This paper introduces a novel dynamic topology-based non-linear reinforcement learning (RL) algorithm designed to enhance learning efficiency and robustness. Traditional reinforcement learning methods often rely on static strategies, limiting adaptability and vulnerability to environmental changes. Our algorithm dynamically adjusts the topology of the state space, effectively simulating the learning process and mitigating the impact of perturbations. We explore how this dynamic structure contributes to improved performance across a range of tasks. The core mechanism centers around the continuous evolution of state representations, driven by a simulated topology, enabling the agent to better generalize to unseen scenarios. This work presents a framework for building more robust and adaptable reinforcement learning agents.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to dynamic program optimization leveraging multi-modal contextual information. The core idea is to combine code execution data, static code analysis, and natural language descriptions to enable adaptive and dynamic optimization strategies. We propose a deep learning-based framework that transforms these diverse modalities into a unified representation and utilizes reinforcement learning to dynamically select and adjust optimization techniques such as code transformation, memory allocation optimization, and parallelization strategies. Our framework addresses the limitations of existing optimization methods that often rely on single-modal information or predefined rules, demonstrating a significant improvement in optimization effectiveness through multi-modal fusion and dynamic adjustment. The key contribution lies in the automated adaptation to program context, leading to more efficient and tailored optimization solutions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel prediction framework based on adaptive non-linear geometry models. Traditional predictive models often rely on fixed parameters, limiting their accuracy and efficiency. Our approach dynamically adjusts model parameters to capture the complex, non-linear relationships within the system being modeled. This adaptive mechanism significantly enhances prediction precision and speed compared to static models. We present a methodology for parameter adjustment, leveraging a reinforcement learning algorithm to optimize for model performance across a range of input data. This framework demonstrates improved accuracy and efficiency in predicting time-series data, particularly in scenarios involving complex dynamics and non-linear dependencies. We provide a comprehensive analysis of the algorithm's effectiveness through simulations and experimental validation. The core claim is that this adaptive framework achieves superior predictive performance through dynamic parameter adjustment.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel self-adaptive neural network architecture, termed Dynamic Adaptive Neural Network (DANN), designed to enhance model generalization. Traditional neural networks often suffer from suboptimal performance due to fixed parameters and lack of adaptability. DANN leverages dynamic adjustments to both the weights and connections within the network, achieved through a reinforcement learning-based optimization process. This approach allows the network to continuously adapt to the data, mitigating the limitations of static model parameters. The paper details the core mechanism, including the learning algorithm employed for weight and connection adjustment, and discusses the experimental results demonstrating the superior performance of DANN compared to state-of-the-art models. The core claim is that the dynamic self-adaptive network architecture significantly improves generalization capabilities through continuous optimization of the network's parameters.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Predicting the folding pathway of a protein – the process by which a linear chain of amino acids adopts its functional three-dimensional structure – is a central challenge in computational biology. Existing methods often struggle to accurately represent the intricate and dynamic interactions between amino acids that govern this process. This paper proposes a novel approach leveraging Graph Neural Networks (GNNs) to address this limitation. We represent proteins as graphs, where nodes correspond to individual amino acids and edges encode the physical and chemical interactions between them. The GNN learns to predict the folding pathway by propagating information through this graph structure, effectively capturing the sequential and interconnected nature of the folding process. We demonstrate that this approach offers a significant improvement over traditional methods in capturing the complex relationships within protein sequences and predicting the pathways of protein folding. The core of our method lies in the ability of GNNs to learn representations that are robust to noise and variations in protein sequences, ultimately leading to more accurate predictions. This work highlights the potential of graph-based neural networks in tackling complex biological problems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Resonance-Based Probability Distribution Modeling presents a novel probabilistic modeling framework predicated on the principles of resonant frequencies and vibrational modes within complex systems. This approach aims to enhance predictive accuracy across diverse domains, including protein folding, fluid dynamics, and other systems exhibiting dynamic behavior. The core mechanism involves constructing a complex, multi-dimensional resonance function to represent system stability and predict outcomes, offering a departure from conventional statistical approaches. This research investigates the potential of this framework to achieve unprecedented levels of predictive capability by leveraging the inherent sensitivity of systems to resonant frequencies.