We previously introduced a “range corrected” Δ−machine learning potential (ΔMLP) that used deep neural networks to improve the accuracy of combined quantum mechanical/molecular mechanical (QM/MM) ...

In this way, DPC can grasp the co-occurrent and long-range relationship for both domains. To further narrow down the domain gap, we design a Domain-common Knowledge Incorporator (DKI) to guide the ...

ABSTRACT This work aims to study the equitable total coloring into subfamilies of regular graphs. For this purpose, we use some relationships between equitable total coloring and range (vertex) ...

Aggregate to Adapt: Node-Centric Aggregation for Multi-Source-Free Graph Domain Adaptation (WWW-2025). This is a PyTorch implementation of the GraphATA algorithm, which tries to address the ...

This repository contains the official code and datasets for the paper, "Continuous Temporal Domain Generalization", accepted by NeurIPS 2024. TL;DR: The study proposes a method capable of generating ...

The iRangeGraph technique involves a dynamic construction of graph-based indexes during query processing. Instead of building and storing an index for every possible range, the method constructs these ...

Dynamic graphs arise in various real-world applications, and it is often welcomed to model the dynamics in continuous time domain for its flexibility. This paper aims to design an easy-to-use pipeline ...

Background and Purpose— Assessment of autoregulation in the time domain is a promising monitoring method for actively optimizating cerebral perfusion pressure (CPP) in critically ill patients. The ...