Visible light positioning (VLP) is a promising indoor localization technology with strong interference immunity and easy integration with existing infrastructure. However, most existing VLP systems rely on multi-anchor deployments and Received Signal Strength (RSS), which are costly to deploy and remain sensitive to re...
Xuan Wang, Xiao-Xiang Cao, Di-Zhou Guo et al.· IEEE Transactions on Mobile...· 0 citations
This paper presents a novel approach to digital circuit synthesis leveraging Generative Graph Neural Networks (GNNs). The core claim is that designing complex digital circuits is a significantly time-consuming and iterative process. To address this, we propose a system that utilizes a GNN to automatically generate circ...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper presents a novel approach to complex system modeling and simulation utilizing graph neural networks (GNNs). The core focus is to develop a method capable of automatically learning complex relationships within a system, and generating high-fidelity predictions. Traditional methods often struggle to effectivel...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel self-adaptive graph neural network (GNN) architecture based on dynamic graph representations. Traditional GNNs often struggle with the inherent limitations of static graphs, hindering their ability to effectively handle complex and dynamic data. Our proposed method leverages a dynamic grap...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
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Existing self-supervised learning methods for graph embeddings often rely on heuristic approaches without a robust theoretical underpinning. This work introduces a novel framework for learning graph embeddings based on contrastive predictive coding (CPC). The core idea is to train a neural network to predict future gra...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel method for complex system modeling based on graph neural networks (GNNs) and adaptive learning. Traditional approaches often require manual design of complex network structures, limiting the model's flexibility. Our method leverages GNNs to automatically learn and update graph structures,...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper addresses the limitations of current deep learning models in performing abstract symbolic reasoning. The core argument is that deep learning's reliance on pattern recognition hinders its ability to grasp underlying concepts and relationships, leading to a lack of genuine understanding. We propose a novel hie...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel neuro-symbolic reasoning framework centered around the dynamic construction of a knowledge graph. The core challenge in integrating neural networks and symbolic reasoning lies in the inherent differences in their representations and the difficulty in seamless translation between them. Our...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper proposes a novel graph neural network (GNN) optimization algorithm based on a dynamically adjusted attention mechanism. Traditional GNN optimization methods often rely on fixed architectures, limiting their adaptability to data variations. Our proposed algorithm leverages a self-adaptive strategy to dynamica...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
The goal of this paper is to develop a novel approach to automated generation using self-adaptive graph neural networks (SA-GNNs). Traditional graph neural networks (GNNs) often struggle with adapting to dynamic data, limiting their effectiveness in complex generative tasks. This work introduces a new model, termed SA-...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
This paper introduces a novel architecture for Graph Neural Networks (GNNs) designed to effectively handle multi-scale spatio-temporal data. The core concept revolves around decomposing time-series data into multiple scales, each represented by a corresponding graph structure. A GNN is then employed to learn dependenci...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Graph embeddings are a fundamental tool for representing graph data, offering insights into network structure and relationships. Traditional methods often employ fixed weights, limiting the flexibility and scalability of the representation. This paper introduces a novel non-local graph embedding method that dynamically...
Jincheng Zhang· Zenodo (CERN European Organi...· 0 citations
Machine-learning networks can learn without detailed instructions from a designer. Those that do may offer insight into how the brain reorganizes itself during learning.
MIT News · Artificial Intelligence· news.mit.eduJul 15, 2026
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI's neural network before their chatbot ever says a word.
Microsoft Research Blog· microsoft.comJul 13, 2026
Cryptographic code supports vital protections in modern computing systems. Learn how a new method helps verify code as developers write it while preserving speed and adaptability as it gets implemented and evolves. The post Verifying Rust cryptography in SymCrypt, from standards to code appeared first on Microsoft Research.
MIT News · Artificial Intelligence· news.mit.eduJul 6, 2026
PhD student Rachel Sava, winner of the Envisioning the Future of Computing Prize, explores transformative improvements and dystopian risks of neural technology.