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to protein structure prediction leveraging the principles of quantum entanglement. Traditional protein structure prediction methods are often limited by the computational complexity of simulating large biomolecular systems. We propose a framework that utilizes quantum entanglement to model the complex correlations inherent in protein folding, potentially overcoming these limitations. The core idea involves translating the amino acid sequence of a protein into a quantum state and employing quantum computation, specifically entanglement-based algorithms, to predict the protein's three-dimensional structure. The theoretical framework outlines the transformation process, the quantum algorithm design, and the methods for interpreting the results. We explore the potential advantages of this approach, focusing on its ability to capture long-range interactions and conformational flexibility that are difficult to model accurately with classical methods. The ultimate goal is to establish a new paradigm for protein structure prediction, offering improved accuracy and efficiency.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed within biological systems. This research proposes a framework that leverages these quantum phenomena, specifically focusing on quantum tunneling, superposition, and entanglement, to address complex challenges in biological data analysis. The core mechanism involves simulating quantum effects during processes like protein folding and DNA sequence recognition, and utilizing quantum algorithms – such as Grover's algorithm and quantum annealing – to optimize sequence data analysis. We present a conceptual approach to formulating biological problems as quantum computing problems, offering a fundamentally new perspective for bioinformatics. The potential impact of this approach lies in developing more efficient and accurate algorithms for tasks including genomic sequence alignment, protein structure prediction, and drug discovery. This work aims to bridge the gap between quantum mechanics and biology, paving the way for a new era of bioinformatics driven by quantum principles. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Protein folding is a fundamental process in biology, crucial for protein function and stability. Traditional methods often rely on rigid, predefined folding rules, limiting flexibility and efficiency. This paper introduces a novel computational approach – a dynamic modeling algorithm – that adapts protein structure during folding, significantly enhancing stability. We propose a model leveraging self-adaptive mechanisms to dynamically adjust the protein's conformation, achieving a more robust and versatile folding process. This research addresses the limitations of existing methods by offering a flexible framework for predicting and controlling protein folding.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of non-standard geometry to computational geometry, focusing on the development of a novel method for defining and computing geometric properties for complex systems, particularly fluid dynamics and protein folding. Traditional geometric computation often struggles with the inherent complexity and self-organization of these systems, necessitating the creation of intricate geometric constructs. We propose a 'geometric language' – a system of rules and symbolic representations – that enables efficient manipulation and analysis of these complex shapes. The core mechanism involves establishing a hierarchical structure within this language, allowing for the generation of novel geometric configurations through a combination of geometric transformations and parametric modeling. This approach aims to overcome limitations in current computational geometry by offering a framework for tackling problems that are currently computationally intractable. The paper will detail the conceptualization of this language, its implementation through a set of rules and algorithms, and initial explorations into its potential for solving specific problems within fluid dynamics and protein folding. Finally, we present preliminary results demonstrating the feasibility of this approach, highlighting its potential for advancing the field of computational geometry.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores the application of principles from quantum biology to design novel bioinformatics algorithms. Traditional bioinformatics relies heavily on classical computational models, often neglecting the potential influence of quantum effects observed within biological systems. This research proposes a framework that leverages these quantum phenomena, specifically focusing on quantum tunneling, superposition, and entanglement, to address complex challenges in biological data analysis. The core mechanism involves simulating quantum effects during processes like protein folding and DNA sequence recognition, and utilizing quantum algorithms – such as Grover's algorithm and quantum annealing – to optimize sequence data analysis. We present a conceptual approach to formulating biological problems as quantum computing problems, offering a fundamentally new perspective for bioinformatics. The potential impact of this approach lies in developing more efficient and accurate algorithms for tasks including genomic sequence alignment, protein structure prediction, and drug discovery. This work aims to bridge the gap between quantum mechanics and biology, paving the way for a new era of bioinformatics driven by quantum principles. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Protein structure prediction remains a grand challenge in computational biology. Traditional methods often struggle to accurately capture the intricate relationships within a protein sequence, leading to suboptimal structural models. This work explores the application of Graph Neural Networks (GNNs) to address this challenge. We hypothesize that by representing protein sequences as graphs, where nodes represent amino acids and edges represent interactions, GNNs can effectively learn and model these complex relationships, ultimately improving the accuracy and efficiency of protein structure prediction. This paper details the framework for utilizing GNNs, focusing on the construction of protein graphs, the design of suitable GNN architectures, and the training process. We demonstrate the potential of this approach and discuss future research directions. The core claim of this work is the utilization of GNNs to enhance protein structure prediction. The core mechanism involves transforming protein sequences into graph structures, leveraging GNNs to learn structural information. This approach represents a novel way to tackle the protein folding problem. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
