Cristani, C. and Tessera, D. (2026) A Foundational Protocol for Reproducible Visualization in Multivariate Quantum Data. Open Access Library Journal, 13, 1-13. doi: 10.4236/oalib.1114704 .

Jan 10 (Reuters) - Elon Musk said on Saturday that social media platform X will open to the public its new algorithm, including all code for organic and advertising post recommendations, in seven days ...

Submodular maximization is a significant area of interest in combinatorial optimization, with numerous real-world applications. A research team led by Xiaoming SUN from the State Key Lab of Processors ...

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 ...

Advanced data visualization and analytics have become central to enterprise IT strategies as organizations face rapid data growth from cloud services, software-as-a-service applications, edge devices, ...

Understanding the differences between probabilistic and deterministic AI will help manufacturers make more informed choices and achieve measurable results. As professionals become interested in using ...

Service intelligence startup Neuron7 Inc. said today it has come up with a solution to solve the reliability challenges that prevent enterprises from adopting artificial intelligence agents. That ...

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 ...

Forbes contributors publish independent expert analyses and insights. I track enterprise software application development & data management. Prototyping is natural. New smartphone concepts are tested ...

import torch @torch.compile(backend="inductor") def fn(src, index, base_tensor): src = src + 10 torch.use_deterministic_algorithms(True) base_tensor.scatter_(0, index ...