Recent survey delivers the first systematic benchmark of TSP solvers spanning end-to-end deep learners, hybrid methods and brand-new LLM-based ...

By leveraging inference-time scaling and a novel "reflection" mechanism, ALE-Agent solves the context-drift problems that ...

There are some classic pairings where each half makes the other better: peanut butter and jelly, bread and butter, cookies ...

Where, exactly, could quantum hardware reduce end-to-end training cost rather than merely improve asymptotic complexity on a ...

Social media algorithms change constantly, making it hard to maintain consistent reach and engagement. This article breaks ...

The wide-speed-range vehicles have attracted significant attention due to the exceptional performance in autonomous aerospace ...

QuoteIQ, a CRM platform built for mobile and field service businesses, has announced the launch of Route Density, a new ...

Car oil life monitors use manufacturer algorithms for warranty survival, not engine health-real oil analysis reveals ...

Understand and implement the RMSProp optimization algorithm in Python. Essential for training deep neural networks ...

These open-source MMM tools solve different measurement problems, from budget optimization to forecasting and preprocessing.

Abstract: Distributed optimization provides a framework for deriving distributed algorithms for a variety of multi-robot problems. This tutorial constitutes the first part of a two-part series on ...

Renewable energy (RE) offers a promising solution to address the electricity shortages in isolated and rural areas. Microgrid (MG) can incorporate various energy sources, including renewable ...