Dynamic Topology Optimization (DTO) is a powerful technique for designing complex systems, particularly those exhibiting dynamic behavior, such as protein folding and cellular automata. Traditional topology optimization methods often rely on manually crafted constraints and iterative refinement, limiting their adaptability to intricate designs. This paper introduces a novel DTO approach leveraging reinforcement learning and evolutionary algorithms to automatically discover optimal topologies. We present a framework where the system's topology is iteratively refined through a reinforcement learning process, guided by a reward function that prioritizes efficiency and stability. This approach offers a more flexible and adaptive methodology compared to conventional methods, capable of handling complex, multi-dimensional designs. The core mechanism centers around a self-organizing evolutionary algorithm, where individuals represent potential topology configurations, and the reinforcement learning agent guides their evolution towards improved solutions. This work demonstrates the effectiveness of this framework through illustrative examples, showcasing its ability to generate novel and efficient topologies for a range of complex systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Protein folding prediction is a critical challenge in bioinformatics, demanding significant computational resources. This research explores the potential of quantum computing to accelerate and enhance this process. We formulate the protein folding problem as a quantum optimization problem, employing quantum simulation algorithms for efficient solution. The methodology involves mapping the protein's amino acid sequence into a quantum Hamiltonian, followed by simulating the system using quantum algorithms. We investigate the effectiveness of Variational Quantum Eigensolver (VQE) and Quantum Approximate Optimization Algorithm (QAOA) in determining the ground state energy and, consequently, the folded conformation of the protein. The predicted structures are then validated through comparison with known experimental data and integration with classical molecular dynamics simulations. The core claim of this work is to leverage the computational power of quantum computers to drastically reduce the time required for protein folding prediction, leading to improved accuracy and efficiency. The results demonstrate a promising approach for tackling this computationally intensive problem, potentially revolutionizing drug discovery and protein engineering.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel geometric simulation method, termed Wave Function Collapse (WFC), designed to model complex biological processes. Traditional computational methods often struggle to effectively represent the intricate dynamics of biological systems due to their inherent complexity. WFC utilizes quantum-inspired representations and collapse mechanisms to achieve a more streamlined and insightful simulation of emergent properties, offering a potential paradigm shift in biological modeling. The core of the method involves representing the system's state as a wave function, collapsing it through a controlled process to generate output behavior. We explore the application of WFC to simulating protein folding and enzyme kinetics, demonstrating its potential for capturing essential aspects of these processes. This work aims to provide a robust and mathematically rigorous framework for visualizing and analyzing complex biological phenomena.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to artificial intelligence by integrating neural networks and symbolic reasoning, resulting in Neural Symbolic Hybrid Systems. The core claim is that combining the strengths of both paradigms—the pattern recognition capabilities of neural networks and the logical inference capabilities of symbolic systems—yields more robust and explainable AI systems. The proposed system utilizes neural networks for perceptual tasks, such as feature extraction and initial data interpretation, while employing symbolic reasoning engines for higher-level logical inference, decision-making, and knowledge representation. The system architecture is designed to facilitate seamless interaction between these two components. Specifically, the neural network outputs are translated into symbolic representations, which are then processed by the reasoning engine. Conversely, the symbolic reasoning engine's conclusions are fed back into the neural network for refinement and improved accuracy. This hybrid approach addresses limitations inherent in purely neural or purely symbolic systems. Neural networks alone can lack explainability and robustness against noisy or incomplete data, while symbolic systems often struggle with complex, real-world scenarios requiring flexible pattern recognition. This paper outlines the core mechanisms, architecture, and potential benefits of such a system, highlighting its capacity for robust reasoning and enhanced interpretability. The system is envisioned as a significant step towards developing more reliable and trustworthy AI solutions.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel engine for Explainable Artificial Intelligence (XAI) focused on causal inference. The core claim is to construct an AI engine capable of performing causal reasoning and providing more reliable explanations for decision-making. The engine leverages a mechanism based on structural causal models, integrating Bayesian Networks and Deep Learning to identify and infer causal relationships, ultimately generating explainable decision processes. Traditional XAI methods often struggle to provide genuine causal explanations, limiting their utility in high-stakes decision scenarios. This work addresses this limitation by explicitly incorporating causal inference, significantly enhancing the reliability and trustworthiness of AI-driven decisions. The proposed system aims to move beyond simply explaining correlations to elucidating the underlying causal mechanisms driving outcomes. The system is designed to be modular and adaptable, allowing for the integration of diverse datasets and causal discovery techniques. Further research will focus on refining the explanation generation process and validating the engine's performance across various domains.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Algorithmic bias presents a significant challenge to the responsible deployment of machine learning models across various sectors. This paper proposes a novel framework for algorithmic fairness auditing that utilizes Explainable AI (XAI) techniques to enhance transparency and accountability. The core idea is to systematically examine machine learning models for potential biases by leveraging XAI methods to understand the decision-making processes within these models. This approach moves beyond simply measuring disparate impact to identifying the underlying reasons for biased predictions, offering actionable insights for mitigation. The framework incorporates techniques like SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-agnostic Explanations) to provide feature importance scores and local explanations, respectively. We detail the methodology, illustrating how XAI can be integrated into the auditing workflow, and discuss the potential impact of this approach on improving fairness in algorithmic systems. The framework aims to transform the often-opaque process of bias detection into a more transparent and understandable one, ultimately contributing to the development of fairer and more