China's LineShine supercomputer debuted at number one on the 67th TOP500 list on June 23, 2026, posting 2.198 exaflops on the High Performance Linpack benchmark — the first machine in the ranking's ...

Yes, that simple question is, in the modern Nvidia world that has come to dominate AI training and to a certain extent HPC simulation and modeling, heretical. But given that CPUs are in many cases ...

Abstract: Hyperspectral imagery (HSI) denoising is a challenging problem because of the difficulty in preserving both spectral and spatial structures simultaneously. In recent years, sparse coding, ...

OpenAI partnered with Broadcom in October 2025 to design a custom inference chip aimed at reducing the growing expense of ...

Researchers at OpenAI trained a single language model on 175 billion learned numerical weights, each one adjusted during training to predict the next word in a sequence. That model, GPT-3, ...

Matrix structures don’t work on their own. The work is less about control and more about integration, often without formal ...

DeepSeek V4 architecture uses sparse attention to cut inference costs 73% at one-million-token contexts, but a NIST ...

Naveen Rao's Unconventional AI raised $475M at a $4.5B valuation to build biology-inspired, energy-efficient chips for ...

Objective We aimed to investigate the association between occupational standing, walking and forward bending during pregnancy ...

Abstract: In many applications in wireless communications, we need to recover a structured sparse signal from a linear measurement model with uncertain sensing matrix. There are two challenges of ...

Support vector regression can predict numeric values effectively, and this article shows how to implement and train a kernel SVR model in C# using stochastic sub-gradient descent.