Dr. James McCaffrey presents a complete end-to-end demonstration of the kernel ridge regression technique to predict a single ...

Whole-mount 3D imaging at the cellular scale is a powerful tool for exploring complex processes during morphogenesis. In organoids, it allows examining tissue architecture, cell types, and morphology ...

The traditional subspace-based algorithms in the process of coherent direction of arrival (DOA) estimation get in trouble because of the rank loss of the signal covariance matrix. To this end, this ...

A security researcher has discovered a security flaw in the Linux kernel using the OpenAI o3 reasoning model. An official patch has been released.

Understand the process of convolution and its applications in blurring and sharpening images using two-dimensional filters.

As AI continues to reshape the way developers build applications, Microsoft's Semantic Kernel is emerging as a powerful tool for integrating AI-driven capabilities into existing codebases -- without ...

This use case is a vectorized conv2d kernel. It's lowered from MLIR without using any target-specific dialect (like AIEVec), and it presents a couple of interesting challenges. The first thing that ...

Figure 1. The schematic diagram of STM-ac4C. (A) Feature encoding. One-hot encoding converts the 201 nt RNA sequence into a 5 × 201 matrix. (B) Model architecture. The encoded feature matrix is ...

The convolution kernel is essentially a weight matrix, and the so-called “convolution” operation refers to the matrix multiplication operation. Figure 3 shows the convolution operation with a 3 × 3 ...

Image restoration is actually a deconvolution problem. In the restoration equation, the convolution kernel matrix is a large-scale Toeplitz matrix. In order to reduce the computational complexity of ...