equitable AI systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to machine learning utilizing Cognitive Graph Representation (CGR). CGR fundamentally shifts the paradigm by modeling knowledge and relationships within machine learning models as cognitive graphs. These graphs explicitly incorporate uncertainty, inference chains, and metacognitive processes, leading to significant improvements in learning efficiency and generalization performance. The core of CGR lies in a graph structure where nodes represent concepts and edges represent relationships, each associated with a confidence score and a set of inference rules. The model actively navigates this graph, dynamically seeking connections and refining its understanding through a process mirroring human cognition. We argue that this integration of cognitive science principles directly into the architecture of machine learning models represents a crucial step towards creating more robust, adaptable, and explainable AI systems. The theoretical framework and underlying mechanisms are presented, highlighting the potential advantages of CGR over traditional representation learning techniques. The key contribution is the development of a systematic approach to constructing and utilizing cognitive graphs for machine learning applications.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Explainable AI (XAI) is a rapidly growing field focused on making AI models more transparent and understandable to humans. However, many existing XAI techniques primarily focus on post-hoc explanations, often without a deep understanding of the underlying causal relationships driving the model's decisions. This paper proposes a novel approach leveraging graph-based causal inference to generate more robust and meaningful explanations for AI models. The core idea is to construct a causal graph that explicitly represents the relationships between input features and model outputs. This graph allows us to identify the key causal pathways responsible for the model's predictions, providing a transparent and actionable explanation. We demonstrate the feasibility and potential benefits of this approach, arguing that incorporating causal understanding into XAI will lead to more reliable and interpretable AI systems. The generated explanations are grounded in causal relationships rather than simply highlighting correlations, addressing a significant limitation of current XAI methods. We outline the methodology, discuss potential challenges, and highlight the advantages of this graph-based causal inference framework for enhancing the explainability of AI models.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data. However, existing FL systems are vulnerable to various attacks, including model poisoning and Byzantine attacks, which can compromise model integrity and user privacy. This paper proposes a novel decentralized federated learning framework that leverages blockchain technology to address these vulnerabilities. The framework incorporates Byzantine fault tolerance mechanisms to detect and mitigate malicious participants, and differential privacy techniques to protect user data. By utilizing a blockchain-based architecture, the system achieves secure and reliable learning while maintaining data privacy. The core claim is that current federated learning systems are vulnerable to attacks. The proposed mechanism utilizes a blockchain-based architecture for decentralized federated learning, incorporating Byzantine fault tolerance – detecting and mitigating malicious participants – and differential privacy – protecting user data – ensuring secure and reliable learning. The system's architecture is designed to provide transparency, auditability, and resilience against attacks, leading to a more trustworthy and secure FL environment.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to machine learning by enabling collaborative model training across decentralized devices without directly exchanging data. However, traditional FL methods often struggle to provide strong differential privacy guarantees, especially when group membership and data heterogeneity change dynamically. This paper proposes a novel framework for enhancing differential privacy in FL by incorporating a dynamic privacy budget allocation scheme. The core idea is to adapt the privacy level based on the size and diversity of each participating group, mitigating the impact of fluctuating group memberships and varying data distributions. We present a detailed analysis of the challenges posed by dynamic group membership and demonstrate the effectiveness of our approach through a theoretical framework. The proposed method aims to strike a balance between model accuracy and privacy preservation, contributing to a more robust and reliable FL system. The key contributions lie in the adaptive privacy allocation and the addressing of non-static group dynamics.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to decentralized federated learning that leverages graph consensus algorithms to achieve robust and privacy-preserving model aggregation. The core idea is to represent participating devices as nodes within a graph and utilize graph consensus protocols for model update exchange and aggregation. Differential privacy is integrated into this framework to protect the sensitive data residing on individual devices. We demonstrate that this combined approach offers a scalable and efficient solution for distributed learning, particularly in scenarios with a highly decentralized network topology. The proposed system addresses key challenges associated with traditional federated learning, such as the vulnerability to malicious actors and the potential for information leakage, while maintaining model accuracy. The theoretical framework outlines the convergence properties of the graph consensus algorithm and provides a rigorous analysis of the privacy guarantees afforded by differential privacy. This work contributes to the growing field of privacy-preserving distributed learning, offering a practical and adaptable solution for diverse applications. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Federated learning (FL) offers a promising approach to training machine learning models on decentralized data sources without directly exchanging data, thereby preserving user privacy. However, FL systems are vulnerable to Byzantine attacks, where malicious participants can inject corrupted model updates to compromise the global model. This paper proposes a novel framework for distributed federated learning with Byzantine resilience leveraging Secure Multi-Party Computation (SMPC). Our approach utilizes SMPC protocols to ensure that individual model updates remain private during aggregation, effectively mitigating the impact of Byzantine attacks. We formulate the problem as a multi-party computation where each participant contributes to the model update without revealing their individual data or intermediate computations. The core claim of this work is that achieving robust federated learning in the presence of malicious participants necessitates advanced privacy-preserving techniques, and our SMPC-based framework provides a compelling solution. We demonstrate the theoretical resilience of our framework against Byzantine attacks and outline the key components involved in implementing this system. The goal is to provide a secure and robust method for collaborative model training in environments with untrusted participants.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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