Traffic loss isn’t the full story as answer engines create higher-value demand, forcing CMOs to rethink SEO fundamentals, ...

Abstract: The graph neural networks (GNNs) have drawn much attention for predicting the remaining useful life (RUL) due to their excellent performance in processing correlation relationship among data ...

EmeraldGraph, a domain-specific Knowledge Graph, its schema and the full pipeline for its construction EmeraldDB, a document store extracted from ESG reports for text-based retrieval Claim grounding, ...

Due to the intricate dynamic coupling between molecular networks and brain regions, early diagnosis and pathological mechanism analysis of Alzheimer's disease (AD) remain highly challenging. To ...

Could 2026 be the year of the beautiful back end? We explore the range of options for server-side JavaScript development, from Express to Next and all the rest. A grumpy Scrooge of a developer might ...

Model misspecification is a common challenge in population genetic inference, as oversimplified mathematical models often fail to capture complex evolutionary processes. Here, we introduce a framework ...

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In modern drug discovery, generative molecular design models have greatly expanded the chemical space available to researchers, enabling rapid exploration of new compounds. Yet, a major challenge ...

Abstract: Knowledge graph completion (KGC) tasks aim to infer missing facts in a knowledge graph (KG) for many knowledgeintensive applications. However, existing embedding-based KGC approaches ...

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