Abstract: This paper proposes an automatic framework for controlled data flow graph (CDFG) generation from verilog designs, where the generated CDFGs can be applied to visualization, formal ...

Graph Neural Networks (GNNs) are effective and popular techniques for representation learning of graph data, significantly relying on message passing mechanism. Most GNNs utilize graph convolution ...

Many successful machine learning models for molecular property prediction rely on Lewis structure representations, commonly encoded as SMILES strings. However, a key limitation arises with molecules ...

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School of Automation and Electrical Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China Key Laboratory of Synthetical Automation for Process Industries at ...

Paper: Graph Representation of 3D CAD Models for Machining Feature Recognition With Deep Learning The MFCAD (Machining Feature CAD) dataset is a comprehensive collection of 3D CAD models with labeled ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

LangGraph Multi-Agent Swarm is a Python library designed to orchestrate multiple AI agents as a cohesive “swarm.” It builds on LangGraph, a framework for constructing robust, stateful agent workflows, ...

Directed graphs and their afferent/efferent capacities are produced by Markov modeling of the universal cover of undirected graphs simultaneously with the calculation of volume entropy. Using these ...

Machine learning has expanded beyond traditional Euclidean spaces in recent years, exploring representations in more complex geometric structures. Non-Euclidean representation learning is a growing ...