This paper proposes a novel approach to simulating complex biological systems utilizing graph-based modeling and simulation techniques. The core claim is that traditional simulation methods often face significant computational limitations when dealing with intricate biological systems. This limitation stems from the exponential growth of computational complexity with increasing system size and interaction density. The proposed solution involves representing biological systems as graphs, where nodes represent individual biological entities (e.g., genes, proteins, cells, organisms) and edges represent the interactions between them. This graph representation allows for the application of efficient graph algorithms and simulation techniques, dramatically reducing computational burden. We detail the methodology, including graph construction, node and edge attributes, and simulation algorithms tailored for biological systems. The approach demonstrates scalability and offers a viable alternative for modeling complex biological interactions, particularly those involving large numbers of components and intricate feedback loops. We explore various simulation techniques applicable within this framework, such as random walks, message passing, and network diffusion, and discuss their suitability for different biological scenarios. The results, while hypothetical due to the absence of experimental data, illustrate the potential of this method for generating insights into system dynamics and identifying key regulatory pathways. The ultimate goal is to provide a robust and scalable platform for understanding the behavior of complex biological systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the potential of quantum-enhanced diffusion processes to revolutionize generative modeling. Traditional diffusion models, while effective, suffer from computationally intensive training times and limitations in generating diverse and accurate outputs. We propose a novel approach leveraging quantum annealing and Grover's algorithm to dramatically accelerate the diffusion process, thereby improving both speed and accuracy. This research explores the theoretical foundations of QEP, outlines the implementation details, and presents preliminary results demonstrating significant performance improvements compared to conventional diffusion methods. The core mechanism centers around quantum acceleration of the forward and reverse diffusion steps, offering a pathway to overcome limitations in traditional algorithms. The investigation touches on the implications of this technology for various generative modeling applications, including image generation and data synthesis.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the potential of quantum-enhanced diffusion processes to revolutionize generative modeling. Traditional diffusion models, while effective, suffer from computationally intensive training times and limitations in generating diverse and accurate outputs. We propose a novel approach leveraging quantum annealing and Grover's algorithm to dramatically accelerate the diffusion process, thereby improving both speed and accuracy. This research explores the theoretical foundations of QEP, outlines the implementation details, and presents preliminary results demonstrating significant performance improvements compared to conventional diffusion methods. The core mechanism centers around quantum acceleration of the forward and reverse diffusion steps, offering a pathway to overcome limitations in traditional algorithms. The investigation touches on the implications of this technology for various generative modeling applications, including image generation and data synthesis.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel approach to simulating complex biological systems utilizing graph-based modeling and simulation techniques. The core claim is that traditional simulation methods often face significant computational limitations when dealing with intricate biological systems. This limitation stems from the exponential growth of computational complexity with increasing system size and interaction density. The proposed solution involves representing biological systems as graphs, where nodes represent individual biological entities (e.g., genes, proteins, cells, organisms) and edges represent the interactions between them. This graph representation allows for the application of efficient graph algorithms and simulation techniques, dramatically reducing computational burden. We detail the methodology, including graph construction, node and edge attributes, and simulation algorithms tailored for biological systems. The approach demonstrates scalability and offers a viable alternative for modeling complex biological interactions, particularly those involving large numbers of components and intricate feedback loops. We explore various simulation techniques applicable within this framework, such as random walks, message passing, and network diffusion, and discuss their suitability for different biological scenarios. The results, while hypothetical due to the absence of experimental data, illustrate the potential of this method for generating insights into system dynamics and identifying key regulatory pathways. The ultimate goal is to provide a robust and scalable platform for understanding the behavior of complex biological systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to network anomaly detection that leverages topological feature learning to address the limitations of traditional static feature engineering methods. The core idea is to dynamically capture network changes by automatically learning relevant topological features from network graphs. This allows the system to identify anomalies based on shifts in network topology and node behavior. We propose a framework where a graph neural network (GNN) is utilized to learn these dynamic topological features. The learned features are then used in a classification model to detect anomalous nodes or edges. Our approach demonstrates improved accuracy and adaptability compared to traditional methods in dynamic network environments. The key contributions lie in the automated feature extraction process and the utilization of GNNs for capturing temporal dependencies within network structures. The performance is evaluated using synthetic and real-world network datasets.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel framework for generating graph embeddings that explicitly accounts for the temporal dynamics of relationships within a network. Traditional graph embedding techniques often treat graphs as static structures, neglecting the evolving nature of connections and their influence over time. Our approach leverages a recurrent neural network (RNN) coupled with a custom-designed loss function that measures "temporal divergence" – the difference in embeddings of nodes at consecutive time steps – weighted by the strength and type of relationships. This allows the model to learn and represent how relationships change over time, leading to more accurate and robust graph embeddings. We argue that this method represents a significant advancement over existing static embedding techniques and offers the potential to capture emergent network behaviors and predict future network modifications with improved accuracy. The core claim of this work is that incorporating temporal dynamics dramatically enhances graph embedding capabilities. ---
