AI tools are frequently used in data visualization — this article describes how they can make data preparation more efficient ...

Original Reporting This article contains firsthand information gathered by reporters. This includes directly interviewing sources and analyzing primary source documents. Subject Specialist The ...

We introduce a novel dataset of large depreciations worldwide since 1971. First, we use a multi-step approach to accurately pinpoint large depreciation events on monthly data. We then construct large ...

Q. I work with large spreadsheets. These spreadsheets have hundreds or even thousands of rows and often 10 or more columns. It’s so much to process that I become confused and make mistakes. Does Excel ...

Large language models (LLMs) are increasingly used for text-rich graph machine learning tasks such as node classification in high-impact domains like fraud detection and recommendation systems. Yet, ...

Neo4j, the graph database from the US-Swedish company of the same name, is used by 76% of the Fortune 100, and its Australian customers include organisations in the healthcare, policing and banking ...

# Two signals with a coherent part at 10Hz and a random part s1 = np.sin(2 * np.pi * 10 * t) + nse1 s2 = np.sin(2 * np.pi * 10 * t) + nse2 ...

Series 1 uses 'smallX' and 'smallY' as its dataset source, where the small dataset consists of only 3 points. Series 2 uses 'largeX' and 'largeY' as its dataset source, where the large dataset ...

On the CMeEE dataset, GPT-4.0 achieved an F1-score of 65.42 using few-shot learning, surpassing traditional models such as BERT-CRF (62.11) and Med-BERT (60.66). Building upon this, we compiled a ...

Abstract: Recently, there has been increasing interest in developing and deploying deep graph learning algorithms for various tasks, such as fraud detection and recommender systems. However, there is ...