Researchers report that the integration of machine learning and Internet of Things (IoT) technologies is enabling a new generation of intelligent industrial environments capable of real-time ...

When a worker thread completes a task, it doesn't return a sprawling transcript of every failed attempt; it returns a compressed summary of the successful tool calls and conclusions.

Practical Application: The authors propose QFI-Informed Mutation (QIm), a heuristic that adapts mutation probabilities using diagonal QFI entries. QIm outperforms uniform and random-restart baselines, ...

Graph cover problems form a critical area within discrete optimisation and theoretical computer science, addressing the challenge of selecting subsets of vertices (or edges) that satisfy predetermined ...

Abstract: Sampling random walks is a crucial component of many graph algorithms that perform graph embedding, link prediction, and other tasks. The effectiveness of these stochastic algorithms coupled ...

Abstract: Random walk centrality is a fundamental metric in graph mining for quantifying node importance and influence, defined as the weighted average of hitting times to a node from all other nodes.

In this video, we explore why Spotify's shuffle feature isn't truly random and operates based on an algorithm. We discuss the reasons behind our preferences for non-random shuffle, the results of an ...

This contribution is part of the special series of Inaugural Articles by members of the National Academy of Sciences elected in 2024. Many real-world networks change dynamically but can be notoriously ...

ABSTRACT: Missing data remains a persistent and pervasive challenge across a wide range of domains, significantly impacting data analysis pipelines, predictive modeling outcomes, and the reliability ...