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Biological regulatory networks (BRNs) govern cellular processes with inherent stochasticity, reflecting the probabilistic nature of gene expression and protein interactions. Traditional Graph Neural Networks (GNNs) often treat these networks as deterministic, leading to inaccurate representations and predictions. This work proposes a novel framework for modeling BRNs that explicitly incorporates stochasticity. We introduce probabilistic layers within a GNN architecture to represent the uncertainty in network dynamics, drawing inspiration from stochastic differential equations (SDEs). These layers allow the GNN to learn and propagate probabilistic information, capturing the random fluctuations observed in biological systems. Our approach provides a more realistic and robust model of BRNs, offering potential improvements in predicting network behavior and identifying key regulatory nodes. The core claim is to develop GNN architectures that can explicitly model the stochastic nature of biological regulatory networks, accounting for the inherent randomness in gene expression and protein interactions. The core mechanism involves introducing probabilistic layers within the GNN to represent the uncertainty in the network's dynamics, incorporating elements of stochastic differential equations. This addresses a critical gap in current GNN applications to biology, acknowledging the inherent noise in biological systems.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper investigates the integration of topology optimization algorithms with graph neural networks (GNNs) to develop a novel framework for efficient and robust optimization of complex topological structures. Traditional topology optimization methods often struggle with intricate designs, necessitating manual configuration. We propose a system that leverages GNNs to dynamically represent and analyze the topology of the problem, accelerating the optimization process. The core mechanism involves constructing a multi-layered graph representing the topological structure, enabling the network to effectively capture and exploit relationships between nodes and edges. The integration of these two powerful tools promises to significantly improve the performance of topology optimization across a range of applications. This work explores the benefits of this combined approach, demonstrating its effectiveness through simulations and preliminary results.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper explores a novel approach to program generation and explanation by integrating the strengths of neural networks and neuro-symbolic reasoning. Current program generation techniques often fall short due to a lack of explicit logical reasoning and interpretability. We propose a framework that combines a neural network encoder to translate problem representations into neural network states and a neuro-symbolic decoder to generate program code based on these states, leveraging knowledge graphs and logical rules. Furthermore, the neuro-symbolic decoder facilitates code explanation, providing a traceable execution path. The core claim is that this hybrid approach surpasses the limitations of single-technique methods, offering a more intelligent and explainable solution for automated program generation and understanding. The methodology outlines a system architecture, detailing the components and their interactions, and highlights the key innovations within the system.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel deep learning model designed for analyzing complex systems by directly processing topological data. The core concept revolves around representing complex data as graphs and leveraging Graph Neural Networks (GNNs) for deep learning. Traditional deep learning methods often struggle with non-Euclidean data, presenting a significant limitation when dealing with systems where relationships are more important than coordinates. This research aims to bridge this gap by providing a framework capable of learning from topological features, ultimately leading to improved insights and predictive capabilities. The model utilizes persistent homology to extract topological features and then feeds them into a GNN for further processing. We demonstrate the potential of this approach through theoretical analysis and discuss its implications for various applications. This work contributes to the growing field of topological data analysis (TDA) by integrating it with the power of deep learning.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel approach to graph embedding that leverages graph neural networks to create a 'meta-graph' of data across multiple scales. The goal is to facilitate the identification of long-range dependencies and non-local relationships within complex datasets. Current methods typically focus on local analysis, restricting the scope of investigation to individual nodes or small clusters. This work proposes a technique that allows for a more comprehensive understanding of the data's structure by representing the data as a network of interconnected nodes, enabling the analysis of relationships that extend beyond immediate neighbors. We demonstrate the effectiveness of this method through a series of quantitative and qualitative analyses, showcasing its ability to uncover hidden patterns and dependencies across multiple scales. The core mechanism involves constructing a meta-graph, where nodes represent data points and edges represent relationships. This facilitates the modeling of complex network structures and the identification of long-range connections.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Existing graph embedding techniques primarily focus on static graph representations, often neglecting the crucial aspect of temporal dynamics inherent in many real-world graphs. This paper introduces a novel approach combining relational graph embeddings with Temporal Graph Neural Networks (TGNNs) to address this limitation. The core idea is to learn embeddings that adaptively reflect changes in the graph's relational structure over time. We propose a framework where a TGNN is trained to process graph data streams, capturing evolving relationships and generating dynamic embeddings. These embeddings are then utilized for downstream tasks requiring understanding of temporal graph patterns. The proposed method offers a significant advancement over static graph embeddings by incorporating temporal information, leading to improved performance in tasks such as node classification, link prediction, and graph representation learning within dynamic environments. The key contributions lie in the integration of TGNNs with relational graph embeddings and the development of a learning strategy that allows for continuous adaptation to temporal changes.
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations