiveness in achieving superior performance compared to traditional scheduling approaches. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 representation of the network, enabling the calculation of statistical properties through a fundamentally different approach. The core mechanism centers around dynamically updating node and edge relationships to capture evolving network structure and behavior. This research explores the potential of this approach to provide a more robust and insightful analysis of complex networks compared to traditional methods. We demonstrate the efficacy of this method through a series of examples, showcasing its ability to capture intricate patterns and relationships within the data.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 system's neural network structure and dynamics through neuromorphic models. These models are then implemented utilizing hardware acceleration techniques to achieve real-time performance. The system's architecture is designed to overcome the limitations of traditional von Neumann architectures in image processing by exploiting inherent parallelism and energy efficiency found in biological neural systems. This research introduces a new paradigm for visual processing, offering a potentially transformative solution for applications demanding low-latency and low-power operation, such as autonomous robotics, surveillance, and edge computing. The system's performance is evaluated through simulation and theoretical analysis, demonstrating its potential for achieving significant improvements in processing speed and energy efficiency compared to conventional approaches.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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, leading to a superficial understanding of how models arrive at their conclusions. This paper proposes a novel algorithm for automatically generating causal graphs from AI models, leveraging techniques of counterfactual reasoning and causal discovery. The core claim is that these generated graphs provide a transparent representation of the causal relationships within the model, significantly enhancing its explainability. The mechanism involves analyzing the relationships between model inputs and outputs using these techniques, resulting in a graph that accurately reflects the model's decision-making logic. We demonstrate a new approach to interpreting complex AI models, increasing their trustworthiness.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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 learning and symbolic AI. Specifically, we leverage the predictive power of deep neural networks alongside logical inference techniques to explain the internal reasoning of these models. The core mechanism involves comparing the outputs of a deep learning model with knowledge represented in a symbolic knowledge base. By applying logical inference algorithms, we can derive a symbolic representation of the model's reasoning process, providing a more transparent and understandable explanation. This approach aims to enhance the trustworthiness and interpretability of deep learning systems, a critical step towards wider adoption and reliable deployment. The key contributions include a novel framework and a methodology for translating deep learning outputs into logical inferences, leading to enhanced model interpretability.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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, leading to a superficial understanding of how models arrive at their conclusions. This paper proposes a novel algorithm for automatically generating causal graphs from AI models, leveraging techniques of counterfactual reasoning and causal discovery. The core claim is that these generated graphs provide a transparent representation of the causal relationships within the model, significantly enhancing its explainability. The mechanism involves analyzing the relationships between model inputs and outputs using these techniques, resulting in a graph that accurately reflects the model's decision-making logic. We demonstrate a new approach to interpreting complex AI models, increasing their trustworthiness.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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