Fine-grained spatial data are critical for informed decision-making in domains ranging from economic planning to ...
Researchers present a comprehensive review of frontier AI applications in computational structural analysis from 2020 to 2025, focusing on graph neural networks (GNNs), sequence-to-sequence (Seq2Seq) ...
Abstract: Recommender systems have undergone more than three decades of continuous development, from early collaborative filtering techniques and matrix factorization to the integration of deep ...
Dynamic Graph Neural Networks (Dynamic GNNs) have emerged as powerful tools for modeling real-world networks with evolving topologies and node attributes over time. A survey by Professors Zhewei Wei, ...
ABSTRACT: The increasing complexity of financial statements, which encompass both structured numerical data and unstructured textual narratives, presents significant challenges for traditional ...
Past psychology and behavioral science studies have identified various ways in which people's acquisition of new knowledge can be disrupted. One of these, known as interference, occurs when humans are ...
Accurately identifying small molecule binding sites on proteins is fundamental to understanding protein function and enabling structure-based drug discovery, yet this critical step remains a major ...
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