Abstract: Despite the wide variety of applications and use cases that can be solved with the help of machine learning algorithms, researchers have yet to develop a general artificial intelligence ...

Researchers from Chiba University have developed a lightweight peer-selection algorithm that significantly reduces data propagation delays without increasing resource usage on internet of things (IoT) ...

China’s high-speed rail network continues to expand at a rapid pace, and recently added routes have pushed its total length to over 50,000 kilometers, around 31,000 miles. According to a press release ...

While the creation of this new entity marks a big step toward avoiding a U.S. ban, as well as easing trade and tech-related tensions between Washington and Beijing, there is still uncertainty ...

Instagram is introducing a new tool that lets you see and control your algorithm, starting with Reels, the company announced on Wednesday. The new tool, called “Your Algorithm,” lets you view the ...

Abstract: In order to improve the prediction accuracy of wind farms and solve the problem that nonlinear autoregressive neural network (NARX) is difficult to find the optimal hidden layer structure ...

When Edsger W. Dijkstra published his algorithm in 1959, computer networks were barely a thing. The algorithm in question found the shortest path between any two nodes on a graph, with a variant ...

More non-Tesla drivers have started using Superchargers as their default network as it opens up to all EVs. This migration towards Superchargers has reduced utilization at some non-Tesla networks. The ...

The original version of this story appeared in Quanta Magazine. If you want to solve a tricky problem, it often helps to get organized. You might, for example, break the problem into pieces and tackle ...

When Fei-Fei Li arrived in Princeton in January 2007 as an assistant professor, she was assigned an office on the second floor of the computer science building. Her neighbor was Christiane Fellbaum.

Artificial neural networks are machine learning models that have been applied to various genomic problems, with the ability to learn non-linear relationships and model high-dimensional data. These ...